This paper addresses the scheduling and inventory management of a straight pipeline system connecting a single refinery to multiple distribution centers.By increasing the number of batches and time periods,maintaining...This paper addresses the scheduling and inventory management of a straight pipeline system connecting a single refinery to multiple distribution centers.By increasing the number of batches and time periods,maintaining the model resolution by using linear programming-based methods and commercial solvers would be very time-consuming.In this paper,we make an attempt to utilize the problem structure and develop a decomposition-based algorithm capable of finding near-optimal solutions for large instances in a reasonable time.The algorithm starts with a relaxed version of the model and adds a family of cuts on the fly,so that a near-optimal solution is obtained within a few iterations.The idea behind the cut generation is based on the knowledge of the underlying problem structure.Computational experiments on a real-world data case and some randomly generated instances confirm the efficiency of the proposed algorithm in terms of the solution quality and time.展开更多
Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review exp...Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review explores the historical development of MOEAs,beginning with foundational concepts in multi-objective optimization,basic types of MOEAs,and the evolution of Pareto-based selection and niching methods.Further advancements,including decom-position-based approaches and hybrid algorithms,are discussed.Applications are analyzed in established domains such as engineering and economics,as well as in emerging fields like advanced analytics and machine learning.The significance of MOEAs in addressing real-world problems is emphasized,highlighting their role in facilitating informed decision-making.Finally,the development trajectory of MOEAs is compared with evolutionary processes,offering insights into their progress and future potential.展开更多
Wind farm layout optimization is a critical challenge in renewable energy development,especially in regions with complex terrain.Micro-siting of wind turbines has a significant impact on the overall efficiency and eco...Wind farm layout optimization is a critical challenge in renewable energy development,especially in regions with complex terrain.Micro-siting of wind turbines has a significant impact on the overall efficiency and economic viability of wind farm,where the wake effect,wind speed,types of wind turbines,etc.,have an impact on the output power of the wind farm.To solve the optimization problem of wind farm layout under complex terrain conditions,this paper proposes wind turbine layout optimization using different types of wind turbines,the aim is to reduce the influence of the wake effect and maximize economic benefits.The linear wake model is used for wake flow calculation over complex terrain.Minimizing the unit energy cost is taken as the objective function,considering that the objective function is affected by cost and output power,which influence each other.The cost function includes construction cost,installation cost,maintenance cost,etc.Therefore,a bi-level constrained optimization model is established,in which the upper-level objective function is to minimize the unit energy cost,and the lower-level objective function is to maximize the output power.Then,a hybrid evolutionary algorithm is designed according to the characteristics of the decision variables.The improved genetic algorithm and differential evolution are used to optimize the upper-level and lower-level objective functions,respectively,these evolutionary operations search for the optimal solution as much as possible.Finally,taking the roughness of different terrain,wind farms of different scales and different types of wind turbines as research scenarios,the optimal deployment is solved by using the algorithm in this paper,and four algorithms are compared to verify the effectiveness of the proposed algorithm.展开更多
In recent years,the development of new types of nuclear reactors,such as transportable,marine,and space reactors,has presented new challenges for the optimization of reactor radiation-shielding design.Shielding struct...In recent years,the development of new types of nuclear reactors,such as transportable,marine,and space reactors,has presented new challenges for the optimization of reactor radiation-shielding design.Shielding structures typically need to be lightweight,miniaturized,and radiation-protected,which is a multi-parameter and multi-objective optimization problem.The conventional multi-objective(two or three objectives)optimization method for radiation-shielding design exhibits limitations for a number of optimization objectives and variable parameters,as well as a deficiency in achieving a global optimal solution,thereby failing to meet the requirements of shielding optimization for newly developed reactors.In this study,genetic and artificial bee-colony algorithms are combined with a reference-point-selection strategy and applied to the many-objective(having four or more objectives)optimal design of reactor radiation shielding.To validate the reliability of the methods,an optimization simulation is conducted on three-dimensional shielding structures and another complicated shielding-optimization problem.The numerical results demonstrate that the proposed algorithms outperform conventional shielding-design methods in terms of optimization performance,and they exhibit their reliability in practical engineering problems.The many-objective optimization algorithms developed in this study are proven to efficiently and consistently search for Pareto-front shielding schemes.Therefore,the algorithms proposed in this study offer novel insights into improving the shielding-design performance and shielding quality of new reactor types.展开更多
