Parallel Bat Algorithm Applied to the Economic Load Dispatch Problem

Abstract

In this paper, a novel optimization algorithm based on parallel version of Bat Algorithm (PBA) with communication strategy is proposed to solve the numerical optimization problems and the economic load dispatch problem (ELD). The aim of the parallel bat algorithm with communication strategies is to correlate individuals in swarm and to share the computation load over few processors. Based on the original structure of the Bat Algorithm (BA), the bat populations are split into several independent groups. In addition, the communication strategy provides the information flow for the bats to communicate in different groups. In the experiment, a set of benchmark functions and the ELD are used to test the behavior of convergence, the accuracy, and the speed of the PBA method. According to the experimental results, this novel method with communicational strategy increases the accuracy of the BA on finding the better solution. Compared with the genetic algorithm (GA) method and the particle swarm optimization (PSO) method. The experimental results are shown that the proposed PBA method can provide the higher efficiency and accuracy.