Participative Biogeography-Based Optimization
الموضوعات :Abbas Salehi 1 , Behrooz Masoumi 2
1 - Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 - Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
الکلمات المفتاحية: evolutionary algorithms, Meta-heuristics, Biogeography Based Optimization, Migration operator,
ملخص المقالة :
Biogeography-Based Optimization (BBO) has recently gained interest of researchers due to its simplicity in implementation, efficiency and existence of very few parameters. The BBO algorithm is a new type of optimization technique based on biogeography concept. This population-based algorithm uses the idea of the migration strategy of animals or other species for solving optimization problems. the original BBO sometimes has not resulted in desirable outcomes. Migration, mutation and elitism are three Principal operators in BBO. The migration operator plays an important role in sharing information among candidate habitats. This paper proposes a novel migration operator in Original BBO. The proposed BBO is named as PBBO and new migration operator is examined over 12 test problems. Also, results are compared with original BBO and others Meta-heuristic algorithms. Results show that PBBO outperforms over basic BBO and other considered variants of BBO.
Al-Roomi, A.R. & El-Hawary, M.E. (2016) Metropolis biogeography- based optimization, Information Sciences, 0020-0255/© Elsevier, http://dx.doi.org/10.1016/j.ins.2016.03.051
Ardalan, Z., S. Karimi, Poursabzi, O. & Naderi, B.(2015) A novel imperialist competitive algorithm for generalized traveling salesman problems, in Applied Soft Computing, http://dx.doi.org/10.1016/j.asoc.2014.08.033
Bansal, J.C. (2016) Modified Blended Migration and Polynomial Mutation in Biogeography-Based Optimization, in: J.H. Kim and Z.W. Greem(eds.), Harmony Search Algorithm, Advances in Intelligent Systems and Computing 382, Springer-Verlag Berlin Heidelberg,, DOI: 10.1007/978-3-662-47926-1_21
Chen, X. Tianfield, H. Du W. & G. Liu (2016) Biogeography-based optimization with covariance matrix based migration, in: Applied Soft Computing 45, 71–85, http://dx.doi.org/10.1016/j.asoc.2016.04.022
Farswan, P. Bansal, J.C. & Deep, K. (2016) A Modified Biogeography Based Optimization, in: J.H. Kim and Z.W. Geem (eds.), Harmony Search Algorithm, Advances in: Intelligent Systems and Computing 382, Springer- Verlag Berlin Heidelberg, , DOI: 10.1007/978-3-662-47926-1_22
Feng, Q., Zhang, S.L.J., Yang G. & L. Yong, (2014) Biogeography-based optimization with improved migrationoperator and self-adaptive clear duplicate operator, Appl Intell, DOI: 10.1007/s10489-014-0527-z
Gong, W. Cai, Z. & Ling, C.X. (2010) DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization, in: Soft Comput, Springer-Verlag , DOI: 10.1007/s00500-010-0591-1
Guo, W. and et al . (2016) Novel migration operators of biogeography-based optimization and Markov analysis, in: Soft Comput, © Springer-Verlag Berlin Heidelberg , DOI 10.1007/s00500-016-2209-8
Mo, H. & Xu, L. (2010) Biogeography Migration Algorithm for Traveling Salesman Problem, in: Advances in Swarm Intelligence, 6145 , Springer-Verlag Berlin Heidelberg, 2010
Simon, D. (2008) Biogeography-based optimization, IEEE Transactions on Evolutionary Computation. 12 (6) 702–713, DOI:10.1109/TEVC.20 08.919004
Suganthan, P.N. and et al. (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization, Kan GAL report, 2005005 (2005)
Thomas, G., Simon, D. & Michelini, J. (2015) Biogeography-based optimization of a variable camshaft timing system, Engineering Applications of Artificial Intelligence 45, 376–387, 0952-1976/& 2015 Elsevier Ltd. http://dx.doi.org/10.1016/j.engappai.2015.07.015
Zheng, Y.J. and et al. (2014) Localized biogeography-based optimization, in: Soft Comput, Springer-Verlag Berlin Heidelberg , DOI: 10.1007/s00500-013-1209-1
Zhong, Y., Lin, J. Wang L. & Zhang, H. (2017) Hybrid Discrete Artificial Bee Colony Algorithm with Threshold Acceptance Criterion for Traveling Salesman Problem, in: Information Sciences, DOI: 10.1016/j.ins.2017.08.067
