A Comprehensive Study of Particle Swarm Based Multi-objective Optimization

被引：0

作者：

Mohankrishna, Samantula ^{[1
]}

Maheshwari, Divya ^{[2
]}

Satyanarayana, P. ^{[3
]}

Satapathy, Suresh Chandra ^{[4
]}

机构：

[1] Gitam Univ, GIT, Dept IT, Visakhapatnam, Andhra Pradesh, India

[2] IME Sahibabad, Ghaziabad, Uttar Pradesh, India

[3] Vizag Steel, Visakhapatnam, Andhra Pradesh, India

[4] Anil Neerukonda Inst Technol & Sci, Dept CSE, Visakhapatnam, Andhra Pradesh, India

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS DESIGN AND INTELLIGENT APPLICATIONS 2012 (INDIA 2012) | 2012年 / 132卷

关键词：

Multi objective; Particle swarm optimization; PSO; Social networks; Swarm theory; Swarm dynamics; DESIGN; CONVERGENCE; ALGORITHM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recently there has been a growing interest in evolutionary multiobjective optimization algorithms which combines two major disciplines: evolutionary computation and the theoretical frameworks of multicriteria decision making. This paper presents a comprehensive study of Multi-Objective Optimization (MOO) with Particle Swarm Optimization (PSO). Different suggestions of various researchers have been compiled to give a first-hand information of PSO based MOO. It is found that no single approach is superior. Rather, the selection of a specific method depends on the type of information that is provided in the problem, the user's preferences, the solution requirements and the availability of software.

引用

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页码：689 / +

页数：6

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