Bat algorithm with global convergence for solving large-scale optimization problem

Abstract

To solve large-scale optimization problems(OP),this paper constructed a bat algorithm with global convergence.In the algorithm,each bat was just an alternative solution of OP,used the principle of orthogonal Latin squares to construct initial positions of bat group so as to cover search space with balance dispersion and neat comparability,used the following behavior,autonomous behavior,averting-danger behavior and conformability behavior of bats to construct space positions trasnsfering strategies;used the loudness and rate of plus emission to ensure the bat groups keep either to stay unchanged or to transfer toward better positions,but never to transfer worse positions.During evoluation process,a bat's transferring from one position to another realized the search for the optimium solution.The result shows that the reducible stochastic matrix stability theorem can be applied to prove the global convergence of the algorithm.The case studys show that the algorithm has advantages of good suitability for different types of OPs and high convergence speed when applied to solve large-scale optimization problems.