An Improved Multi-Objective Particle Swarm Optimization Algorithm Based on Angle Preference

Multi-objective particle swarm optimization (MOPSO) algorithms based on angle preference provide a set of preferred solutions by incorporating a user’s preference. However, since the search mechanism is stochastic and asymmetric, traditional MOPSO based on angle preference are still easy to fall int...

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Vydáno v:Symmetry (Basel) Ročník 14; číslo 12; s. 2619
Hlavní autoři: Ling, Qing-Hua, Tang, Zhi-Hao, Huang, Gan, Han, Fei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.12.2022
Témata:
Algorithms
Analysis
Archives & records
Data exchange
Decision making
Genetic algorithms
Mathematical optimization
Multiple objective analysis
Objectives
Optimization algorithms
Particle swarm optimization
Preferences
Random variables
ISSN:2073-8994, 2073-8994
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Shrnutí:Multi-objective particle swarm optimization (MOPSO) algorithms based on angle preference provide a set of preferred solutions by incorporating a user’s preference. However, since the search mechanism is stochastic and asymmetric, traditional MOPSO based on angle preference are still easy to fall into local optima and lack enough selection pressure on excellent individuals. In this paper, an improved MOPSO algorithm based on angle preference called IAPMOPSO is proposed to alleviate those problems. First, to create a stricter partial order among the non-dominated solutions, reference vectors are established in the preference region, and the adaptive penalty-based boundary intersection (PBI) value is used to update the external archive. Second, to effectively alleviate the swarm to fall into local optima, an adaptive preference angle is designed to increase the diversity of the population. Third, neighborhood individuals are selected for each particle to update the individual optimum to increase the information exchange among the particles. With the proposed angle preference-based external archive update strategy, solutions with a smaller PBI are given higher priority to be selected, and thus the selection pressure on excellent individuals is enhanced. In terms of an increase in the diversity of the population, the adaptive preference angle adjustment strategy that gradually narrows the preferred area, and the individual optimum update strategy which updates the individual optimum according to the information of neighborhood individuals, are presented. The experimental results on the benchmark test functions and GEM data verify the effectiveness and efficiency of the proposed method.
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ISSN:2073-8994
2073-8994
DOI:10.3390/sym14122619