Study on the Effectiveness of Queen Ant Strategy for Binary Ant Colony Optimization

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  • IKEMIZU Takayuki
    Department of Information and Computer Science, Graduate School of Science and Engineering, Kagoshima University
  • ONO Satoshi
    Department of Information and Computer Science, Graduate School of Science and Engineering, Kagoshima University
  • MORISHIGE Ryota
    Department of Information and Computer Science, Graduate School of Science and Engineering, Kagoshima University
  • NAKAYAMA Shigeru
    Department of Information and Computer Science, Graduate School of Science and Engineering, Kagoshima University
  • IIMURA Ichiro
    Department of Administration, Faculty of Administration, Prefectural University of Kumamoto

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  • Ｂｉｎａｒｙ　Ａｎｔ　Ｃｏｌｏｎｙ　Ｏｐｔｉｍｉｚａｔｉｏｎにおける女王蟻戦略の有効性の検討
  • Binary Ant Colony Optimization ニ オケル ジョオウ アリ センリャク ノ ユウコウセイ ノ ケントウ

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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.

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