Hybridized modified bat algorithm with cardinal priority ranking for solving multi area environmental economic dispatch problem

Abstract

The Bat Algorithm (BA) is a metaheuristic algorithm which is inspired by echolocation behavior of micro bats. It has successfully been used in solving many tough optimization problems in various fields. BA however has a weak diversification capability which leads to premature convergence and getting stuck in local optima when used in higher-dimensional optimization problems. This paper presents modifications to the velocity and frequency equations of BA to improve its exploitation and exploration capability. The Modified Bat Algorithm (MBA) is further hybridized using the Differential Evolution (DE) to increase its accuracy. The Hybridized Modified Bat Algorithm (HMBA) is then used to solve the Multi Area Environmental Economic Dispatch (MAEED) problem which is a multi-objective, nonlinear power system optimization problem. A weighted sum method is used to convert the multi objective function into a single objective one and optimal solutions are selected using cardinal priority ranking. HMBA is tested on a four-area system and results in lower fuel costs and lower emissions as compared to BA, MBA and Particle Swarm Optimization.