Approach of Hybrid Ant Agents and Probabilistic Analysis for Combinatorial Optimization Problems
| Project/Area Number |
14580466
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
社会システム工学
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| Research Institution | Otaru University of Commerce |
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Principal Investigator |
KAJI Taichi Otaru University of Commerce, Faculty of Commerce, Professor, 商学部, 教授 (60214300)
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| Project Period (FY) |
2002 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥3,400,000 (Direct Cost: ¥3,400,000)
Fiscal Year 2004: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2003: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2002: ¥1,200,000 (Direct Cost: ¥1,200,000)
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| Keywords | Traveling Salesman Problem / Ant System Algorithm / Agent / Tabu Search / Pheromone / Probabilistic Analysis |
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| Research Abstract |
The idea of ant system algorithm proposed by Dorigo is very unique. However, the standard type of the ant system algorithm cannot obtain better solutions for random graphs. So, we design new agent by using pheromone based on intensification and diversification strategy, such as the tabu search is applied, in order to reach better solutions. We attempt to apply approach based on neighborhood to the ant system algorithm in terms of improving quality of solutions because the ant system algorithm does not depend on neighborhood. And, parallel ant system algorithm by above-mentioned new agents is implemented to reduce computational time. Furthermore, we present how the difficulty caused in parallel algorithm for another meta-heuristics has been overcome using agent technology. Finally we discuss the characteristics of these meta-heuristics attempting to construct a model which gives theoretical probabilistic analysis for wide class of combinatorial optimization problem. We consider that it is possible to adapt this model to many problems. Here, we introduce AR(1) model to numerically approximate various kinds of neighborhood, and formulate a probabilistic model, which compute the average-case of the costs of the solutions found by local search and the required number of steps. We discuss the characteristics of meta-heuristics using this probabilistic analysis.
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Report
(4 results)
Research Products
(14 results)
