A constrained multi-objective biogeography-based optimization algorithm （CMBOA） was proposed to solve robot path planning （RPP）. For RPP, the length and smoothness of path were taken as the optimization objectives...A constrained multi-objective biogeography-based optimization algorithm （CMBOA） was proposed to solve robot path planning （RPP）. For RPP, the length and smoothness of path were taken as the optimization objectives, and the distance from the obstacles was constraint. In CMBOA, a new migration operator with disturbance factor was designed and applied to the feasible population to generate many more non-dominated feasible individuals; meanwhile, some infeasible individuals nearby feasible region were recombined with the nearest feasible ones to approach the feasibility. Compared with classical multi-objective evolutionary algorithms, the current study indicates that CM- BOA has better performance for RPP.展开更多
基金Supported by the National Natural Science Foundation of Chi- na（61075113） the Excellent Young Teacher Foundation of Heilongjiang Province of China （1155G18） the Fundamental Research Funds for the Central Universities （HEUCFZl209）
文摘A constrained multi-objective biogeography-based optimization algorithm （CMBOA） was proposed to solve robot path planning （RPP）. For RPP, the length and smoothness of path were taken as the optimization objectives, and the distance from the obstacles was constraint. In CMBOA, a new migration operator with disturbance factor was designed and applied to the feasible population to generate many more non-dominated feasible individuals; meanwhile, some infeasible individuals nearby feasible region were recombined with the nearest feasible ones to approach the feasibility. Compared with classical multi-objective evolutionary algorithms, the current study indicates that CM- BOA has better performance for RPP.
