Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients

被引：468

作者：

Tripathi, Praveen Kumar ^{[1
]}

Bandyopadhyay, Sanghamitra ^{[1
]}

Pal, Sankar Kumar ^{[1
]}

机构：

[1] Indian Stat Inst, Machine Intelligence Unit, Kolkata 700108, India

来源：

INFORMATION SCIENCES | 2007年 / 177卷 / 22期

关键词：

multi-objective optimization; pareto dominance; Particle Swarm Optimization;

D O I：

10.1016/j.ins.2007.06.018

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this article we describe a novel Particle Swarm Optimization (PSO) approach to multi-objective optimization (MOO), called Time Variant Multi-Objective Particle Swarm Optimization (TV-MOPSO). TV-MOPSO is made adaptive in nature by allowing its vital parameters (viz., inertia weight and acceleration coefficients) to change with iterations. This adaptiveness helps the algorithm to explore the search space more efficiently. A new diversity parameter has been used to ensure sufficient diversity amongst the solutions of the non-dominated fronts, while retaining at the same time the convergence to the Pareto-optimal front. TV-MOPSO has been compared with some recently developed multi-objective PSO techniques and evolutionary algorithms for I I function optimization problems, using different performance measures. (C) 2007 Elsevier Inc. All rights reserved.

引用

页码：5033 / 5049

页数：17

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