Authors: Adil Amirjanov; Konstantin Sobolev
Addresses: Department of Computer Engineering, Near East University, N. Cyprus, Nicosia, Cyprus ' Department of Civil and Environmental Engineering, University of Wisconsin-Milwaukee, Milwaukee, USA
Abstract: The performance of a sequential changing range genetic algorithm (SCRGA) is described. This algorithm enables the transformation of an unconstrained numerical optimisation problem to a constrained problem by implementing constraints which convert the area near previously found optima to an infeasible region. This SCRGA feature is used for locating all optima of unconstrained and constrained numerical optimisation problems. Several test cases, related to unconstrained and constrained numerical optimisation problems, demonstrate the ability of this approach to reduce the computational costs, significantly improving success rates, accurately and precisely locating all optimal solutions.
Keywords: nonlinear programming; genetic algorithms; unconstrained numerical optimisation; constrained numerical optimisation; computational cost; multimodal optimisation.
International Journal of Bio-Inspired Computation, 2015 Vol.7 No.4, pp.209 - 221
Available online: 11 Aug 2015Full-text access for editors Access for subscribers Purchase this article Comment on this article