Hybrid seeker optimization algorithm for global optimization

Swarm intelligence algorithms have been succesfully applied to hard optimization problems. Seeker optimization algorithm is one of the latest members of that class of metaheuristics and it has not yet been thorougly researched. Since the early versions of this algorithm were less succesful with multimodal functions, we propose in this paper hybridization of the seeker optimization algorithm with the well known artificial bee colony (ABC) algorithm. At certain stages we modify seeker's position by search formulas from the ABC algorithm and also modify the inter-subpopulation learning phase by using the binomial crossover operator. Our proposed algorithm was tested on the complete set of 23 well-known benchmark functions. Comparisons show that our proposed algorithm outperforms six state-of-the-art algorithms in terms of the quality of the resulting solutions as well as robustenss on most of the test functions.