Integrating optimization technique coupling an evolutionary algorithm and local search scheme
Abd Allah A. Mousa · 2014
Evolutionary algorithms (EAs) have become widely used tools in many fields such as decision support tool, pattern recognition, engineering, and many other fields. Evolutionary algorithms are powerful computing systems to solve large-scale problems that have many local optima. However, they require high CPU times, and they are very poor in terms of convergence performance. On the other hand, local search schemes can converge quickly to these local minima and get stuck in a local optimum solution far away from the global optimal. The combination of global and local search procedures should offer the advantages of both optimization methods while offsetting their disadvantages. This paper proposes a new hybrid optimization technique that integrates a genetic algorithm with a local search strategy based on concept of co-evolution and repair algorithm for handling nonlinear constraints. The results, provided by the proposed algorithm for benchmark problems, are promising. Also, our results suggest that our algorithm is better applicable for solving real-world application problems.