Title :
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
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;
Conference_Titel :
Evolutionary Computation, 2001. Proceedings of the 2001 Congress on
Conference_Location :
Seoul
Print_ISBN :
0-7803-6657-3
DOI :
10.1109/CEC.2001.934295
