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

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...

[1]  Dan Simon,et al.  Probabilistic properties of fitness-based quasi-reflection in evolutionary algorithms , 2015, Comput. Oper. Res..

[2]  Lifang Xu,et al.  Biogeography migration algorithm for traveling salesman problem , 2011, Int. J. Intell. Comput. Cybern..

[3]  Dan Simon,et al.  Biogeography-Based Optimization for Large Scale Combinatorial Problems , 2013 .

[4]  Wenyin Gong,et al.  DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization , 2010, Soft Comput..

[5]  Dan Simon,et al.  A Probabilistic Analysis of a Simplified Biogeography-Based Optimization Algorithm , 2011, Evolutionary Computation.

[6]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[7]  Shen Lin.  Computer solutions of the traveling salesman problem , 1965 .

[8]  Zheng Wang,et al.  Biogeography-Based Optimization for the Traveling Salesman Problems , 2010, 2010 Third International Joint Conference on Computational Science and Optimization.

[9]  Valeria V. Krzhizhanovskaya,et al.  How to Speed up Optimization? Opposite-center Learning and Its Application to Differential Evolution , 2015, ICCS.

[10]  Jehad I. Ababneh.  Greedy particle swarm and biogeography-based optimization algorithm , 2015, Int. J. Intell. Comput. Cybern..

[11]  Cai Zhi-hua,et al.  Research on a novel biogeography-based optimization algorithm based on evolutionary programming , 2010 .

[12]  Zbigniew Michalewicz,et al.  Inver-over Operator for the TSP , 1998, PPSN.

[13]  G. Croes.  A Method for Solving Traveling-Salesman Problems , 1958 .

[14]  Abraham P. Punnen,et al.  The traveling salesman problem and its variations , 2007 .

[15]  Longquan Yong,et al.  Biogeography-based optimization with improved migration operator and self-adaptive clear duplicate operator , 2014, Applied Intelligence.

[16]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[17]  Hajime Kita,et al.  A genetic solution for the traveling salesman problem by means of a thermodynamical selection rule , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[18]  Dawei Du,et al.  BIOGEOGRAPHY-BASED OPTIMIZATION FOR COMBINATORIAL PROBLEMS AND COMPLEX SYSTEMS , 2014 .

[19]  Lei Wang,et al.  A novel oppositional biogeography-based optimization for combinatorial problems , 2014, 2014 10th International Conference on Natural Computation (ICNC).

[20]  Dan Simon,et al.  Mathematical and Experimental Analyses of Oppositional Algorithms , 2014, IEEE Transactions on Cybernetics.

[21]  Tiago Salvador,et al.  The Traveling Salesman Problem and the Gnedenko Theorem , 2014 .

[22]  Gregory Gutin,et al.  Traveling salesman should not be greedy: domination analysis of greedy-type heuristics for the TSP , 2001, Discret. Appl. Math..

[23]  Wang Na.  Opposition-Based Differential Evolution Using the Current Optimum for Function Optimization , 2011 .

[24]  Isao Ono,et al.  High-Order Sequence Entropies for Measuring Population Diversity in the Traveling Salesman Problem , 2013, EvoCOP.

[25]  Lifang Xu,et al.  Biogeography based optimization for Traveling Salesman Problem , 2010, 2010 Sixth International Conference on Natural Computation.

[26]  Dan Simon,et al.  Oppositional biogeography-based optimization for combinatorial problems , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[27]  Mohammad Bagher Ahmadi,et al.  Opposition versus randomness in binary spaces , 2015, Appl. Soft Comput..

[28]  Mohammad Bagher Ahmadi,et al.  An opposition-based algorithm for function optimization , 2015, Eng. Appl. Artif. Intell..

[29]  B. R. Fox,et al.  Genetic Operators for Sequencing Problems , 1990, FOGA.

[30]  D. J. Smith,et al.  A Study of Permutation Crossover Operators on the Traveling Salesman Problem , 1987, ICGA.

[31]  Hamid R. Tizhoosh,et al.  Opposition-Based Learning: A New Scheme for Machine Intelligence , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[32]  Gerhard Reinelt,et al.  TSPLIB - A Traveling Salesman Problem Library , 1991, INFORMS J. Comput..

[33]  Dan Simon,et al.  Blended biogeography-based optimization for constrained optimization , 2011, Eng. Appl. Artif. Intell..

[34]  Li Zhao,et al.  A review of opposition-based learning from 2005 to 2012 , 2014, Eng. Appl. Artif. Intell..

[35]  Dan Simon,et al.  Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms , 2011, Inf. Sci..

[36]  Marjan Mernik,et al.  Exploration and exploitation in evolutionary algorithms: A survey , 2013, CSUR.