Gbest inspired Biogeography Based Optimization algorithm

Biogeography Based Optimization (BBO) algorithm is a population based evolutionary optimization algorithm modeled on the theory of biogeography. Like other evolutionary algorithms, BBO also suffers from the problem of slow convergence. To improve the convergence property of the algorithm, global best solution inspired search strategy is incorporated with BBO. The modified strategy is named as global-best inspired biogeography based optimization (GBBO) algorithm. In the proposed work, the search process is guided by the information incorporating from global best (gbest) solution and one random solution, leads to improve the exploitation capability. The developed algorithm is compared with BBO and three other algorithms, namely Gravitational Search Algorithm (GSA), Shuffled Frog Leaping Algorithm (SFLA) and Differential Evolution (DE) Algorithm with the experiments over 12 test problems. Obtained results confirm the competitive performance of the proposed algorithm.

[1]  Hui Li,et al.  A real-coded biogeography-based optimization with mutation , 2010, Appl. Math. Comput..

[2]  Dan Simon,et al.  Markov Models for Biogeography-Based Optimization , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  Sam Kwong,et al.  Genetic algorithms and their applications , 1996, IEEE Signal Process. Mag..

[4]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[5]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[6]  M.M.A. Salama,et al.  Opposition-Based Differential Evolution , 2008, IEEE Transactions on Evolutionary Computation.

[7]  Dan Simon,et al.  Population distributions in biogeography-based optimization algorithms with elitism , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[8]  Harish Sharma,et al.  Memetic search in artificial bee colony algorithm , 2013, Soft Computing.

[9]  Xiangtao Li,et al.  A perturb biogeography based optimization with mutation for global numerical optimization , 2011, Appl. Math. Comput..

[10]  Harish Sharma,et al.  Lévy flight artificial bee colony algorithm , 2016, Int. J. Syst. Sci..

[11]  Harish Sharma,et al.  Gbest guided differential evolution , 2014, 2014 9th International Conference on Industrial and Information Systems (ICIIS).

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

[13]  Sam Kwong,et al.  Gbest-guided artificial bee colony algorithm for numerical function optimization , 2010, Appl. Math. Comput..

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

[15]  Muzaffar Eusuff,et al.  Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization , 2006 .

[16]  Harish Sharma,et al.  Self-adaptive artificial bee colony , 2014 .