by Ruhul Sarker, Hussein A. Abbass, Samin Karim
http://www.lania.mx/~ccoello/EMOO/sarker01a.pdf.gz
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Abstract:
The use of evolutionary algorithms (EAs) to solve problems with multiple objectives (known as Multiobjective Optimization Problems (MOPs)) has attracted much attention recently. Population based approaches, such as EAs, offer a means to find a group of pareto-optimal solutions in a single run. However, most studies are undertaken on unconstrained MOPs. Recently, we developed the Pareto–frontier Differential Evolution (PDE) algorithm for unconstrained MOPs. The objective of this paper is to introduce a modification to PDE for handling constraints. The solutions, provided by the proposed algorithm for three test problems, are promising when compared with an existing well-known algorithm. 1
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