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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
ACO(개미 군체 최적화) 알고리즘은 최근 개발된 인구 기반 접근 방식으로 최적화 문제에 성공적으로 적용되었습니다. 그러나 ACO 알고리즘에서는 강화와 다양화 사이의 균형을 조정하기가 어렵기 때문에 항상 성능이 좋지는 않습니다. 본 연구에서는 일부 개미가 유전적 연산을 수행하여 진화할 수 있고, 유전적 연산을 수행하는 개미의 수에 따라 강화와 다양화의 균형을 조정할 수 있는 개선된 ACO 알고리즘을 제안합니다. 제안된 알고리즘은 TSP(Traveling Salesman Problem)를 시뮬레이션하여 테스트하였다. 실험적 연구에 따르면 제안된 유전적 연산을 이용한 ACO 알고리즘은 기존의 다른 ACO 알고리즘에 비해 우수한 성능을 보인다.
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Rong-Long WANG, Xiao-Fan ZHOU, Kozo OKAZAKI, "Ant Colony Optimization with Genetic Operation and Its Application to Traveling Salesman Problem" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 5, pp. 1368-1372, May 2009, doi: 10.1587/transfun.E92.A.1368.
Abstract: Ant colony optimization (ACO) algorithms are a recently developed, population-based approach which has been successfully applied to optimization problems. However, in the ACO algorithms it is difficult to adjust the balance between intensification and diversification and thus the performance is not always very well. In this work, we propose an improved ACO algorithm in which some of ants can evolve by performing genetic operation, and the balance between intensification and diversification can be adjusted by numbers of ants which perform genetic operation. The proposed algorithm is tested by simulating the Traveling Salesman Problem (TSP). Experimental studies show that the proposed ACO algorithm with genetic operation has superior performance when compared to other existing ACO algorithms.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.1368/_p
부
@ARTICLE{e92-a_5_1368,
author={Rong-Long WANG, Xiao-Fan ZHOU, Kozo OKAZAKI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Ant Colony Optimization with Genetic Operation and Its Application to Traveling Salesman Problem},
year={2009},
volume={E92-A},
number={5},
pages={1368-1372},
abstract={Ant colony optimization (ACO) algorithms are a recently developed, population-based approach which has been successfully applied to optimization problems. However, in the ACO algorithms it is difficult to adjust the balance between intensification and diversification and thus the performance is not always very well. In this work, we propose an improved ACO algorithm in which some of ants can evolve by performing genetic operation, and the balance between intensification and diversification can be adjusted by numbers of ants which perform genetic operation. The proposed algorithm is tested by simulating the Traveling Salesman Problem (TSP). Experimental studies show that the proposed ACO algorithm with genetic operation has superior performance when compared to other existing ACO algorithms.},
keywords={},
doi={10.1587/transfun.E92.A.1368},
ISSN={1745-1337},
month={May},}
부
TY - JOUR
TI - Ant Colony Optimization with Genetic Operation and Its Application to Traveling Salesman Problem
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1368
EP - 1372
AU - Rong-Long WANG
AU - Xiao-Fan ZHOU
AU - Kozo OKAZAKI
PY - 2009
DO - 10.1587/transfun.E92.A.1368
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2009
AB - Ant colony optimization (ACO) algorithms are a recently developed, population-based approach which has been successfully applied to optimization problems. However, in the ACO algorithms it is difficult to adjust the balance between intensification and diversification and thus the performance is not always very well. In this work, we propose an improved ACO algorithm in which some of ants can evolve by performing genetic operation, and the balance between intensification and diversification can be adjusted by numbers of ants which perform genetic operation. The proposed algorithm is tested by simulating the Traveling Salesman Problem (TSP). Experimental studies show that the proposed ACO algorithm with genetic operation has superior performance when compared to other existing ACO algorithms.
ER -