conference paper
Particle swarm with extended memory for multiobjective optimization
Xiaohui Hua b, Russell C. Eberhartb, Yuhui Shic
- aDepartment of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
- bDepartment of Electrical and Computer Engineering, Purdue School of Engineering and Technology, Indianapolis, IN, USA
- cEDS Embedded Systems Group, Kokomo, IN, USA
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Abstract
This paper presents a modified dynamic neighborhood particle swarm optimization (DNPSO) algorithm for multiobjective optimization problems. PSO is modified by using a dynamic neighborhood strategy, new particle memory updating, and one-dimension optimization to deal with multiple objectives. An extended memory is introduced to store global Pareto optimal solutions to reduce computation time. Several benchmark cases were tested and the results show that the modified DNPSO is much more efficient than the original DNPSO and other multiobjective optimization techniques.