Abstract
Bio-inspired algorithms are now becoming powerful methods for solving many real-world optimization problems. In this paper, we propose a hybrid approach involving Grey Wolf optimizer (GWO) and Bat swarm optimizer (BA) for global function optimization problems. GWO is well known for its balanced exploration/exploitation behavior, while BA is known to be more exploitative due to its low exploration ability in some conditions. We use GWO exploration skills to explore the search space effectively and BA local search capabilities to refine the solution. In our hybrid algorithm, namely (GWOBA), GWO is used to explore the problem space alone and pass the best two solutions to BA to guide its local search, then BA digs deeper and find the best solution. The new proposed approach has been tested using 30 standard benchmark functions from CEC2017 benchmark suite. The performance of the hybrid algorithm has been compared to the original GWO, BA and the Whale optimization algorithm (WOA). We use a set of performance indicators to evaluate the efficiency of the method. Results over various dimensions show the superiority of the proposed algorithm.
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ElGayyar, M., Emary, E., Sweilam, N.H., Abdelazeem, M. (2018). A Hybrid Grey Wolf-Bat Algorithm for Global Optimization. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_1
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