A Combined Genetic Algorithms-local Search Engine (GAs–LCE) in Constrained Nonlinear Programming

Evolutionary optimization provides robust and efficient techniques for solving complex real-world problems. The aim of this paper is to present an enhanced evolutionary algorithm for solving constraint nonlinear programming problems NLPPs, which based on concept of co-evolution and repair algorithm for handling nonlinear constraints. Our proposed approach is made of two phases, firstly, phase I is a classical genetic algorithm, which based on the ideas of repair strategy and co-evolution. Secondly in phase II, Based on the k-means cluster algorithm, the search space is shrunk after phase I to the generated rectangular-atom with highly rate and concentrating the optimal solution region, so local search techniques will implemented in order to get more accurate optimal solution. Finally, the results of various experimental studies using a suite of benchmark functions have demonstrated the superiority of the proposed algorithm to finding the global optimal solution for constraint nonlinear programming problems.