A Novel Locally and Globally Tuned Biogeography-based Optimization Algorithm

Abstract

Biogeography-Based Optimization (BBO) is a nature-inspired meta-heuristic algorithm, which uses the idea of the migration strategy of animals or other species for solving complex optimization problems. In BBO, adaptation of the intensification and diversification for solving complex optimization problem is a challenging task. Migration and mutation operators are two imperative features that largely affect the performance and computational efficiency in BBO, which maintains both exploration and exploitation of existing approaches. In this paper, an innovative migration operator has been introduced in BBO, which inherit the features from a nearest neighbor of the local best individual to be migrated to the globally best individual of the pool and we name it as “Locally and Globally Tuned BBO (LGBBO)”. We have carried out an extensive numerical evaluation on ten benchmark functions to measure the efficiency of the proposed method. The experimental study confirms that LGBBO is better than canonical and blended BBO in terms of accuracy and convergence time to locate the global optimal solution.