Bat Algorithm Inspired Algorithm for Solving Numerical Optimization Problems
Inspired by Bat Algorithm, a novel algorithm, which is called Evolved Bat Algorithm (EBA), for solving the numerical optimization problem is proposed based on the framework of the original bat algorithm. By reanalyzing the behavior of bats and considering the general characteristics of whole species of bat, we redefine the corresponding operation to the bats behaviors. EBA is a new method in the branch of swarm intelligence for solving numerical optimization problems. In order to analyze the improvement on the accuracy of finding the near best solution and the reduction in the computational cost, three well-known and commonly used test functions in the field of swarm intelligence for testing the accuracy and the performance of the algorithm, are used in the experiments. The experimental results indicate that our proposed method improves at least 99.42％ on the accuracy of finding the near best solution and reduces 6.07％ in average, simultaneously, on the computational time than the original bat algorithm.
Evolved Bat Algorithm Bat Algorithm Swarm Intelligence Optimization Bio-inspired Computing
Pei-Wei Tsai Jeng-Shyang Pan Bin-Yih Liao Ming-Jer Tsai Vaci·Istanda
Department of Electronic Engineering, National Kaohsiung University of Applied Sciences,Kaohsiung City, 80778, Taiwan (R.O.C.)
国际会议
大连
英文
134-137
2011-10-19（万方平台首次上网日期，不代表论文的发表时间）