Multi-firmware comparison techniques can improve efficiency when auditing firmwares in bulk.How-ever,the problem of matching functions between multiple firmwares has not been studied before.This paper proposes a multi...Multi-firmware comparison techniques can improve efficiency when auditing firmwares in bulk.How-ever,the problem of matching functions between multiple firmwares has not been studied before.This paper proposes a multi-firmware comparison method based on evolutionary algorithms and trusted base points.We first model the multi-firmware comparison as a multi-sequence matching problem.Then,we propose an adaptation function and a population generation method based on trusted base points.Finally,we apply an evolutionary algorithm to find the optimal result.At the same time,we design the similarity of matching results as an evaluation metric to measure the effect of multi-firmware comparison.The experiments show that the proposed method outperforms Bindiff and the string-based method.Precisely,the similarity between the matching results of the proposed method and Bindiff matching results is 61%,and the similarity between the matching results of the proposed method and the string-based method is 62.8%.By sampling and manual verification,the accuracy of the matching results of the proposed method can be about 66.4%.展开更多
In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classificati...In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classification methods that utilize evolutionary algorithms(EAs)for gene expression profiles in cancer or medical applications based on research motivations,challenges,and recommendations.Relevant studies were retrieved from four major academic databases-IEEE,Scopus,Springer,and ScienceDirect-using the keywords‘cancer classification’,‘optimization’,‘FS’,and‘gene expression profile’.A total of 67 papers were finally selected with key advancements identified as follows:(1)The majority of papers(44.8%)focused on developing algorithms and models for FS and classification.(2)The second category encompassed studies on biomarker identification by EAs,including 20 papers(30%).(3)The third category comprised works that applied FS to cancer data for decision support system purposes,addressing high-dimensional data and the formulation of chromosome length.These studies accounted for 12%of the total number of studies.(4)The remaining three papers(4.5%)were reviews and surveys focusing on models and developments in prediction and classification optimization for cancer classification under current technical conditions.This review highlights the importance of optimizing FS in EAs to manage high-dimensional data effectively.Despite recent advancements,significant limitations remain:the dynamic formulation of chromosome length remains an underexplored area.Thus,further research is needed on dynamic-length chromosome techniques for more sophisticated biomarker gene selection techniques.The findings suggest that further advancements in dynamic chromosome length formulations and adaptive algorithms could enhance cancer classification accuracy and efficiency.展开更多
In order to establish the lake eutrophic evaluation model for multiple indices,based on the gauge transformation,an index formula in the form of a logarithmic power function was proposed to design an eutrophic evaluat...In order to establish the lake eutrophic evaluation model for multiple indices,based on the gauge transformation,an index formula in the form of a logarithmic power function was proposed to design an eutrophic evaluation model for the ＂ normalized values＂ of multi-indexes.The parameters in the formula were also optimized by bee immune evolutionary algorithm（BEIEA）.The universal index formula was suitable to multiindices items for eutrophic evaluation.At the same time,the formula was applied to practical eutrophic evaluations in 10 regions of Dong Lake.The evaluation results were coincident with those obtained from the power function of weighted sums and also with actual conditions.It was shown that the bee immune evolutionary algorithm was suitable to the parameter optimization in the eutrophic evaluation model.展开更多
In order to optimize the signal control system, this paper proposes a method to design an optimized fuzzy logic controller （FLC） with the DNA evolutionary algorithm. Inspired by the DNA molecular operation character...In order to optimize the signal control system, this paper proposes a method to design an optimized fuzzy logic controller （FLC） with the DNA evolutionary algorithm. Inspired by the DNA molecular operation characteristics, the DNA evolutionary algorithm modifies the corresponding genetic operators. Compared with the traditional genetic algorithm （GA）, the DNA evolutionary algorithm can overcome weak local search capability and premature convergence. The parameters of membership functions are optimized by adopting the quaternary encoding method and performing corresponding DNA genetic operators. The relevant optimized parameters are combined with the FLC for single intersection traffic signal control. Simulation experiments shows the better performance of the FLC with the DNA evolutionary algorithm optimization. The experimental results demonstrate the efficiency of the nrotmsed method.展开更多
A new algorithm based on genetic algorithm(GA) is developed for solving function optimization problems with inequality constraints. This algorithm has been used to a series of standard test problems and exhibited good...A new algorithm based on genetic algorithm(GA) is developed for solving function optimization problems with inequality constraints. This algorithm has been used to a series of standard test problems and exhibited good performance. The computation results show that its generality, precision, robustness, simplicity and performance are all satisfactory.展开更多
