CMCABC: Clustering and Memory-Based Chaotic Artificial Bee Colony Dynamic Optimization Algorithm

Mohsen Moradi, Samad Nejatian, Hamid Parvin and Vahideh Rezaie
Additional contact information
Mohsen Moradi: Department of Computer Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran
Samad Nejatian: #x2020;Department of Electrical Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran‡Young Researchers and Elite Club, Yasooj Branch, Islamic Azad University, Yasooj, Iran
Hamid Parvin: #xA7;Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran¶Young Researchers and Elite Club, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran
Vahideh Rezaie: #x2021;Young Researchers and Elite Club, Yasooj Branch, Islamic Azad University, Yasooj, Iran∥Department of Mathematics, Yasooj Branch, Islamic Azad University, Yasooj, Iran

International Journal of Information Technology & Decision Making (IJITDM), 2018, vol. 17, issue 04, 1007-1046

Abstract: The swarm intelligence optimization algorithms are used widely in static purposes and applications. They solve the static optimization problems successfully. However, most of the recent optimization problems in the real world have a dynamic nature. Thus, an optimization algorithm is required to solve the problems in dynamic environments as well. The dynamic optimization problems indicate the ones whose solutions change over time. The artificial bee colony algorithm is one of the swarm intelligence optimization algorithms. In this study, a clustering and memory-based chaotic artificial bee colony algorithm, denoted by CMCABC, has been proposed for solving the dynamic optimization problems. A chaotic system has a more accurate prediction for future in the real-world applications compared to a random system, because in the real-world chaotic behaviors have emerged, but random behaviors havenot been observed. In the proposed CMCABC method, explicit memory has been used to save the previous good solutions which are not very old. Maintaining diversity in the dynamic environments is one of the fundamental challenges while solving the dynamic optimization problems. Using clustering technique in the proposed method can well maintain the diversity of the problem environment. The proposed CMCABC method has been tested on the moving peaks benchmark (MPB). The MPB is a good simulator to evaluate the efficiency of the optimization algorithms in dynamic environments. The experimental results on the MPB reveal the appropriate efficiency of the proposed CMCABC method compared to the other state-of-the-art methods in solving dynamic optimization problems.

Keywords: Dynamic optimization; artificial bee colony; dynamic environments; chaos; memory; moving peaks benchmark (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0219622018500153

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