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This paper presents a new powerful nature-inspired method named Virus Colony Search (VCS). VCS simulates diffusion and infection strategies for the host cells adopted by virus to survive and propagate in the cell environment. With the strategies, the individual in the new algorithm explores and exploits the search space more efficiently. To verify the performance of our VCS, both the unconstrained classic and CEC2014 modern benchmark functions, and constrained engineering design optimization problems are employed. The experimental results, considering both convergence and accuracy simultaneously, demonstrate the effectiveness of VCS for global numerical and engineering optimization problems. [ABSTRACT FROM AUTHOR]

Highlights: [•] A new meta-heuristic called Grey Wolf Optimizer inspired by grey wolves is proposed. [•] The GWO algorithm is benchmarked on 29 well-known test functions. [•] The results on the unimodal functions show the superior exploitation of GWO. [•] The exploration ability of GWO is confirmed by the results on multimodal functions. [•] The results on semi-real and real problems confirm the performance of GWO in practice. [ABSTRACT FROM AUTHOR]