Presentation 2018-07-02
Solving Constraint Satisfaction Problems Using Ant Colony Optimization with Multi-Pheromones
Takuya Masukane, Kazunori Mizuno,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Ant colony optimization, ACO, is one of the meta-heuristics. ACO has been effective to solve constraint satisfaction problems. However, once pheromones become flat, the pheromones tend to be ineffective. In this paper, we propose the ACO model applying another kind of pheromones stored negative information for updating usual pheromones. We also implement the proposed model to the ACO based algorithm, the cunning ant system, and evaluate the effectiveness of the proposed model for solving graph coloring problems which are one of constraint satisfaction problems.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) constraint satisfaction / search / meta heuristics / ant colony optimization / graph coloring
Paper # AI2018-2
Date of Issue 2018-06-25 (AI)

Conference Information
Committee AI
Conference Date 2018/7/2(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Tsunenori Mine(Kyushu Univ.)
Vice Chair Daisuke Katagami(Tokyo Polytechnic Univ.) / Naoki Fukuta(Shizuoka Univ.)
Secretary Daisuke Katagami(Ritsumeikan Univ.) / Naoki Fukuta(Univ. of Electro-Comm.)
Assistant Yuko Sakurai(AIST)

Paper Information
Registration To Technical Committee on Artificial Intelligence and Knowledge-Based Processing
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Solving Constraint Satisfaction Problems Using Ant Colony Optimization with Multi-Pheromones
Sub Title (in English)
Keyword(1) constraint satisfaction
Keyword(2) search
Keyword(3) meta heuristics
Keyword(4) ant colony optimization
Keyword(5) graph coloring
1st Author's Name Takuya Masukane
1st Author's Affiliation Takushoku University(Takushoku Univ.)
2nd Author's Name Kazunori Mizuno
2nd Author's Affiliation Takushoku University(Takushoku Univ.)
Date 2018-07-02
Paper # AI2018-2
Volume (vol) vol.118
Number (no) AI-116
Page pp.pp.7-10(AI),
#Pages 4
Date of Issue 2018-06-25 (AI)