Virus control optimizer based on the prevention and control mechanism of COVID-19 for engineering optimization problems

Abstract

Metaheuristic algorithms have been widely used in many application fields and have achieved some good results, but there is still a strong demand for these algorithms in different engineering application fields. In this paper, a novel metaheuristics algorithm named Virus Control Optimizer (VCO) is proposed to solve engineering optimization problems. The algorithm is inspired by the prevention and control mechanism of COVID-19, which designed an effective solution space partition strategy and a multi-subpopulation collaborative search framework. This framework will maintain the whole population’s diversity while making an accurate search on the local optimal region. Compared to other existing algorithms, VCO has a unique search mechanism and can achieve better convergence efficiency, especially in engineering optimization problems. The search capabilities of VCO are assessed by testing on 12 classical benchmark functions, 29 unconstrained benchmark functions obtained from CEC 2017, 4 constrained engineering optimization problems, 5 real-world constrained problems from CEC 2020 and 3 simulation test of multi-UAVs path planning problems. In addition, VCO is compared with other typical and state-of-the-art heuristic algorithms, our results reveal that VCO can obtain better solutions than the comparison algorithms.