Adaptive Multi-objective Particle Swarm Optimization algorithm

被引：43
|
作者：
Tripathi, P. K. [1 ]
Bandyopadhyay, Sanghamitra [1 ]
Pal, S. K. [2 ]
机构：
[1] Indian Stat Inst, Machine Intelligence Unit, 203 BT Rd, Kolkata 700108, India
[2] Indian Stat Inst, Ctr Soft Comp Res, 203 BT Rd, Kolkata 700108, India
来源：
2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS | 2007年
关键词：
D O I：
10.1109/CEC.2007.4424755
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this article we describe a novel Particle Swarm Optimization (PSO) approach to Multi-objective Optimization (MOO) called Adaptive Multi-objective Particle Swarm Optimization (AMOPSO). AMOPSO algorithm's novelty lies in its adaptive nature, that is attained by incorporating inertia and the acceleration coefficient as control variables with usual optimization variables, and evolving these through the swarming procedure. A new diversity parameter has been used to ensure sufficient diversity amongst the solutions of the non dominated front. AMOPSO has been compared with some recently developed multi-objective PSO techniques and evolutionary algorithms for nine function optimization problems, using different performance measures.
引用
收藏
页码：2281 / +
页数：4
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