Abstract
Glowworm Swarm Optimization algorithm is applied for the simultaneous capture of multiple optima of multimodal functions. In this paper, we have attempted to create a Hybrid Extremal Glowworm Swarm Optimization (HEGSO) algorithm. Aiming at the glowworm swarm optimization algorithm is easy to fall into local optima, having low accuracy, and to be unable to find the best local optima. However for solving these problems, the present algorithm has been increased the probability of choosing the best local optima. Moreover we want to use this method to have a best movement for agents in Glow worm optimization algorithm. Simulation and comparison based on several well-studied benchmarks demonstrate the effectiveness, efficiency and robustness of the proposed algorithms.
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Ghandehari, N., Miranian, E., Maddahi, M. (2013). Hybrid Extremal Optimization and Glowworm Swarm Optimization. In: Das, V. (eds) Proceedings of the Third International Conference on Trends in Information, Telecommunication and Computing. Lecture Notes in Electrical Engineering, vol 150. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3363-7_10
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DOI: https://doi.org/10.1007/978-1-4614-3363-7_10
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