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An adaptive penalty-based boundary intersection approach for multiobjective evolutionary algorithm based on decomposition

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Bibliographic Details
Title: An adaptive penalty-based boundary intersection approach for multiobjective evolutionary algorithm based on decomposition
Authors: Yang, Shengxiang, Jiang, Shouyong
Publisher Information: IEEE
Publication Year: 2016
Collection: De Montfort University, Leicester: Open Research Archive (DORA)
Subject Terms: Multiobjective evolutionary algorithm based on decomposition (MOEA/D), multiobjective optimization problem, penalty-based boundary intersection, adaptive penalty scheme
Description: The multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem into a number of sing-objective subproblems and solves them collaboratively. Since its introduction, MOEA/D has gained increasing research interest and has become a benchmark for validating new designed algorithms. Despite that, some recent studies have revealed that MOEA/D faces some difficulties to solve problems with complicated characteristics. In this paper, we study the influence the penalty-based boundary intersection (PBI) approach, one of the most popular decomposition approaches used in MOEA/D, on individuals’ convergence and diversity, showing that the fixed same penalty value for all the subproblems is not very sensible. Based on this observation, we propose to use adaptive penalty values to enhance the balance between population convergence and diversity. Experimental studies show that the proposed adaptive penalty scheme can generally improve the performance of the original PBI when solving the problems considered in this paper.
Document Type: conference object
File Description: application/pdf
Language: English
Relation: https://hdl.handle.net/2086/11892; https://doi.org/10.1109/CEC.2016.7744053
DOI: 10.1109/CEC.2016.7744053
Availability: https://hdl.handle.net/2086/11892
https://doi.org/10.1109/CEC.2016.7744053
Accession Number: edsbas.CFB55425
Database: BASE
Description
Abstract:The multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem into a number of sing-objective subproblems and solves them collaboratively. Since its introduction, MOEA/D has gained increasing research interest and has become a benchmark for validating new designed algorithms. Despite that, some recent studies have revealed that MOEA/D faces some difficulties to solve problems with complicated characteristics. In this paper, we study the influence the penalty-based boundary intersection (PBI) approach, one of the most popular decomposition approaches used in MOEA/D, on individuals’ convergence and diversity, showing that the fixed same penalty value for all the subproblems is not very sensible. Based on this observation, we propose to use adaptive penalty values to enhance the balance between population convergence and diversity. Experimental studies show that the proposed adaptive penalty scheme can generally improve the performance of the original PBI when solving the problems considered in this paper.
DOI:10.1109/CEC.2016.7744053