An artificial algae algorithm for solving binary optimization problems

This paper focuses on modification of basic artificial algae algorithm (AAA) for solving binary optimization problems by using a new solution update rule because the agents in AAA work on continuous solution space. The candidate solution generation process of algorithm in the basic version of AAA is replaced with a mechanism that use a neighbor solution randomly selected from the population and three decision variables of this solution. The current solution is taken from the population and randomly selected three dimensions of this solution are changed using the neighbor solution. The agents of AAA work on continuous solution space and this modification for AAA is required for solving a binary optimization problem because a binary optimization problems have decision variables which are element of set {0, 1}. The performance of the proposed algorithm, binAAA for short, is investigated on the uncapacitated facility location problems which are pure binary optimization problem and there is no integer or real valued decision variables in this problem. The results obtained by binAAA are compared with the results of state-of-art algorithms such as artificial bee colony, particle swarm optimization, and genetic algorithms. Experimental results and comparisons show that the binAAA is better than the other algorithm almost all cases in terms of solution quality and robustness based on the mean and standard deviations, respectively.