A complex-valued encoding satin bowerbird optimization algorithm for global optimization
- 17 Downloads
The real-valued satin bowerbird optimization (SBO) is a novel metaheuristic bio-inspired algorithm which imitates the ‘male-attracts-the-female for breeding’ principle of the specialized stick structure mechanism of satin birds. SBO has achieved success in congestion management, accurate software development effort estimation. In this paper, to enhance the SBO algorithm global exploration ability, a complex-valued encoding satin bowerbird optimization algorithm (CSBO) is proposed. We use complex-valued encoding enhance the diversity of the population, and the global exploration ability of the SBO algorithm. The proposed CSBO optimization algorithm is compared to SBO and other state-of-art optimization algorithms using ten benchmark functions. Simulation results show that the proposed CSBO can significantly improve the convergence accuracy and convergence speed of the original algorithm.
KeywordsComplex-valued encoding Satin bowerbird optimization Benchmark functions Metaheuristic algorithm
This work is supported by National Science Foundation of China under Grants No. 61563008, and by Project of Guangxi Natural Science Foundation under Grant No. 2018GXNSFAA138146.
- Abdel-Baset M, Wu H, Zhou Y (2017) A complex encoding flower pollination algorithm for constrained engineering optimisation problems. Int J Math Model Numer Optim 8(2):108–126Google Scholar
- Huang DS (1996) Systematic theory of neural networks for pattern recognition (in Chinese). Publishing House of Electronic Industry of China, BeijingGoogle Scholar
- Kennedy J (2011) Particle swarm optimization. Encyclopedia of machine learning. Springer, New York, pp 760–766Google Scholar
- Klein CE, dos Santos Coelho L (2018) Meerkats-inspired algorithm for global optimization problems. ESANN 2018 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), 25–27 April 2018Google Scholar
- Klein CE, Mariani VC, dos Santos Coelho L (2018) Cheetah based optimization algorithm: a novel swarm intelligence paradigm. ESANN 2018 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), 25–27 April 2018Google Scholar
- Pierezan J, Dos Santos Coelho L (2018) Coyote optimization algorithm: a new metaheuristic for global optimization problems. IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, Brazil, 8–13 July 2018Google Scholar
- Yang X-S, Deb S (2009) Cuckoo search via Lévy flights. Nature & biologically inspired computing, 2009. NaBIC 2009. World Congress on. IEEEGoogle Scholar