Dynamic Inertia Weight Binary Bat Algorithm with Neighborhood Search

• Published in: Computational Intelligence and Neuroscience Vol. 2017; no. 2017; pp. 1 - 15
• Main Authors: Huang, Xingwang, Han, Rui, Zeng, Xuewen
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Limiteds 01.01.2017; Hindawi Publishing Corporation; Hindawi; John Wiley & Sons, Inc
• Subjects: Algorithms; Animals; Bats; Biomimetics; Chiroptera - physiology; Comparative analysis; Computer Simulation; Datasets; Echolocation; Heuristic programming; Heuristics; Literature reviews; Mathematical optimization; Predatory Behavior; R&D; Research & development
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2017/3235720

Binary bat algorithm (BBA) is a binary version of the bat algorithm (BA). It has been proven that BBA is competitive compared to other binary heuristic algorithms. Since the update processes of velocity in the algorithm are consistent with BA, in some cases, this algorithm also faces the premature convergence problem. This paper proposes an improved binary bat algorithm (IBBA) to solve this problem. To evaluate the performance of IBBA, standard benchmark functions and zero-one knapsack problems have been employed. The numeric results obtained by benchmark functions experiment prove that the proposed approach greatly outperforms the original BBA and binary particle swarm optimization (BPSO). Compared with several other heuristic algorithms on zero-one knapsack problems, it also verifies that the proposed algorithm is more able to avoid local minima.