Jitkongchuen D (2016) A hybrid differential evolution with grey wolf optimizer for continuous global optimization. Guo Z, Liu R, Gong C et al (2017) Study on Improvement of grey wolf algorithm. Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. In: Proceedings of the 1999 congress on evolutionary computation, CEC 99, vol 3. Expert Syst Appl 81:309–320Ĭlerc M (2002) The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. J Mol Liq 261(1):431–438Ĭhen Z, Zhou S, Luo J (2017) A robust ant colony optimization for continuous functions. The simulation results show that the proposed algorithm can better search global optimal solution and better robustness than other algorithm.īian XQ, Zhang L, Du ZM et al (2018) Prediction of sulfur solubility in supercritical sour gases using grey wolf optimizer-based support vector machine. To verify the performance of this algorithm, the proposed method is tested on 18 benchmark functions and compared with some other improved algorithms. This method preserves the best position information of the individual and avoids the algorithm falling into a local optimum.
#Alpha wolf solver update
At the same time, the idea of PSO is introduced, which utilize the best value of the individual and the best value of the wolf pack to update the position information of each grey wolf.
And the nonlinear control parameter is used to balance the global search and local search ability of the algorithm and improve the convergence speed of the algorithm. In this new algorithm, the Tent chaotic sequence is used to initiate the individuals’ position, which can increase the diversity of the wolf pack. So this paper proposes a grey wolf optimization algorithm combined with particle swarm optimization (PSO_GWO). The existing grey wolf optimization algorithm has some disadvantages, such as slow convergence speed, low precision and so on.
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