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
收藏
页码：689 / +
页数：6
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