Purpose-The purpose of this paper is to examine and compare the entire impact of various execution skills of oppositional biogeography-based optimization using the current optimum(COOBBO)algorithm.Design/methodology/a...Purpose-The purpose of this paper is to examine and compare the entire impact of various execution skills of oppositional biogeography-based optimization using the current optimum(COOBBO)algorithm.Design/methodology/approach-The improvement measures tested in this paper include different initialization approaches,crossover approaches,local optimization approaches,and greedy approaches.Eight well-known traveling salesman problems(TSP)are employed for performance verification.Four comparison criteria are recoded and compared to analyze the contribution of each modified method.Findings-Experiment results illustrate that the combination model of“25 nearest-neighbor algorithm initialization+inver-over crossover+2-opt+all greedy”may be the best choice of all when considering both the overall algorithm performance and computation overhead.Originality/value-When solving TSP with varying scales,these modified methods can enhance the performance and efficiency of COOBBO algorithm in different degrees.And an appropriate combination model may make the fullest possible contribution.展开更多
基金This work was supported in part by the National Natural Science Foundation of China(Nos.61375089 and 61305083).
文摘Purpose-The purpose of this paper is to examine and compare the entire impact of various execution skills of oppositional biogeography-based optimization using the current optimum(COOBBO)algorithm.Design/methodology/approach-The improvement measures tested in this paper include different initialization approaches,crossover approaches,local optimization approaches,and greedy approaches.Eight well-known traveling salesman problems(TSP)are employed for performance verification.Four comparison criteria are recoded and compared to analyze the contribution of each modified method.Findings-Experiment results illustrate that the combination model of“25 nearest-neighbor algorithm initialization+inver-over crossover+2-opt+all greedy”may be the best choice of all when considering both the overall algorithm performance and computation overhead.Originality/value-When solving TSP with varying scales,these modified methods can enhance the performance and efficiency of COOBBO algorithm in different degrees.And an appropriate combination model may make the fullest possible contribution.
