A bee colony optimization-based approach for binary optimization

Abstract

The bee colony optimization (BCO) algorithm, one of the swarm intelligence algorithms, is a population based iterative search algorithm. Being inspired by collective bee intelligence, BCO has been proposed for solving discrete optimization problems such as travelling salesman problem. The BCO uses constructive approach for creating a feasible solution for the discrete optimization problems but in this study, we used the solution improvement technique due to nature of the uncapacitated facility location problem (UFLP). In the proposed method named as binBCO, the feasible solutions are generated for the artificial bees in hive of BCO and these solutions are tried to improve by utilizing interaction in the hive. At the end of the each iteration, some of the bees leave self-solutions and the leaving process depends on the loyalty of the bee to the self-solution. After a bee leaves self-solution, a random feasible solution is generated and assigned to this bee. In order to show the performance of binBCO, we examined it on well-known UFLPs, and the experimental studies show that the proposed method produces promising results.