A Decomposition-Based Evolutionary Algorithm with Adaptive Weight Vectors for Multi- and Many-objective Optimization

2020 ◽  
pp. 149-164
Author(s):  
Guang Peng ◽  
Katinka Wolter
Keyword(s):  
Evolutionary Algorithm ◽  
Adaptive Weight

An adaptive weight vector guided evolutionary algorithm for preference-based multi-objective optimization

2019 ◽  
Vol 49 ◽  
pp. 220-233 ◽  
Author(s):  
Feng Wang ◽  
Yixuan Li ◽  
Heng Zhang ◽  
Ting Hu ◽  
Xiao-Liang Shen
Keyword(s):  
Evolutionary Algorithm ◽  
Weight Vector ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Adaptive Weight

scholarly journals A Decomposition-Based Multiobjective Evolutionary Algorithm with Adaptive Weight Adjustment

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Cai Dai ◽  
Xiujuan Lei
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Uniform Design ◽  
Weight Vector ◽  
Selection Strategy ◽  
Main Role ◽  
Multiobjective Evolutionary Algorithm ◽  
Adjustment Strategy ◽  
Multiobjective Optimization Problems ◽  
Adaptive Weight

Recently, decomposition-based multiobjective evolutionary algorithms have good performances in the field of multiobjective optimization problems (MOPs) and have been paid attention by many scholars. Generally, a MOP is decomposed into a number of subproblems through a set of weight vectors with good uniformly and aggregate functions. The main role of weight vectors is to ensure the diversity and convergence of obtained solutions. However, these algorithms with uniformity of weight vectors cannot obtain a set of solutions with good diversity on some MOPs with complex Pareto optimal fronts (PFs) (i.e., PFs with a sharp peak or low tail or discontinuous PFs). To deal with this problem, an improved decomposition-based multiobjective evolutionary algorithm with adaptive weight adjustment (IMOEA/DA) is proposed. Firstly, a new method based on uniform design and crowding distance is used to generate a set of weight vectors with good uniformly. Secondly, according to the distances of obtained nondominated solutions, an adaptive weight vector adjustment strategy is proposed to redistribute the weight vectors of subobjective spaces. Thirdly, a selection strategy is used to help each subobjective space to obtain a nondominated solution (if have). Comparing with six efficient state-of-the-art algorithms, for example, NSGAII, MOEA/D, MOEA/D-AWA, EMOSA, RVEA, and KnEA on some benchmark functions, the proposed algorithm is able to find a set of solutions with better diversity and convergence.

A decomposition-based evolutionary algorithm with adaptive weight adjustment for many-objective problems

2019 ◽  
Vol 24 (14) ◽  
pp. 10597-10609 ◽  
Author(s):  
Cai Dai ◽  
Xiujuan Lei ◽  
Xiaoguang He
Keyword(s):  
Evolutionary Algorithm ◽  
Adaptive Weight

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

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Guo-Zhong Fu ◽  
Tianda Yu ◽  
Wei Li ◽  
Qiang Deng ◽  
Bo Yang
Keyword(s):  
Evolutionary Algorithms ◽  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Multiobjective Evolutionary Algorithms ◽  
Optimal Solutions ◽  
Optimal Weight ◽  
Vector Generation ◽  
Adaptive Weight ◽  
Fixed Set ◽  
Distributed Solutions

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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