Abstract
Ant Colony Optimization (ACO) is one of promising meta-heuristics for graph search such as shortest path planning and traveling salesman problems. In recent years, some attempts have shown that ACO algorithms are applicable to 0-1 Integer Programming Problems (0-1IP). ACO algorithms for 0-1IP are called Binary ACO (BACO) algorithms. Although it is predictable that balance between search exploitation and exploration is important in ACO for 0-1IP, no previous work has proposed an algorithm which adjusts the balance. This paper proposes a method which is designed by applying Queen Ant Strategy (ASqueen) to BACO algorithms. The proposed method has a prospect for finding well-qualified solutions due to its subpopulation structure and the search area adjustment by a queen ant. Experimental results in 0-1 Knapsack problems have shown that the search performance of the proposed BASqueen shows better than that of other BACO algorithms, Simulated Annealing and Discrete Particle Swarm Optimization.
