An enhanced genetic algorithm for numerical functions optimization

Optimization is the process of making something as good or effective as possible. Optimization problems are used over many fields such as economics, science, industry and engineering. The growing use of optimization makes it essential for researchers in every branch of science and technology. To solve optimization problems many algorithms have been introduced, while achieving a higher quality of results in terms of accuracy and robustness is still an issue. Metaheuristics are widely recognized as efficient approaches for many hard optimization problems. In this study, to achieve a higher quality of results in numerical functions optimization, two new operators named N-digit lock search (NLS) and Two-Math crossover are introduced to enhance the genetic algorithm (GA) as a widely used metaheuristic. The NLS operator is inspired by the N-digit combination lock pattern and enhances the exploitative behavior of the GA by calibrating the current best solution and the relatively new Two-Math crossover operator combines both two-point and arithmetic crossover techniques to guide the overall search process better. The proposed enhanced genetic algorithm (EGA) is tested over 33 benchmark mathematical functions and the results are compared to some population-based, particle swarm optimization (PSO2011) and artificial bee colony (ABC) algorithms, and single-solution based, simulated annealing (SA), pattern search (PS), and vortex search (VS). A problem-based test is performed to compare the performance of the algorithms, which results shows the proposed EGA outperforms all other algorithms, SA, PS, VS, PSO2011 and ABC. In addition, it surprisingly finds the global best points for almost all 33 test functions with a constant value for 2 out of 3 EGA operators.