Abstract
In this paper, the authors propose a new evolutionary optimization technique i.e. modified biogeography-based optimization (MBBO). This technique is an improved version of BBO with each solution is directly encoded by floating point. BBO is a new bio-inspired and population based optimization algorithm for global optimization. The exploitation ability of BBO method is good but it lacks in exploration ability. The convergence of original BBO to the optimum value is slow. MBBO enhances computational throughput and global search capability for optimization of multimodal and high dimensional functions. In MBBO, the original BBO is modified by applying the concept of Mutation as extended migration in mutation scheme. Due to this concept of sharing of the information from best solution, increase the diversity among the population limited to the near feasible solution. This makes faster convergence of the algorithm. The proposed technique is validated on thirteen benchmark functions. The results of MBBO is compared with BBO and different existing methods, which includes multimodal and high dimensional functions. MBBO is used as soft computing tool for calculating resonant frequency of circular microstrip antennas.
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