Results 221 to 230 of about 75,350 (240)

Online objective reduction for many-objective optimization problems

2014 IEEE Congress on Evolutionary Computation (CEC), 2014
For many-objective optimization problems, i.e. the number of objectives is greater than three, the performance of most of the existing Evolutionary Multi-objective Optimization algorithms will deteriorate to a certain degree. It is therefore desirable to reduce many objectives to fewer essential objectives, if applicable.
Yiu-ming Cheung, Fangqing Gu
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An improved competitive particle swarm optimization for many-objective optimization problems

Expert Systems with Applications, 2022
Abstract Multi-objective particle swarm optimization (MOPSO) has been widely applied to solve multi-objective optimization problems (MOPs), due to its efficient implementation and fast convergence. However, most MOPSOs are ineffective in achieving the balance between convergence and diversity in the high-dimensional objective space. In this paper, an
Qinghua Gu, Yingyin Liu, Lu Chen, Naixue Xiong   +3 more
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A pigeon-inspired optimization algorithm for many-objective optimization problems

Science China Information Sciences, 2019
Swarm intelligence optimization algorithms are in-spired by the behaviour of biological groups in nature. Such algorithms have the advantages of aclear structure, simple operation, comprehensible principles, strong parallelism, effective search abilities, and strong robustness.
Cui, Zhihua, Zhang, Jiangjiang, Wang, Yechuang, Cao, Yang, Cai, Xingjuan, Zhang, Wensheng, Chen, Jinjun   +6 more
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Evolutionary Path Control Strategy for Solving Many-Objective Optimization Problem

IEEE Transactions on Cybernetics, 2015
The number of objectives in many-objective optimization problems (MaOPs) is typically high and evolutionary algorithms face severe difficulties in solving such problems. In this paper, we propose a new scalable evolutionary algorithm, called evolutionary path control strategy (EPCS), for solving MaOPs.
Proteek Chandan, Roy, Md Monirul, Islam, Kazuyuki, Murase, Xin, Yao   +3 more
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Objective Extraction for Many-Objective Optimization Problems: Algorithm and Test Problems

IEEE Transactions on Evolutionary Computation, 2016
For many-objective optimization problems (MaOPs), in which the number of objectives is greater than three, the performance of most existing evolutionary multi-objective optimization algorithms generally deteriorates over the number of objectives. As some MaOPs may have redundant or correlated objectives, it is desirable to reduce the number of the ...
Yiu-ming Cheung, Fangqing Gu, Hai-Lin Liu   +2 more
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Convergence properties of E-optimality algorithms for Many objective Optimization Problems

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008
In the paper, for many-objective optimization problems, the authors pointed out that the Pareto Optimality is unfair, unreasonable and imperfect for Many-objective Optimization Problems (MOPs) underlying the hypothesis that all objectives have equal importance and propose a new evolutionary decision theory.
null Zhuo Kang, null Lishan Kang, null Changhe Li, null Yuping Chen, null Minzhong Liu   +4 more
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An evolutionary based framework for many-objective optimization problems

Engineering Computations, 2018
Purpose Recently, many-objective optimization evolutionary algorithms have been the main issue for researchers in the multi-objective optimization community. To deal with many-objective problems (typically for four or more objectives) some modern frameworks are proposed which have the potential of achieving the finest non-dominated solutions in many ...
Kimia Bazargan Lari, Ali Hamzeh
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Quantum particle swarm algorithm for Many-objective optimization problem

Proceeding of the 11th World Congress on Intelligent Control and Automation, 2014
Many-objective optimization problems widely exist in real world. However, there is lack of effective solutions to solve this problem because they contain more than three conflicting objectives. Based on quantum particle swarm optimization algorithm, this paper presents an efficient many-objective particle swarm optimization algorithm. In this algorithm,
Changhong Xia, Yong Zhang, Dunwei Gong, Xiaoyan Sun   +3 more
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An improved secondary ranking for many objective optimization problems

Proceedings of the 11th Annual conference on Genetic and evolutionary computation, 2009
Many objective optimization refers to optimization problems for which the number of objectives is significantly greater than conventionally studied 2 or 3. For such problems, large number of solutions become non-dominated, which reduces the convergence pressure of the Evolutionary Algorithms~(EAs) towards the Pareto Optimal Front.
Hemant Kumar Singh, Amitay Isaacs, Tapabrata Ray, Warren Smith   +3 more
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Many-objective optimization with dynamic constraint handling for constrained optimization problems

Soft Computing, 2016
In real-world applications, the optimization problems are usually subject to various constraints. To solve constrained optimization problems (COPs), this paper presents a new methodology, which incorporates a dynamic constraint handling mechanism into many-objective evolutionary optimization.
Xi Li, Sanyou Zeng, Changhe Li, Jiantao Ma   +3 more
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