conference paper

Multiobjective optimization using dynamic neighborhood particle swarm optimization

^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

IEEE Congress on Evolutionary Computation (CEC)2003-06-25 695 citations9 references

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Abstract

This paper presents a particle swarm optimization (PSO) 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. Several benchmark cases were tested and showed that PSO could efficiently find multiple Pareto optimal solutions.

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Multiobjective optimization using dynamic neighborhood particle swarm optimization

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