Flexible job shop scheduling problems(FJSP)have received much attention from academia and industry for many years.Due to their exponential complexity,swarm intelligence(SI)and evolutionary algorithms(EA)are developed,...Flexible job shop scheduling problems(FJSP)have received much attention from academia and industry for many years.Due to their exponential complexity,swarm intelligence(SI)and evolutionary algorithms(EA)are developed,employed and improved for solving them.More than 60%of the publications are related to SI and EA.This paper intents to give a comprehensive literature review of SI and EA for solving FJSP.First,the mathematical model of FJSP is presented and the constraints in applications are summarized.Then,the encoding and decoding strategies for connecting the problem and algorithms are reviewed.The strategies for initializing algorithms?population and local search operators for improving convergence performance are summarized.Next,one classical hybrid genetic algorithm(GA)and one newest imperialist competitive algorithm(ICA)with variables neighborhood search(VNS)for solving FJSP are presented.Finally,we summarize,discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions.展开更多
Computational time complexity analyzes of evolutionary algorithms （EAs） have been performed since the mid-nineties. The first results were related to very simple algorithms, such as the （1＋1）-EA, on toy problems....Computational time complexity analyzes of evolutionary algorithms （EAs） have been performed since the mid-nineties. The first results were related to very simple algorithms, such as the （1＋1）-EA, on toy problems. These efforts produced a deeper understanding of how EAs perform on different kinds of fitness landscapes and general mathematical tools that may be extended to the analysis of more complicated EAs on more realistic problems. In fact, in recent years, it has been possible to analyze the （1＋1）-EA on combinatorial optimization problems with practical applications and more realistic population-based EAs on structured toy problems. This paper presents a survey of the results obtained in the last decade along these two research lines. The most common mathematical techniques are introduced, the basic ideas behind them are discussed and their elective applications are highlighted. Solved problems that were still open are enumerated as are those still awaiting for a solution. New questions and problems arisen in the meantime are also considered.展开更多
This paper presents a parallel two-level evolutionary algorithm based on domain decomposition for solving function optimization problem containing multiple solutions. By combining the characteristics of the global sea...This paper presents a parallel two-level evolutionary algorithm based on domain decomposition for solving function optimization problem containing multiple solutions. By combining the characteristics of the global search and local search in each sub-domain, the former enables individual to draw closer to each optima and keeps the diversity of individuals, while the latter selects local optimal solutions known as latent solutions in sub-domain. In the end, by selecting the global optimal solutions from latent solutions in each sub-domain, we can discover all the optimal solutions easily and quickly.展开更多
Evolutionary algorithms have been shown to be very successful in solving multi-objective optimization problems(MOPs).However,their performance often deteriorates when solving MOPs with irregular Pareto fronts.To remed...Evolutionary algorithms have been shown to be very successful in solving multi-objective optimization problems(MOPs).However,their performance often deteriorates when solving MOPs with irregular Pareto fronts.To remedy this issue,a large body of research has been performed in recent years and many new algorithms have been proposed.This paper provides a comprehensive survey of the research on MOPs with irregular Pareto fronts.We start with a brief introduction to the basic concepts,followed by a summary of the benchmark test problems with irregular problems,an analysis of the causes of the irregularity,and real-world optimization problems with irregular Pareto fronts.Then,a taxonomy of the existing methodologies for handling irregular problems is given and representative algorithms are reviewed with a discussion of their strengths and weaknesses.Finally,open challenges are pointed out and a few promising future directions are suggested.展开更多
Application of the multiobjective evolutionary algorithms to the aerodynamicoptimization design of a centrifugal impeller is presented. The aerodynamic performance of acentrifugal impeller is evaluated by using the th...Application of the multiobjective evolutionary algorithms to the aerodynamicoptimization design of a centrifugal impeller is presented. The aerodynamic performance of acentrifugal impeller is evaluated by using the three-dimensional Navier-Stokes solutions. Thetypical centrifugal impeller is redesigned for maximization of the pressure rise and blade load andminimization of the rotational total pressure loss at the given flow conditions. The Bezier curvesare used to parameterize the three-dimensional impeller blade shape. The present method obtains manyreasonable Pareto optimal designs that outperform the original centrifugal impeller. Detailedobservation of the certain Pareto optimal design demonstrates the feasibility of the presentmultiobjective optimization method tool for turbomachinery design.展开更多
Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitnes...Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitness assignment strategy of non-dominated sorting genetic algorithm (NSGA). The fitness assignment strategy is improved and a new self-adjustment scheme of is proposed. This algorithm is proved to be very efficient both computationally and in terms of the quality of the Pareto fronts produced with five test problems including GA difficult problem and GA deceptive one. Finally, SNSGA is introduced to solve multi-objective mixed integer linear programming (MILP) and mixed integer non-linear programming (MINLP) problems in process synthesis.展开更多
