Author:
Xiong Lixue,Chen Debao,Zou Feng,Ge Fangzhen,Liu Fuqiang
Abstract
AbstractWeighted optimization framework (WOF) achieves variable dimensionality reduction by grouping variables and optimizing weights, playing an important role in large-scale multi-objective optimization problems. However, because of possible problems such as duplicate weight vectors in the selection process and loss of population diversity, the algorithm is susceptible to local optimization. Therefore, this paper develops an algorithm framework called multi-population multi-stage adaptive weighted optimization (MPSOF) to improve the performance of WOF in two aspects. First, the method of using multi-population is employed to address the issue of insufficient algorithmic diversity, while simultaneously reducing the likelihood of converging towards local optima. Secondly, a processing stage is incorporated into MPSOF, where a certain number of individuals are adaptively selected for updating based on the weight information and evolutionary status of different subpopulations, targeting different types of weights. This approach alleviates the impact of repetitive weights on the diversity of newly generated individuals, avoids the drawback of easily converging to local optima when using a single type of weight for updating, and effectively balances the diversity and convergence of subpopulations. Experiments of three types designed on several typical function sets demonstrate that MPSOF exceeds the comparison algorithms in the three metrics for Inverse Generation Distance, Hypervolume and Spacing.
Funder
National Natural Science Foundation of China
Top talent projects in disciplines (majors) at colleges and universities in Anhui Province
The funding plan for scientific research activities of academic and technical leaders and reserve candidates in Anhui Province
Intelligent computing theory and application of excellent scientific research and innovation team of Anhui Province
Publisher
Springer Science and Business Media LLC
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