The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Tanto o algoritmo de seleção clonal (CSA) quanto a otimização de colônias de formigas (ACO) são inspirados em fenômenos naturais e são ferramentas eficazes para resolver problemas complexos. O CSA pode explorar e explorar o espaço de soluções de forma paralela e eficaz. No entanto, ele não pode usar informações suficientes de feedback do ambiente e, portanto, precisa fazer uma grande repetição de redundância durante a pesquisa. Por outro lado, o ACO baseia-se no conceito de processo de forrageamento cooperativo indireto por meio da secreção de feromônios. Sua capacidade de feedback positivo é boa, mas sua velocidade de convergência é lenta por causa dos poucos feromônios iniciais. Neste artigo, propomos um ligante de feromônios para combinar esses dois algoritmos. A seleção clonal híbrida proposta e otimização de colônias de formigas (CSA-ACO) utiliza razoavelmente as superioridades de ambos os algoritmos e também supera suas desvantagens inerentes. Resultados de simulações baseadas nos problemas do caixeiro viajante demonstraram o mérito do algoritmo proposto sobre algumas técnicas tradicionais.
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Shangce GAO, Wei WANG, Hongwei DAI, Fangjia LI, Zheng TANG, "Improved Clonal Selection Algorithm Combined with Ant Colony Optimization" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 6, pp. 1813-1823, June 2008, doi: 10.1093/ietisy/e91-d.6.1813.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.6.1813/_p
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@ARTICLE{e91-d_6_1813,
author={Shangce GAO, Wei WANG, Hongwei DAI, Fangjia LI, Zheng TANG, },
journal={IEICE TRANSACTIONS on Information},
title={Improved Clonal Selection Algorithm Combined with Ant Colony Optimization},
year={2008},
volume={E91-D},
number={6},
pages={1813-1823},
abstract={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.},
keywords={},
doi={10.1093/ietisy/e91-d.6.1813},
ISSN={1745-1361},
month={June},}
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TY - JOUR
TI - Improved Clonal Selection Algorithm Combined with Ant Colony Optimization
T2 - IEICE TRANSACTIONS on Information
SP - 1813
EP - 1823
AU - Shangce GAO
AU - Wei WANG
AU - Hongwei DAI
AU - Fangjia LI
AU - Zheng TANG
PY - 2008
DO - 10.1093/ietisy/e91-d.6.1813
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2008
AB - 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.
ER -