Papers by Author: Jeng Shyang Pan

Abstract: 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.

Abstract: Crank block steering mechanism optimization, which is a nonlinear constrained optimization problem, is important for forklift truck to get preferable steering performance. Vector particle swarm optimization (VPSO) is a swarm intelligent algorithm proposed to solve constrained optimization problems with simple concept and easy implementation. Therefore, VPSO was proposed to optimize crank block steering mechanism of forklift truck in this paper, which will be intended for minimizing the maximal error of outer wheel steering angle, maximizing the minimal transmission angle and making the transmission capability of the force better. The experimental results obtained by VPSO are compared with the results obtained by enumeration algorithm which are reliable for optimization problems. The comparison results showed that vector particle swarm optimization can get same optimal results as enumeration algorithm in much less calculation times.