In solving many-objective optimization problems(MaO Ps),existing nondominated sorting-based multi-objective evolutionary algorithms suffer from the fast loss of selection pressure.Most candidate solutions become nondo...In solving many-objective optimization problems(MaO Ps),existing nondominated sorting-based multi-objective evolutionary algorithms suffer from the fast loss of selection pressure.Most candidate solutions become nondominated during the evolutionary process,thus leading to the failure of producing offspring toward Pareto-optimal front with diversity.Can we find a more effective way to select nondominated solutions and resolve this issue?To answer this critical question,this work proposes to evolve solutions through line complex rather than solution points in Euclidean space.First,Plücker coordinates are used to project solution points to line complex composed of position vectors and momentum ones.Besides position vectors of the solution points,momentum vectors are used to extend the comparability of nondominated solutions and enhance selection pressure.Then,a new distance function designed for high-dimensional space is proposed to replace Euclidean distance as a more effective distancebased estimator.Based on them,a novel many-objective evolutionary algorithm(MaOEA)is proposed by integrating a line complex-based environmental selection strategy into the NSGAⅢframework.The proposed algorithm is compared with the state of the art on widely used benchmark problems with up to 15 objectives.Experimental results demonstrate its superior competitiveness in solving MaOPs.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
Artificial Bee Colony(ABC)algorithm is a classical Swarm Intelligence Optimization Algorithm(SIOA),which has been widely used to solve various optimization problems.However,these problems mainly focus on single-object...Artificial Bee Colony(ABC)algorithm is a classical Swarm Intelligence Optimization Algorithm(SIOA),which has been widely used to solve various optimization problems.However,these problems mainly focus on single-objective and ordinary Multi-objective Optimization Problems(MOPs).For Many-objective Optimization Problems(MaOPs),ABC shows some difficulties:(1)the selection pressure based on Pareto dominance degrades severely;and(2)it is not easy to balance convergence and population diversity.In this paper,a new Many-Objective ABC variant with Hybrid Strategies(namely HSMaOABC)is proposed to deal with MaOPs.Firstly,the fitness function is redefined based on objective values and cosine similarity to handle multiple objectives.Then,a new selection method is designed on the basis of the new fitness function.In order to enhance convergence,an elite set guided search strategy is utilized for the employed bee stage,and dimensional learning is incorporated for the onlooker bee stage.Finally,a modified environmental selection strategy is employed based on Penalty-based Boundary Intersection(PBI)distance.To evaluate the performance of HSMaOABC,the DTLZ and MaF benchmarks with 3,5,8,and 15 objectives are used.Experimental results demonstrate that HSMaOABC obtains competitive performance when compared with nine other well-known approaches.展开更多
With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are non-dominant.Therefore,the selection of individuals and the retention of elite individuals are important.Ex...With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are non-dominant.Therefore,the selection of individuals and the retention of elite individuals are important.Existing algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial problems.Thus,this work proposes an improved many-objective pigeon-inspired optimization(ImMAPIO)algorithm with multiple selection strategies to solve many-objective optimization problems.Multiple selection strategies integrating hypervolume,knee point,and vector angles are utilized to increase selection pressure to the true Pareto Front.Thus,the accuracy,convergence,and diversity of solutions are improved.ImMAPIO is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III,GrEA,MOEA/D,RVEA,and many-objective Pigeon-inspired optimization algorithm.Experimental results indicate the superiority of ImMAPIO on these test functions.展开更多
The multi-objective optimization problem has been encountered in numerous fields such as high-speed train head shape design,overlapping community detection,power dispatch,and unmanned aerial vehicle formation.To addre...The multi-objective optimization problem has been encountered in numerous fields such as high-speed train head shape design,overlapping community detection,power dispatch,and unmanned aerial vehicle formation.To address such issues,current approaches focus mainly on problems with regular Pareto front rather than solving the irregular Pareto front.Considering this situation,we propose a many-objective evolutionary algorithm based on decomposition with dynamic resource allocation(Ma OEA/D-DRA)for irregular optimization.The proposed algorithm can dynamically allocate computing resources to different search areas according to different shapes of the problem’s Pareto front.An evolutionary population and an external archive are used in the search process,and information extracted from the external archive is used to guide the evolutionary population to different search regions.The evolutionary population evolves with the Tchebycheff approach to decompose a problem into several subproblems,and all the subproblems are optimized in a collaborative manner.The external archive is updated with the method of rithms using a variety of test problems with irregular Pareto front.Experimental results show that the proposed algorithèm out-p￡performs these five algorithms with respect to convergence speed and diversity of population members.By comparison with the weighted-sum approach and penalty-based boundary intersection approach,there is an improvement in performance after integration of the Tchebycheff approach into the proposed algorithm.展开更多
基金supported in part by the National Natural Science Foundation of China(51775385)the Natural Science Foundation of Shanghai(23ZR1466000)+3 种基金the Shanghai Industrial Collaborative Science and Technology Innovation Project(2021-cyxt2-kj10)the Innovation Program of Shanghai Municipal Education Commission(202101070007E00098)the Innovation Project of Engineering Research Center of Integration and Application of Digital Learning Technology of MOE(1221046)the Program to Cultivate Middle-Aged and Young Cadre Teacher of Jiangsu Province。
文摘In solving many-objective optimization problems(MaO Ps),existing nondominated sorting-based multi-objective evolutionary algorithms suffer from the fast loss of selection pressure.Most candidate solutions become nondominated during the evolutionary process,thus leading to the failure of producing offspring toward Pareto-optimal front with diversity.Can we find a more effective way to select nondominated solutions and resolve this issue?To answer this critical question,this work proposes to evolve solutions through line complex rather than solution points in Euclidean space.First,Plücker coordinates are used to project solution points to line complex composed of position vectors and momentum ones.Besides position vectors of the solution points,momentum vectors are used to extend the comparability of nondominated solutions and enhance selection pressure.Then,a new distance function designed for high-dimensional space is proposed to replace Euclidean distance as a more effective distancebased estimator.Based on them,a novel many-objective evolutionary algorithm(MaOEA)is proposed by integrating a line complex-based environmental selection strategy into the NSGAⅢframework.The proposed algorithm is compared with the state of the art on widely used benchmark problems with up to 15 objectives.Experimental results demonstrate its superior competitiveness in solving MaOPs.
