Constraint handling technique based on Lebesgue measure for constrained multiobjective particle swarm optimization algorithm

In this paper, we study on how to achieve balance between minimizing the objectives, satisfying the constraints, and avoiding the population to stuck at locally optimal or locally feasible regions. We propose a new constraints handling technique based on diversity distance measure for constrained mu...

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Vydáno v:Knowledge-based systems Ročník 227; s. 107131
Hlavní autoři: Wang, Hui, Cai, Tie, Li, Kangshun, Pedrycz, Witold
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 05.09.2021
Elsevier Science Ltd
Témata:
Algorithms
Competition
Competitiveness
Constraints
Constraints handle technique based on Lebesgue measure
Diversity distance measure
Experiments
Function words
Interactive effects of constraints
Mahalanobis distance
Modified mechanism of objective space
Multiple objective analysis
Objectives
Optimization
Pareto optimization
Particle swarm optimization
Pull and Push search
Regions
Diversity distance measure
Pull and Push search
Modified mechanism of objective space
Mahalanobis distance
Constraints handle technique based on Lebesgue measure
Interactive effects of constraints
ISSN:0950-7051, 1872-7409
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Shrnutí:In this paper, we study on how to achieve balance between minimizing the objectives, satisfying the constraints, and avoiding the population to stuck at locally optimal or locally feasible regions. We propose a new constraints handling technique based on diversity distance measure for constrained multiobjective optimization problems. We innovatively measure all effects of the constraints on the objectives by the interaction effects and direct effects of constraints on the objectives. The interactive effects of constraints on the objective functions are firstly expressed and quantified by using Mahalanobis distance and computing the value of the distance measure by involving a Lebesgue measure. Based on interactive and direct effects, we propose a new modified mechanism of objective space, called objective space modified mechanism based on diversity distance measure, so that the good infeasible solution could be more effectively used to find the optimal solution. The Pull and Push search is used to adaptively adjust the location of the Pareto front, which could preclude the population from being stuck at some locally optimal or locally feasible regions and could decrease time complexity. Comprehensive experiments completed for several benchmark problems demonstrate the competitiveness of the proposed algorithm, in comparison to the existing state-of-art constrained evolutionary multiobjective optimization.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2021.107131