Abstract
Biogeography-Based Optimization (BBO) is a bio-inspired and population based optimization algorithm. This is mainly formulated to optimize functions of discrete variables. But the convergence of BBO to the optimum value is slow as it lacks in exploration ability. The proposed Accelerated Biogeography-Based Optimization (ABBO) technique is an improved version of BBO. In this paper, authors accelerated the original BBO to enhance the exploitation and exploration ability by modified mutation operator and clear duplicate operator. This significantly improves the convergence characteristics of the original algorithm. To validate the performance of ABBO, experiments have been conducted on unimodal and multimodal benchmark functions of discrete variables. The results shows excellent performance when compared with other modified BBOs and other optimization techniques like stud genetic algorithm (SGA) and ant colony optimization (ACO). The results are also analyzed by using two paired t- test.
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Lohokare, M.R., Pattnaik, S.S., Devi, S., Panigrahi, B.K., Das, S., Jadhav, D.G. (2010). Discrete Variables Function Optimization Using Accelerated Biogeography-Based Optimization. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17563-3_39
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DOI: https://doi.org/10.1007/978-3-642-17563-3_39
Publisher Name: Springer, Berlin, Heidelberg
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