Abstract
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.
Published Version
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