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Particle swarm inspired evolutionary algorithm (PS-EA) for multiobjective optimization problems

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2 Author(s)
Dipti Srinivasan ; Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore ; T. H. Seow

We describe particle swarm inspired evolutionary algorithm (PS-EA), which is a hybridized evolutionary algorithm (EA) combining the concepts of EA and particle swarm theory. PS-EA is developed in aim to extend PSO algorithm to effectively search in multiconstrained solution spaces, due to the constraints rigidly imposed by the PSO equations. To overcome the constraints, PS-EA replaces the PSO equations completely with a self-updating mechanism (SUM), which emulates the workings of the equations. A comparison is performed between PS-EA with genetic algorithm (GA) and PSO and it is found that PS-EA provides an advantage over typical GA and PSO for complex multimodal functions like Rosenbrock, Schwefel and Rastrigrin functions. An application of PS-EA to minimize the classic Fonseca 2-objective functions is also described to illustrate the feasibility of PS-EA as a multiobjective search algorithm.

Published in:

Evolutionary Computation, 2003. CEC '03. The 2003 Congress on  (Volume:4 )

Date of Conference:

8-12 Dec. 2003