In the original Moth-Flame Optimization(MFO),the search behavior of the moth depends on the corresponding flame and the interaction between the moth and its corresponding flame,so it will get stuck in the local optimu...In the original Moth-Flame Optimization(MFO),the search behavior of the moth depends on the corresponding flame and the interaction between the moth and its corresponding flame,so it will get stuck in the local optimum easily when facing the multi-dimensional and high-dimensional optimization problems.Therefore,in this work,a generalized oppositional MFO with crossover strategy,named GCMFO,is presented to overcome the mentioned defects.In the proposed GCMFO,GOBL is employed to increase the population diversity and expand the search range in the initialization and iteration jump phase based on the jump rate;crisscross search(CC)is adopted to promote the exploitation and/or exploration ability of MFO.The proposed algorithm’s performance is estimated by organizing a series of experiments;firstly,the CEC2017 benchmark set is adopted to evaluate the performance of GCMFO in tackling high-dimensional and multimodal problems.Secondly,GCMFO is applied to handle multilevel thresholding image segmentation problems.At last,GCMFO is integrated into kernel extreme learning machine classifier to deal with three medical diagnosis cases,including the appendicitis diagnosis,overweight statuses diagnosis,and thyroid cancer diagnosis.Experimental results and discussions show that the proposed approach outperforms the original MFO and other state-of-the-art algorithms on both convergence speed and accuracy.It also indicates that the presented GCMFO has a promising potential for application.展开更多
This paper presents an efficient enhanced snake optimizer termed BEESO for global optimization and engineering applications.As a newly mooted meta-heuristic algorithm,snake optimizer(SO)mathematically models the matin...This paper presents an efficient enhanced snake optimizer termed BEESO for global optimization and engineering applications.As a newly mooted meta-heuristic algorithm,snake optimizer(SO)mathematically models the mating characteristics of snakes to find the optimal solution.SO has a simple structure and offers a delicate balance between exploitation and exploration.However,it also has some shortcomings to be improved.The proposed BEESO consequently aims to lighten the issues of lack of population diversity,convergence slowness,and the tendency to be stuck in local optima in SO.The presentation of Bi-Directional Search(BDS)is to approach the global optimal value along the direction guided by the best and the worst individuals,which makes the convergence speed faster.The increase in population diversity in BEESO benefits from Modified Evolutionary Population Dynamics(MEPD),and the replacement of poorer quality individuals improves population quality.The Elite Opposition-Based Learning(EOBL)provides improved local exploitation ability of BEESO by utilizing solid solutions with good performance.The performance of BEESO is illustrated by comparing its experimental results with several algorithms on benchmark functions and engineering designs.Additionally,the results of the experiment are analyzed again from a statistical point of view using the Friedman and Wilcoxon rank sum tests.The findings show that these introduced strategies provide some improvements in the performance of SO,and the accuracy and stability of the optimization results provided by the proposed BEESO are competitive among all algorithms.To conclude,the proposed BEESO offers a good alternative to solving optimization issues.展开更多
To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm,this paper presents a new algorithm based on multi-strategy(ISMO).First,the initial populatio...To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm,this paper presents a new algorithm based on multi-strategy(ISMO).First,the initial population is generated by a refracted opposition-based learning strategy to enhance diversity and ergodicity.Second,this paper introduces a non-linear adaptive dynamic weight factor to improve convergence efficiency.Then,using the crisscross strategy,using the horizontal crossover to enhance the global search and vertical crossover to keep the diversity of the population to avoid being trapped in the local optimum.At last,we adopt a Gauss-Cauchy mutation strategy to improve the stability of the algorithm by mutation of the optimal individuals.Therefore,the application of ISMO is validated by ten benchmark functions and feature selection.It is proved that the proposed method can resolve the problem of feature selection.展开更多
The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resource...The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.展开更多
基金This research is supported by the National Natural Science Foundation of China(62076185,U1809209)Zhejiang Provincial Natural Science Foundation of China(LY21F020030)+2 种基金Wenzhou Science&Technology Bureau(2018ZG016)Taif University Researchers Supporting Project Number(TURSP-2020/125)Taif University,Taif,Saudi Arabia。
文摘In the original Moth-Flame Optimization(MFO),the search behavior of the moth depends on the corresponding flame and the interaction between the moth and its corresponding flame,so it will get stuck in the local optimum easily when facing the multi-dimensional and high-dimensional optimization problems.Therefore,in this work,a generalized oppositional MFO with crossover strategy,named GCMFO,is presented to overcome the mentioned defects.In the proposed GCMFO,GOBL is employed to increase the population diversity and expand the search range in the initialization and iteration jump phase based on the jump rate;crisscross search(CC)is adopted to promote the exploitation and/or exploration ability of MFO.The proposed algorithm’s performance is estimated by organizing a series of experiments;firstly,the CEC2017 benchmark set is adopted to evaluate the performance of GCMFO in tackling high-dimensional and multimodal problems.Secondly,GCMFO is applied to handle multilevel thresholding image segmentation problems.At last,GCMFO is integrated into kernel extreme learning machine classifier to deal with three medical diagnosis cases,including the appendicitis diagnosis,overweight statuses diagnosis,and thyroid cancer diagnosis.Experimental results and discussions show that the proposed approach outperforms the original MFO and other state-of-the-art algorithms on both convergence speed and accuracy.It also indicates that the presented GCMFO has a promising potential for application.
基金supported by the National Natural Science Foundation of China (Grant No.51875454).
文摘This paper presents an efficient enhanced snake optimizer termed BEESO for global optimization and engineering applications.As a newly mooted meta-heuristic algorithm,snake optimizer(SO)mathematically models the mating characteristics of snakes to find the optimal solution.SO has a simple structure and offers a delicate balance between exploitation and exploration.However,it also has some shortcomings to be improved.The proposed BEESO consequently aims to lighten the issues of lack of population diversity,convergence slowness,and the tendency to be stuck in local optima in SO.The presentation of Bi-Directional Search(BDS)is to approach the global optimal value along the direction guided by the best and the worst individuals,which makes the convergence speed faster.The increase in population diversity in BEESO benefits from Modified Evolutionary Population Dynamics(MEPD),and the replacement of poorer quality individuals improves population quality.The Elite Opposition-Based Learning(EOBL)provides improved local exploitation ability of BEESO by utilizing solid solutions with good performance.The performance of BEESO is illustrated by comparing its experimental results with several algorithms on benchmark functions and engineering designs.Additionally,the results of the experiment are analyzed again from a statistical point of view using the Friedman and Wilcoxon rank sum tests.The findings show that these introduced strategies provide some improvements in the performance of SO,and the accuracy and stability of the optimization results provided by the proposed BEESO are competitive among all algorithms.To conclude,the proposed BEESO offers a good alternative to solving optimization issues.
文摘To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm,this paper presents a new algorithm based on multi-strategy(ISMO).First,the initial population is generated by a refracted opposition-based learning strategy to enhance diversity and ergodicity.Second,this paper introduces a non-linear adaptive dynamic weight factor to improve convergence efficiency.Then,using the crisscross strategy,using the horizontal crossover to enhance the global search and vertical crossover to keep the diversity of the population to avoid being trapped in the local optimum.At last,we adopt a Gauss-Cauchy mutation strategy to improve the stability of the algorithm by mutation of the optimal individuals.Therefore,the application of ISMO is validated by ten benchmark functions and feature selection.It is proved that the proposed method can resolve the problem of feature selection.
文摘The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.
