Abstract
Cuckoo search algorithm (CSA) is a nature-inspired meta-heuristic based on the obligate brood parasitic behavior of cuckoo species, and it has shown promising performance in solving optimization problems. Chaotic mechanisms have been incorporated into CSA to utilize the dynamic properties of chaos, aiming to further improve its search performance. However, in the previously proposed chaotic cuckoo search algorithms (CCSA), only one chaotic map is utilized in a single search iteration which limited the exploitation ability of the search. In this study, we consider to utilize multiple chaotic maps simultaneously to perform the local search within the neighborhood of the global best solution found by CSA. To realize this, three kinds of multiple chaotic cuckoo search algorithms (MCCSA) are proposed by incorporating several chaotic maps into the chaotic local search parallelly, randomly or selectively. The performance of MCCSA is verified based on 48 widely used benchmark optimization functions. Experimental results reveal that MCCSAs generally perform better than CCSAs, and the MCCSA-P which parallelly utilizes chaotic maps performs the best among all 16 compared variants of CSAs.
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Acknowledgment
This research was partially supported by the National Natural Science Foundation of China (Grant Nos. 11572084, 11472061, and 61472284), the project of Talent Development of Taizhou University (No. QD2016061) and JSPS KAKENHI Grant Number 17K12751, 15K00332 (Japan).
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Wang, S., Song, S., Yu, Y., Xu, Z., Yachi, H., Gao, S. (2017). Multiple Chaotic Cuckoo Search Algorithm. In: Tan, Y., Takagi, H., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10385. Springer, Cham. https://doi.org/10.1007/978-3-319-61824-1_58
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DOI: https://doi.org/10.1007/978-3-319-61824-1_58
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