A Novel Hybrid Approach Particle Swarm Optimizer with Moth-Flame Optimizer Algorithm
Recent trend of research is to hybridize two and more algorithms to obtain superior solution in the field of optimization problems. In this context, a new method hybrid PSO (Particle Swarm Optimization)—MFO (Moth-Flame Optimizer) is exercised on some unconstraint benchmark test functions and overcurrent relay coordination optimization problems in contrast to test results on constrained/complex design problem. Hybrid PSO-MFO is combination of PSO used for exploitation phase and MFO for exploration phase in uncertain environment. Position and Velocity of particle is updated according to Moth and flame position in each iteration. Analysis of competitive results obtained from PSO-MFO validates its effectiveness compare to standard PSO and MFO algorithm.
KeywordsHeuristic Moth-flame optimizer Particle swarm optimization HPSO-MFO Overcurrent relay
- 1.J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, 1995, pp. 1942–1948.Google Scholar
- 2.Seyedali Mirjalili, “Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm,” Knowledge-Based System, vol. 89, pages 228–249, 2015.Google Scholar
- 3.Gai-Ge Wang, Amir H. Gandomi, Amir H. Alavi, Suash Deb, A hybrid PBIL-based Krill Herd Algorithm, December 2015.Google Scholar
- 4.Gai-Ge Wang, Amir H. Gandomi, Amir H. Alavi, Suash Deb, A hybrid method based on krill herd and quantum-behaved particle swarm optimization, Neural Computing and Applications, 2015, doi: 10.1007/s00521-015-1914-z.
- 5.Lihong Guo, Gai-Ge Wang, Heqi Wang, and Dinan Wang, An Effective Hybrid Firefly Algorithm with Harmony Search for Global Numerical Optimization, Hindawi Publishing Corporation The Scientific World Journal Volume 2013, Article ID 125625, 9 pages 10.1155/2013/125625.
- 6.Gai-Ge Wang, Lihong Guo, Amir Hossein Gandomi, Guo-Sheng Hao, Heqi Wang. Chaotic krill herd algorithm. Information Sciences, Vol. 274, pp. 17–34, 2014.Google Scholar
- 7.GaigeWang and Lihong Guo, A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization, Hindawi Publishing Corporation Journal of Applied Mathematics Volume 2013, Article ID 696491, 21 pages http://dx.doi.org/10.1155/2013/696491.
- 8.Gai-Ge Wang, Amir H. Gandomi, Xin-She Yang, Amir H. Alavi, A new hybrid method based on krill herd and cuckoo search for global optimization tasks. Int J of Bio-Inspired Computation, 2012, in press.Google Scholar
- 9.Gai-Ge Wang, Amir Hossein Gandomi, Amir Hossein Alavi, Guo-Sheng Hao. Hybrid krill herd algorithm with differential evolution for global numerical optimization. Neural Computing & Applications, Vol. 25, No. 2, pp. 297–308, 2014.Google Scholar
- 10.Gai-Ge Wang, Amir Hossein Gandomi, Xiangjun Zhao, HaiCheng Eric Chu. Hybridizing harmony search algorithm with cuckoo search for global numerical optimization. Soft Computing, 2014. doi: 10.1007/s00500-014-1502-7.
- 11.Gaige Wang, Lihong Guo, Hong Duan, Heqi Wang, Luo Liu, and Mingzhen Shao, Hybridizing Harmony Search with Biogeography Based Optimization for Global Numerical Optimization, Journal of Computational and Theoretical Nanoscience Vol. 10, 2312–2322, 2013.Google Scholar
- 12.A.H. Gandomi, X.S. Yang, S. Talatahari, A.H. Alavi, Metaheuristic Applications in Structures and Infrastructures, Elsevier, 2013.Google Scholar
- 13.A.H. Gandomi, A.H. Alavi, Krill Herd: a new bio-inspired optimization algorithm, Common Nonlinear Sci. Numer. Simul. 17 (12) (2012) 4831–4845.Google Scholar
- 14.Gandomi A.H. “Interior Search Algorithm (ISA): A Novel Approach for Global Optimization.” ISA Transactions, Elsevier, 53(4), 1168–1183, 2014.Google Scholar
- 15.S. S. Gokhle, Dr. V. S. Kale, “Application of the Firefly Algorithm to Optimal Overcurrent Relay Coordination”, IEEE Conference on Optimization of Electrical and Electronic equipment, Bran, 2014.Google Scholar
- 16.I.N. Trivedi, S.V. Purani, Pradeep Jangir, “Optimized over-current relay coordination using Flower Pollination Algorithm”, “Advance Computing Conference (IACC), 2015 IEEE International”, pages 72–77.Google Scholar