Improved Clonal Selection Algorithm Combined with Ant Colony Optimization

• GAO Shangce

  Faculty of Engineering, University of Toyama

• WANG Wei

  Faculty of Engineering, University of Toyama

• DAI Hongwei

  Faculty of Engineering, University of Toyama

• LI Fangjia

  Faculty of Engineering, University of Toyama

• TANG Zheng

  Faculty of Engineering, University of Toyama The Institute of Electronics, Information and Communication Engineers

抄録

Both the clonal selection algorithm (CSA) and the ant colony optimization (ACO) are inspired by natural phenomena and are effective tools for solving complex problems. CSA can exploit and explore the solution space parallely and effectively. However, it can not use enough environment feedback information and thus has to do a large redundancy repeat during search. On the other hand, ACO is based on the concept of indirect cooperative foraging process via secreting pheromones. Its positive feedback ability is nice but its convergence speed is slow because of the little initial pheromones. In this paper, we propose a pheromone-linker to combine these two algorithms. The proposed hybrid clonal selection and ant colony optimization (CSA-ACO) reasonably utilizes the superiorities of both algorithms and also overcomes their inherent disadvantages. Simulation results based on the traveling salesman problems have demonstrated the merit of the proposed algorithm over some traditional techniques.

収録刊行物

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E91-D (6), 1813-1823, 2008

  一般社団法人 電子情報通信学会

キーワード

• clonal selection algorithm
• ant colony optimization
• traveling salesman problem
• hybridization

詳細情報

• CRID: 1390282679356020096
• NII論文ID: 10026804566
• NII書誌ID: AA10826272
• DOI: 10.1093/ietisy/e91-d.6.1813
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E91.D/6/E91.D_6_1813/_pdf
• 本文言語コード: en
• データソース種別:
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  • Crossref
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• 抄録ライセンスフラグ: 使用不可