The optimal allocation model of regional water resources is built with the purpose of maximizing the comprehensive economic,social and environmental benefits of regional water consumption.In order to solve the problem...The optimal allocation model of regional water resources is built with the purpose of maximizing the comprehensive economic,social and environmental benefits of regional water consumption.In order to solve the problems that easily appear during the model solution of regional water resource optimal allocation with multiple water sources,multiple users and multiple objectives like"curse of dimensionality"or sinking into local optimum,this paper proposes a particle swarm optimization(PSO)algorithm based on immune evolutionary algorithm(IEA).This algorithm introduces immunology principle into particle swarm algorithm.Its immune memorizing and self-adjusting mechanism is utilized to keep the particles in the fitness level at a certain concentration and guarantee the diversity of population.Also,the global search characteristics of IEA and the local search capacity of particle swarm algorithm have been fully utilized to overcome the dependence of PSO on initial swarm and the deficiency of vulnerability to local optimum.After applying this model to the allocation of water resources in Zhoukou,we obtain the scheme for optimization allocation of water resources in the planning level years,i.e.2015and 2025 under the guarantee rate of 50%.The calculation results indicate that the application of this algorithm to solve the issue of optimal allocation of regional water resources is reliable and reasonable.Thus it ofers a new idea for solving the issue of optimal allocation of water resources.展开更多
In this paper we discuss the paradigm of evolutionary algorithms (EAs). We argue about the need for new heuristics in real-world problem solving, discussing reasons why some problems are difficult to solve. After intr...In this paper we discuss the paradigm of evolutionary algorithms (EAs). We argue about the need for new heuristics in real-world problem solving, discussing reasons why some problems are difficult to solve. After introducing the main concepts of evolutionary algorithms, we concentrate on two issues: (1) self-adaptation of the parameters of EA, and (2) handling constraints.展开更多
In this paper, a new algorithm for solving multi-modal function optimization problems-two-level subspace evolutionary algorithm is proposed. In the first level, the improved GT algorithm is used to do global recombina...In this paper, a new algorithm for solving multi-modal function optimization problems-two-level subspace evolutionary algorithm is proposed. In the first level, the improved GT algorithm is used to do global recombination search so that the whole population can be separated into several niches according to the position of solutions; then, in the second level, the niche evolutionary strategy is used for local search in the subspaces gotten in the first level till solutions of the problem are found. The new algorithm has been tested on some hard problems and some good results are obtained.展开更多
The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the Kmeans algorithm depends highly on initial cluster centers and converges to local minima. This paper prop...The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the Kmeans algorithm depends highly on initial cluster centers and converges to local minima. This paper proposes a hybrid evolutionary programming based clustering algorithm, called PSO-SA, by combining particle swarm optimization (PSO) and simulated annealing (SA). The basic idea is to search around the global solution by SA and to increase the information exchange among particles using a mutation operator to escape local optima. Three datasets, Iris, Wisconsin Breast Cancer, and Ripley's Glass, have been considered to show the effectiveness of the proposed clustering algorithm in providing optimal clusters. The simulation results show that the PSO-SA clustering algorithm not only has a better response but also converges more quickly than the K-means, PSO, and SA algorithms.展开更多
The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency...The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency. A multi-objective model was presented for the material distribution routing problem in mixed manufacturing systems, and it was solved by a hybrid multi-objective evolutionary algorithm (HMOEA). The characteristics of the HMOEA are as follows: 1) A route pool is employed to preserve the best routes for the population initiation; 2) A specialized best?worst route crossover (BWRC) mode is designed to perform the crossover operators for selecting the best route from Chromosomes 1 to exchange with the worst one in Chromosomes 2, so that the better genes are inherited to the offspring; 3) A route swap mode is used to perform the mutation for improving the convergence speed and preserving the better gene; 4) Local heuristics search methods are applied in this algorithm. Computational study of a practical case shows that the proposed algorithm can decrease the total travel distance by 51.66%, enhance the average vehicle load rate by 37.85%, cut down 15 routes and reduce a deliver vehicle. The convergence speed of HMOEA is faster than that of famous NSGA-II.展开更多
文摘This paper addresses the scheduling and inventory management of a straight pipeline system connecting a single refinery to multiple distribution centers.By increasing the number of batches and time periods,maintaining the model resolution by using linear programming-based methods and commercial solvers would be very time-consuming.In this paper,we make an attempt to utilize the problem structure and develop a decomposition-based algorithm capable of finding near-optimal solutions for large instances in a reasonable time.The algorithm starts with a relaxed version of the model and adds a family of cuts on the fly,so that a near-optimal solution is obtained within a few iterations.The idea behind the cut generation is based on the knowledge of the underlying problem structure.Computational experiments on a real-world data case and some randomly generated instances confirm the efficiency of the proposed algorithm in terms of the solution quality and time.
