HANDLING DYNAMIC MULTIOBJECTIVE PROBLEMS WITH PARTICLE SWARM OPTIMIZATION

被引：0
|
作者：
Diaz Manriquez, Alan [1 ]
Toscano Pulido, Gregorio [1 ]
Ramirez Torres, Jose Gabriel [1 ]
机构：
[1] CINVESTAV Tamaulipas, Lab Tecnol Informac, Km 6 Carretera Cd Victoria Monterrey, Cd Victoria 87267, Tamaulipas, Mexico
来源：
ICAART 2010: PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE, VOL 1: ARTIFICIAL INTELLIGENCE | 2010年
关键词：
Dynamic multi-objective optimization; Particle swarm optimization; Multi-objective optimization;
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：
In this paper the hyperplane distribution and Pareto dominance were incorporated into a particle swarm optimization algorithm in order to allow it to handle dynamic multiobjective problems. When a change in a dynamic multiobjectve function is detected, the proposed algorithm reinitializes (in different ways) the PSO's velocity parameter and the archive where the non-dominated solutions are beeing stored such that the algorithm can follow the dynamic Pareto front. The proposed approach is validated using two dynamic multiobjective test functions and an standard metric taken from the specialized literature. Results indicate that the proposed approach is highly competitive which can be considered as a viable alternative in order to solve dynamic multiobjective optimization problems.
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页码：337 / 342
页数：6
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