Abstract:In order to improve the performance for solving constrained optimization problems, a novel constrained virus colony search (VCS) algorithm was proposed. Firstly, self-adaptive α-level comparison strategy was proposed to make full use of the feasible and infeasible solution at the different stages of the algorithm. Then, in order to improve the convergence rate and search ing precision for solving constrained optimization problems, the search equations based on opposition-based learning mechanism were designed for the process of virus diffusion in VCS. It was mainly used to improve the population diversity and convergence of the algorithm. Finally, the comparative experiments on thirteen CEC2006 benchmark functions showed that the proposed algorithm possessed more distinct advantages on searching accuracy, convergence rate and stability compared with αSimplex algorithm, particle swarm-assisted genetic algorithm (PSGA), crossover-based artificial bee colony (CBABC) and constrained optimization based on modified differential evolution (COMDE) algorithm. Meanwhile, compared with COMDE algorithm, the successful application in unmanned aerial vehicles (UAVs) cooperative path planning constrained problem verified the superiority of the proposed algorithm in the aspects of planning efficiency and threats avoiding.