文摘Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review explores the historical development of MOEAs,beginning with foundational concepts in multi-objective optimization,basic types of MOEAs,and the evolution of Pareto-based selection and niching methods.Further advancements,including decom-position-based approaches and hybrid algorithms,are discussed.Applications are analyzed in established domains such as engineering and economics,as well as in emerging fields like advanced analytics and machine learning.The significance of MOEAs in addressing real-world problems is emphasized,highlighting their role in facilitating informed decision-making.Finally,the development trajectory of MOEAs is compared with evolutionary processes,offering insights into their progress and future potential.
基金supported by the National Natural Science Foundation of China[Grant No.12461035]Qinghai University Students Innovative Training Program Project[2024-QX-57].
文摘Wind farm layout optimization is a critical challenge in renewable energy development,especially in regions with complex terrain.Micro-siting of wind turbines has a significant impact on the overall efficiency and economic viability of wind farm,where the wake effect,wind speed,types of wind turbines,etc.,have an impact on the output power of the wind farm.To solve the optimization problem of wind farm layout under complex terrain conditions,this paper proposes wind turbine layout optimization using different types of wind turbines,the aim is to reduce the influence of the wake effect and maximize economic benefits.The linear wake model is used for wake flow calculation over complex terrain.Minimizing the unit energy cost is taken as the objective function,considering that the objective function is affected by cost and output power,which influence each other.The cost function includes construction cost,installation cost,maintenance cost,etc.Therefore,a bi-level constrained optimization model is established,in which the upper-level objective function is to minimize the unit energy cost,and the lower-level objective function is to maximize the output power.Then,a hybrid evolutionary algorithm is designed according to the characteristics of the decision variables.The improved genetic algorithm and differential evolution are used to optimize the upper-level and lower-level objective functions,respectively,these evolutionary operations search for the optimal solution as much as possible.Finally,taking the roughness of different terrain,wind farms of different scales and different types of wind turbines as research scenarios,the optimal deployment is solved by using the algorithm in this paper,and four algorithms are compared to verify the effectiveness of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(Nos.12475174 and 12175101)Yue Lu Shan Center Industrial Innovation(No.2024YCII0108)。
文摘In recent years,the development of new types of nuclear reactors,such as transportable,marine,and space reactors,has presented new challenges for the optimization of reactor radiation-shielding design.Shielding structures typically need to be lightweight,miniaturized,and radiation-protected,which is a multi-parameter and multi-objective optimization problem.The conventional multi-objective(two or three objectives)optimization method for radiation-shielding design exhibits limitations for a number of optimization objectives and variable parameters,as well as a deficiency in achieving a global optimal solution,thereby failing to meet the requirements of shielding optimization for newly developed reactors.In this study,genetic and artificial bee-colony algorithms are combined with a reference-point-selection strategy and applied to the many-objective(having four or more objectives)optimal design of reactor radiation shielding.To validate the reliability of the methods,an optimization simulation is conducted on three-dimensional shielding structures and another complicated shielding-optimization problem.The numerical results demonstrate that the proposed algorithms outperform conventional shielding-design methods in terms of optimization performance,and they exhibit their reliability in practical engineering problems.The many-objective optimization algorithms developed in this study are proven to efficiently and consistently search for Pareto-front shielding schemes.Therefore,the algorithms proposed in this study offer novel insights into improving the shielding-design performance and shielding quality of new reactor types.