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.
基金supported by the National Natural Science Foundation of China(No.62166027)the Jiangxi Provincial Natural Science Foundation(No.20212ACB212004).
文摘Artificial Bee Colony(ABC)algorithm is a classical Swarm Intelligence Optimization Algorithm(SIOA),which has been widely used to solve various optimization problems.However,these problems mainly focus on single-objective and ordinary Multi-objective Optimization Problems(MOPs).For Many-objective Optimization Problems(MaOPs),ABC shows some difficulties:(1)the selection pressure based on Pareto dominance degrades severely;and(2)it is not easy to balance convergence and population diversity.In this paper,a new Many-Objective ABC variant with Hybrid Strategies(namely HSMaOABC)is proposed to deal with MaOPs.Firstly,the fitness function is redefined based on objective values and cosine similarity to handle multiple objectives.Then,a new selection method is designed on the basis of the new fitness function.In order to enhance convergence,an elite set guided search strategy is utilized for the employed bee stage,and dimensional learning is incorporated for the onlooker bee stage.Finally,a modified environmental selection strategy is employed based on Penalty-based Boundary Intersection(PBI)distance.To evaluate the performance of HSMaOABC,the DTLZ and MaF benchmarks with 3,5,8,and 15 objectives are used.Experimental results demonstrate that HSMaOABC obtains competitive performance when compared with nine other well-known approaches.
基金This work was supported by the National Key Research and Development Program of China(No.2018YFC1604000)the National Natural Science Foundation of China(Nos.61806138,61772478,U1636220,61961160707,and 61976212)+2 种基金the Key R&D Program of Shanxi Province(High Technology)(No.201903D121119)the Key R&D Program of Shanxi Province(International Cooperation)(No.201903D421048)the Key R&D Program(International Science and Technology Cooperation Project)of Shanxi Province,China(No.201903D421003).
文摘With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are non-dominant.Therefore,the selection of individuals and the retention of elite individuals are important.Existing algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial problems.Thus,this work proposes an improved many-objective pigeon-inspired optimization(ImMAPIO)algorithm with multiple selection strategies to solve many-objective optimization problems.Multiple selection strategies integrating hypervolume,knee point,and vector angles are utilized to increase selection pressure to the true Pareto Front.Thus,the accuracy,convergence,and diversity of solutions are improved.ImMAPIO is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III,GrEA,MOEA/D,RVEA,and many-objective Pigeon-inspired optimization algorithm.Experimental results indicate the superiority of ImMAPIO on these test functions.
基金the National Natural Science Foundation of China(Nos.6156301261802085+5 种基金and 61203109)the Guangxi Natural Science Foundation of China(Nos.2014GhXN6SF AA1183712015GXNSFBA139260and 2020GXNSFAA159038)the Guangxi Key Laboratory of Embedded Technology and Intelligent System Foundation(No.2018A-04)the Guangxi Key Laboratory of Trusted Software Foundation(Nos.kx202011 and khx2601926)。
文摘The multi-objective optimization problem has been encountered in numerous fields such as high-speed train head shape design,overlapping community detection,power dispatch,and unmanned aerial vehicle formation.To address such issues,current approaches focus mainly on problems with regular Pareto front rather than solving the irregular Pareto front.Considering this situation,we propose a many-objective evolutionary algorithm based on decomposition with dynamic resource allocation(Ma OEA/D-DRA)for irregular optimization.The proposed algorithm can dynamically allocate computing resources to different search areas according to different shapes of the problem’s Pareto front.An evolutionary population and an external archive are used in the search process,and information extracted from the external archive is used to guide the evolutionary population to different search regions.The evolutionary population evolves with the Tchebycheff approach to decompose a problem into several subproblems,and all the subproblems are optimized in a collaborative manner.The external archive is updated with the method of rithms using a variety of test problems with irregular Pareto front.Experimental results show that the proposed algorithèm out-p￡performs these five algorithms with respect to convergence speed and diversity of population members.By comparison with the weighted-sum approach and penalty-based boundary intersection approach,there is an improvement in performance after integration of the Tchebycheff approach into the proposed algorithm.
