FastPGA: a dynamic population sizing approach for solving expensive multiobjective optimization problems

Authors:
Hamidreza Eskandari;Christopher D. Geiger;Gary B. Lamont
Affiliations:
Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL;Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL;Department of Electrical and Computer Engineering, Graduate School of Engineering and Management, Air Force Institute of Technology, Dayton, OH
Venue:
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Year:
2007

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Abstract

We present a new multiobjective evolutionary algorithm (MOEA), called fast Pareto genetic algorithm (FastPGA). FastPGA uses a new fitness assignment and ranking strategy for the simultaneous optimization of multiple objectives where each solution evaluation is computationally- and/or financially-expensive. This is often the case when there are time or resource constraints involved in finding a solution. A population regulation operator is introduced to dynamically adapt the population size as needed up to a user-specified maximum population size. Computational results for a number of well-known test problems indicate that FastPGA is a promising approach. FastPGA outperforms the improved nondominated sorting genetic algorithm (NSGA-II) within a relatively small number of solution evaluations.