文摘Multi-firmware comparison techniques can improve efficiency when auditing firmwares in bulk.How-ever,the problem of matching functions between multiple firmwares has not been studied before.This paper proposes a multi-firmware comparison method based on evolutionary algorithms and trusted base points.We first model the multi-firmware comparison as a multi-sequence matching problem.Then,we propose an adaptation function and a population generation method based on trusted base points.Finally,we apply an evolutionary algorithm to find the optimal result.At the same time,we design the similarity of matching results as an evaluation metric to measure the effect of multi-firmware comparison.The experiments show that the proposed method outperforms Bindiff and the string-based method.Precisely,the similarity between the matching results of the proposed method and Bindiff matching results is 61%,and the similarity between the matching results of the proposed method and the string-based method is 62.8%.By sampling and manual verification,the accuracy of the matching results of the proposed method can be about 66.4%.
基金funded by the Ministry of Higher Education of Malaysia,grant number FRGS/1/2022/ICT02/UPSI/02/1.
文摘In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classification methods that utilize evolutionary algorithms(EAs)for gene expression profiles in cancer or medical applications based on research motivations,challenges,and recommendations.Relevant studies were retrieved from four major academic databases-IEEE,Scopus,Springer,and ScienceDirect-using the keywords‘cancer classification’,‘optimization’,‘FS’,and‘gene expression profile’.A total of 67 papers were finally selected with key advancements identified as follows:(1)The majority of papers(44.8%)focused on developing algorithms and models for FS and classification.(2)The second category encompassed studies on biomarker identification by EAs,including 20 papers(30%).(3)The third category comprised works that applied FS to cancer data for decision support system purposes,addressing high-dimensional data and the formulation of chromosome length.These studies accounted for 12%of the total number of studies.(4)The remaining three papers(4.5%)were reviews and surveys focusing on models and developments in prediction and classification optimization for cancer classification under current technical conditions.This review highlights the importance of optimizing FS in EAs to manage high-dimensional data effectively.Despite recent advancements,significant limitations remain:the dynamic formulation of chromosome length remains an underexplored area.Thus,further research is needed on dynamic-length chromosome techniques for more sophisticated biomarker gene selection techniques.The findings suggest that further advancements in dynamic chromosome length formulations and adaptive algorithms could enhance cancer classification accuracy and efficiency.
基金Supported by Science and Technology Basic Special Project(2009IM020100)National Natural Science Foundation of China(5077904250739002)~~
文摘In order to establish the lake eutrophic evaluation model for multiple indices,based on the gauge transformation,an index formula in the form of a logarithmic power function was proposed to design an eutrophic evaluation model for the ＂ normalized values＂ of multi-indexes.The parameters in the formula were also optimized by bee immune evolutionary algorithm（BEIEA）.The universal index formula was suitable to multiindices items for eutrophic evaluation.At the same time,the formula was applied to practical eutrophic evaluations in 10 regions of Dong Lake.The evaluation results were coincident with those obtained from the power function of weighted sums and also with actual conditions.It was shown that the bee immune evolutionary algorithm was suitable to the parameter optimization in the eutrophic evaluation model.
基金The National Natural Science Foundation of China(No.60972001)the Scientific Innovation Research of College Graduates in Jiangsu Province(No.CXZZ_0163)the Scientific Research Foundation of Graduate School of Southeast University(No.YBPY1212)
文摘In order to optimize the signal control system, this paper proposes a method to design an optimized fuzzy logic controller （FLC） with the DNA evolutionary algorithm. Inspired by the DNA molecular operation characteristics, the DNA evolutionary algorithm modifies the corresponding genetic operators. Compared with the traditional genetic algorithm （GA）, the DNA evolutionary algorithm can overcome weak local search capability and premature convergence. The parameters of membership functions are optimized by adopting the quaternary encoding method and performing corresponding DNA genetic operators. The relevant optimized parameters are combined with the FLC for single intersection traffic signal control. Simulation experiments shows the better performance of the FLC with the DNA evolutionary algorithm optimization. The experimental results demonstrate the efficiency of the nrotmsed method.
文摘A new algorithm based on genetic algorithm(GA) is developed for solving function optimization problems with inequality constraints. This algorithm has been used to a series of standard test problems and exhibited good performance. The computation results show that its generality, precision, robustness, simplicity and performance are all satisfactory.
