TITLE:
Local Search-Inspired Rough Sets for Improving Multiobjective Evolutionary Algorithm
AUTHORS:
Ahmed A. EL-Sawy, Mohamed A. Hussein, El-Sayed Mohamed Zaki, Abd Allah A. Mousa
KEYWORDS:
Multiobjective Optimization, Genetic Algorithms, Rough Sets Theory
JOURNAL NAME:
Applied Mathematics,
Vol.5 No.13,
July
15,
2014
ABSTRACT: In this paper we
present a new optimization algorithm, and the proposed algorithm operates in
two phases. In the first one, multiobjective version of genetic algorithm is
used as search engine in order to generate approximate true Pareto front. This
algorithm is based on concept of co-evolution and repair algorithm for
handling nonlinear constraints. Also it maintains a finite-sized archive of
non-dominated solutions which gets iteratively updated in the presence of new
solutions based on the concept e-dominance. Then,
in the second stage, rough set theory is adopted as local search engine in
order to improve the spread of the solutions found so far. The results, provided
by the proposed algorithm for benchmark problems, are promising when compared
with exiting well-known algorithms. Also, our results suggest that our
algorithm is better applicable for solving real-world application problems.