Title

Fastpga: A Dynamic Population Sizing Approach For Solving Expensive Multiobjective Optimization Problems

Keywords

Evolutionary algorithms; Fast convergence; Multiobjective optimization; Pareto optimally

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 userspecified 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. © Springer-Verlag Berlin Heidelberg 2007.

Publication Date

1-1-2007

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

4403 LNCS

Number of Pages

141-155

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-540-70928-2_14

Socpus ID

37249055061 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/37249055061

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