基金supported in part by the National Natural Science Foundation of China(61603169,61773192,61803192)in part by the funding from Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technologyin part by Singapore National Research Foundation(NRF-RSS2016-004)
文摘Flexible job shop scheduling problems(FJSP)have received much attention from academia and industry for many years.Due to their exponential complexity,swarm intelligence(SI)and evolutionary algorithms(EA)are developed,employed and improved for solving them.More than 60%of the publications are related to SI and EA.This paper intents to give a comprehensive literature review of SI and EA for solving FJSP.First,the mathematical model of FJSP is presented and the constraints in applications are summarized.Then,the encoding and decoding strategies for connecting the problem and algorithms are reviewed.The strategies for initializing algorithms?population and local search operators for improving convergence performance are summarized.Next,one classical hybrid genetic algorithm(GA)and one newest imperialist competitive algorithm(ICA)with variables neighborhood search(VNS)for solving FJSP are presented.Finally,we summarize,discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions.
基金This work was supported by an EPSRC grant (No.EP/C520696/1).
文摘Computational time complexity analyzes of evolutionary algorithms （EAs） have been performed since the mid-nineties. The first results were related to very simple algorithms, such as the （1＋1）-EA, on toy problems. These efforts produced a deeper understanding of how EAs perform on different kinds of fitness landscapes and general mathematical tools that may be extended to the analysis of more complicated EAs on more realistic problems. In fact, in recent years, it has been possible to analyze the （1＋1）-EA on combinatorial optimization problems with practical applications and more realistic population-based EAs on structured toy problems. This paper presents a survey of the results obtained in the last decade along these two research lines. The most common mathematical techniques are introduced, the basic ideas behind them are discussed and their elective applications are highlighted. Solved problems that were still open are enumerated as are those still awaiting for a solution. New questions and problems arisen in the meantime are also considered.
基金Supported by the National Natural Science Foundation of China(60133010,60073043,70071042)
文摘This paper presents a parallel two-level evolutionary algorithm based on domain decomposition for solving function optimization problem containing multiple solutions. By combining the characteristics of the global search and local search in each sub-domain, the former enables individual to draw closer to each optima and keeps the diversity of individuals, while the latter selects local optimal solutions known as latent solutions in sub-domain. In the end, by selecting the global optimal solutions from latent solutions in each sub-domain, we can discover all the optimal solutions easily and quickly.
基金supported in part by the National Natural Science Foundation of China(61806051,61903078)Natural Science Foundation of Shanghai(20ZR1400400)+2 种基金Agricultural Project of the Shanghai Committee of Science and Technology(16391902800)the Fundamental Research Funds for the Central Universities(2232020D-48)the Project of the Humanities and Social Sciences on Young Fund of the Ministry of Education in China(Research on swarm intelligence collaborative robust optimization scheduling for high-dimensional dynamic decisionmaking system(20YJCZH052))。
文摘Evolutionary algorithms have been shown to be very successful in solving multi-objective optimization problems(MOPs).However,their performance often deteriorates when solving MOPs with irregular Pareto fronts.To remedy this issue,a large body of research has been performed in recent years and many new algorithms have been proposed.This paper provides a comprehensive survey of the research on MOPs with irregular Pareto fronts.We start with a brief introduction to the basic concepts,followed by a summary of the benchmark test problems with irregular problems,an analysis of the causes of the irregularity,and real-world optimization problems with irregular Pareto fronts.Then,a taxonomy of the existing methodologies for handling irregular problems is given and representative algorithms are reviewed with a discussion of their strengths and weaknesses.Finally,open challenges are pointed out and a few promising future directions are suggested.
文摘Application of the multiobjective evolutionary algorithms to the aerodynamicoptimization design of a centrifugal impeller is presented. The aerodynamic performance of acentrifugal impeller is evaluated by using the three-dimensional Navier-Stokes solutions. Thetypical centrifugal impeller is redesigned for maximization of the pressure rise and blade load andminimization of the rotational total pressure loss at the given flow conditions. The Bezier curvesare used to parameterize the three-dimensional impeller blade shape. The present method obtains manyreasonable Pareto optimal designs that outperform the original centrifugal impeller. Detailedobservation of the certain Pareto optimal design demonstrates the feasibility of the presentmultiobjective optimization method tool for turbomachinery design.
