Environment Sensitivity-Based Cooperative Co-Evolutionary Algorithms for Dynamic Multi-Objective Optimization

Dynamic multi-objective optimization problems (DMOPs) not only involve multiple conflicting objectives, but these objectives may also vary with time, raising a challenge for researchers to solve them. This paper presents a cooperative co-evolutionary strategy based on environment sensitivities for s...

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Vydáno v:IEEE/ACM transactions on computational biology and bioinformatics Ročník 15; číslo 6; s. 1877 - 1890
Hlavní autoři: Xu, Biao, Zhang, Yong, Gong, Dunwei, Guo, Yinan, Rong, Miao
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.11.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
cooperative co-evolution
Dynamic multi-objective optimization
Environments
evolutionary algorithm
Evolutionary algorithms
Evolutionary computation
Heuristic algorithms
Multiple objective analysis
Optimization
particle swarm
Particle swarm optimization
Prediction algorithms
Sensitivity
Sociology
State of the art
Strategy
ISSN:1545-5963, 1557-9964, 1557-9964
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Shrnutí:Dynamic multi-objective optimization problems (DMOPs) not only involve multiple conflicting objectives, but these objectives may also vary with time, raising a challenge for researchers to solve them. This paper presents a cooperative co-evolutionary strategy based on environment sensitivities for solving DMOPs. In this strategy, a new method that groups decision variables is first proposed, in which all the decision variables are partitioned into two subcomponents according to their interrelation with environment. Adopting two populations to cooperatively optimize the two subcomponents, two prediction methods, i.e., differential prediction and Cauchy mutation, are then employed respectively to speed up their responses on the change of the environment. Furthermore, two improved dynamic multi-objective optimization algorithms, i.e., DNSGAII-CO and DMOPSO-CO, are proposed by incorporating the above strategy into NSGA-II and multi-objective particle swarm optimization, respectively. The proposed algorithms are compared with three state-of-the-art algorithms by applying to seven benchmark DMOPs. Experimental results reveal that the proposed algorithms significantly outperform the compared algorithms in terms of convergence and distribution on most DMOPs.
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ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2017.2652453