PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems

Author

Abbass, Hussein A. ; Sarker, Ruhul ; Newton, Charles

Author_Institution

Sch. of Comput. Sci., New South Wales Univ., Canberra, ACT, Australia

Volume

2

fYear

2001

fDate

2001

Firstpage

971

Abstract

The use of evolutionary algorithms (EAs) to solve problems with multiple objectives (known as multi-objective optimization problems (MOPs)) has attracted much attention. Being population based approaches, EAs offer a means to find a group of Pareto-optimal solutions in a single run. Differential evolution (DE) is an EA that was developed to handle optimization problems over continuous domains. The objective of this paper is to introduce a novel Pareto-frontier differential evolution (PDE) algorithm to solve MOPs. The solutions provided by the proposed algorithm for two standard test problems, outperform the Strength Pareto Evolutionary Algorithm, one of the state-of-the-art evolutionary algorithms for solving MOPs

Keywords

evolutionary computation; Pareto-frontier differential evolution; Strength Pareto Evolutionary Algorithm; continuous domains; evolutionary algorithms; multi-objective optimization problems; Australia; Computer science; Costs; Decision making; Drives; Educational institutions; Evolutionary computation; Humans; Mathematical programming; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2001. Proceedings of the 2001 Congress on

Conference_Location

Seoul

Print_ISBN

0-7803-6657-3

Type

conf

DOI

10.1109/CEC.2001.934295

Filename

934295