文摘Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitness assignment strategy of non-dominated sorting genetic algorithm (NSGA). The fitness assignment strategy is improved and a new self-adjustment scheme of is proposed. This algorithm is proved to be very efficient both computationally and in terms of the quality of the Pareto fronts produced with five test problems including GA difficult problem and GA deceptive one. Finally, SNSGA is introduced to solve multi-objective mixed integer linear programming (MILP) and mixed integer non-linear programming (MINLP) problems in process synthesis.
基金the National Natural Science Foundation of China(No.40839902)
文摘The optimal allocation model of regional water resources is built with the purpose of maximizing the comprehensive economic,social and environmental benefits of regional water consumption.In order to solve the problems that easily appear during the model solution of regional water resource optimal allocation with multiple water sources,multiple users and multiple objectives like"curse of dimensionality"or sinking into local optimum,this paper proposes a particle swarm optimization(PSO)algorithm based on immune evolutionary algorithm(IEA).This algorithm introduces immunology principle into particle swarm algorithm.Its immune memorizing and self-adjusting mechanism is utilized to keep the particles in the fitness level at a certain concentration and guarantee the diversity of population.Also,the global search characteristics of IEA and the local search capacity of particle swarm algorithm have been fully utilized to overcome the dependence of PSO on initial swarm and the deficiency of vulnerability to local optimum.After applying this model to the allocation of water resources in Zhoukou,we obtain the scheme for optimization allocation of water resources in the planning level years,i.e.2015and 2025 under the guarantee rate of 50%.The calculation results indicate that the application of this algorithm to solve the issue of optimal allocation of regional water resources is reliable and reasonable.Thus it ofers a new idea for solving the issue of optimal allocation of water resources.
文摘In this paper we discuss the paradigm of evolutionary algorithms (EAs). We argue about the need for new heuristics in real-world problem solving, discussing reasons why some problems are difficult to solve. After introducing the main concepts of evolutionary algorithms, we concentrate on two issues: (1) self-adaptation of the parameters of EA, and (2) handling constraints.
基金Supported by the National Natural Science Foundation of China (70071042,60073043,60133010)
文摘In this paper, a new algorithm for solving multi-modal function optimization problems-two-level subspace evolutionary algorithm is proposed. In the first level, the improved GT algorithm is used to do global recombination search so that the whole population can be separated into several niches according to the position of solutions; then, in the second level, the niche evolutionary strategy is used for local search in the subspaces gotten in the first level till solutions of the problem are found. The new algorithm has been tested on some hard problems and some good results are obtained.
文摘The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the Kmeans algorithm depends highly on initial cluster centers and converges to local minima. This paper proposes a hybrid evolutionary programming based clustering algorithm, called PSO-SA, by combining particle swarm optimization (PSO) and simulated annealing (SA). The basic idea is to search around the global solution by SA and to increase the information exchange among particles using a mutation operator to escape local optima. Three datasets, Iris, Wisconsin Breast Cancer, and Ripley's Glass, have been considered to show the effectiveness of the proposed clustering algorithm in providing optimal clusters. The simulation results show that the PSO-SA clustering algorithm not only has a better response but also converges more quickly than the K-means, PSO, and SA algorithms.
基金Project(50775089)supported by the National Natural Science Foundation of ChinaProject(2007AA04Z190,2009AA043301)supported by the National High Technology Research and Development Program of ChinaProject(2005CB724100)supported by the National Basic Research Program of China
文摘The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency. A multi-objective model was presented for the material distribution routing problem in mixed manufacturing systems, and it was solved by a hybrid multi-objective evolutionary algorithm (HMOEA). The characteristics of the HMOEA are as follows: 1) A route pool is employed to preserve the best routes for the population initiation; 2) A specialized best?worst route crossover (BWRC) mode is designed to perform the crossover operators for selecting the best route from Chromosomes 1 to exchange with the worst one in Chromosomes 2, so that the better genes are inherited to the offspring; 3) A route swap mode is used to perform the mutation for improving the convergence speed and preserving the better gene; 4) Local heuristics search methods are applied in this algorithm. Computational study of a practical case shows that the proposed algorithm can decrease the total travel distance by 51.66%, enhance the average vehicle load rate by 37.85%, cut down 15 routes and reduce a deliver vehicle. The convergence speed of HMOEA is faster than that of famous NSGA-II.
