Enhancing performance of oppositional BBO using the current optimum (COOBBO) for TSP problems

Abstract

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.

Citation

Xu, Q., Wang, N. and Wang, L. (2016), "Enhancing performance of oppositional BBO using the current optimum (COOBBO) for TSP problems", International Journal of Intelligent Computing and Cybernetics, Vol. 9 No. 2, pp. 144-164. https://doi.org/10.1108/IJICC-03-2016-0015