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A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization

机译：基于动态边界搜索的多目标粒子群优化算法对约束优化

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摘要

Due to increased search complexity in multi-objective optimization, premature convergence becomes a problem. Complex engineering problems poses high number of variables with many constraints. Hence, more difficult benchmark problems must be utilized to validate new algorithms performance. A well-known optimizer, Multi-Objective Particle Swarm Optimizer (MOPSO), has a few weakness that needs to be addressed, specifically its convergence in high dimensional problems and its constraints handling capability. For these reasons, we propose a modified MOPSO (M-MOPSO) to improve upon these aspects. M-MOPSO is compared with four other algorithms namely, MOPSO, Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Evolutionary Algorithm based on Decompositions (MOEA/D) and Multi-Objective Differential Evolution (MODE). M-MOPSO emerged as the best algorithm in eight out of the ten constrained benchmark problems. It also shows promising results in bioprocess application problems and tumor treatment problems. In overall, M-MOPSO was able to solve multi-objective problems with good convergence and is suitable to be used in real world problem. (C) 2018 Elsevier B.V. All rights reserved.
机译：由于在多目标优化中的搜索复杂性提高，早产会成为问题。复杂的工程问题具有许多约束的大量变量。因此，必须利用更困难的基准问题来验证新的算法性能。众所周知的优化器，多目标粒子群优化器（MOPSO）具有一些需要解决的弱点，特别是其在高维问题的收敛及其约束处理能力。由于这些原因，我们提出了改进的MOPSO（M-MOPSO）来改善这些方面。将M-MOPSO与四个其他算法进行比较，即基于分解（MOEA / D）和多目标差分进化（模式）的MOPSO，多目标灰狼优化器（MOGWO），多目标进化算法。 M-MOPSO被出现为最佳算法，其中八个受限的基准问题中的八个。它还展示了生物过程应用问题和肿瘤治疗问题的有希望的结果。总体而言，M-MOPSO能够解决良好收敛的多目标问题，适合于现实世界问题。 （c）2018 Elsevier B.v.保留所有权利。

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