A Decomposition-Based Multiobjective Optimization Evolutionary Algorithm with Adaptive Weight Generation Strategy

被引：0
|
作者：
Fu, Guo-Zhong [1 ]
Yu, Tianda [1 ]
Li, Wei [1 ]
Deng, Qiang [1 ]
Yang, Bo [1 ]
机构：
[1] Nucl Power Inst China, Sci & Technol Reactor Syst Design Technol Lab, Chengdu 610213, Peoples R China
来源：
MATHEMATICAL PROBLEMS IN ENGINEERING | 2021年 / 2021卷
关键词：
NONDOMINATED SORTING APPROACH; FAILURE MODE; DIVERSITY; CONVERGENCE; MOEA/D;
D O I：
10.1155/2021/2764558
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is the seminal framework of multiobjective evolutionary algorithms (MOEAs). To alleviate the nonuniformly distributed solutions generated by a fixed set of evenly distributed weight vectors in the presence of nonconvex and disconnected problems, an adaptive vector generation mechanism is proposed. A coevolution strategy and a vector generator are synergistically cooperated to remedy the weight vectors. Optimal weight vectors are generated to replace the useless weight vectors to assure that optimal solutions are distributed evenly. Experiment results indicate that this mechanism is efficient in improving the diversity of MOEA/D.
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页数：12
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