A Novel Improved Bird Swarm Algorithm for Solving Bound Constrained Optimization Problems
WANG Yuhe, WAN Zhongping†, PENG ZhenhuaSchool of Mathematics and Statistics, Wuhan University, Wuhan 430072, Hubei, China
Bird swarm algorithm (BSA), a novel bio-inspired algorithm, has good performance in solving numerical optimization problems. In this paper, a new improved bird swarm algorithm is conducted to solve unconstrained optimization problems. To enhance the performance of BSA, handling boundary constraints are applied to fix the candidate solutions that are out of boundary or on the boundary in iterations, which can boost the diversity of the swarm to avoid the premature problem. On the other hand, we accelerate the foraging behavior by adjusting the cognitive and social components the sin cosine coefficients. Simulation results and comparison based on sixty benchmark functions demonstrate that the improved BSA has superior performance over the BSA in terms of almost all functions.
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