Improving binary ant colony optimization by adaptive pheromone and commutative solution update

Ant Colony Optimization (ACO) algorithm is used to simulate the decision-making processes of ant colonies as they search for food. It has been applied to many combinatorial optimization problems, especially discrete optimization. Binary ACO (BACO) is a tool for optimization of continuous functions. This paper proposes a novel algorithm, abbreviated to ACBACO, to improve BACO in convergence rate and searching stability. ACBACO was evaluated by using nine test functions and compared with other five optimization methods. The results show that ACBACO performs better than the five methods in optima and number of iterations.