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
최근 다양한 분야의 연구자들이 적응성과 유연성의 관점에서 생물의 행동에 관심을 보이고 있다. 사회성 곤충으로 알려진 개미는 개별 개미가 수행할 수 없는 작업을 수행하는 데 있어 집단 행동을 보입니다. 개미 군체에서는 페로몬이라는 화학 물질이 전 지구적 행동에 대한 중요한 정보를 전달하는 방법으로 사용됩니다. 예를 들어, 먹이를 찾는 개미는 특정 유형의 페로몬을 가지고 자신의 둥지로 돌아가는 길을 마련합니다. 다른 개미들은 페로몬 흔적을 따라가며 효율적으로 미끼를 찾을 수 있습니다. 1991년에 Colorni et al. 이러한 수렵 행동과 페로몬 통신의 비유를 사용하여 TSP(여행하는 세일즈맨 문제)에 대한 개미 알고리즘을 제안했습니다. 개미 알고리즘에는 도시 근처를 연결하는 서브투어를 선호한다는 의견으로 TSP 도시를 지속적으로 방문하는 다수의 단순 개미 에이전트로 구성된 군집이 있으며 이들은 강력한 페로몬을 낳습니다. 여행을 마친 개미는 거리에 따라 하위 여행을 통과하면서 다양한 강도의 페로몬을 낳습니다. 즉, 더 나은 가능성이 있는 TSP 투어의 하위 투어는 강한 페로몬을 갖는 경향이 있으므로 개미 에이전트는 이러한 긍정적인 피드백 메커니즘을 사용하여 검색 공간에서 좋은 영역을 지정합니다. 본 논문에서는 개미 알고리즘을 확장한 다중 개미 군체 알고리즘을 제안한다. 이 알고리즘에는 TSP를 해결하기 위한 여러 개미 식민지가 있는 반면 원본에는 단일 개미 식민지만 있습니다. 또한, 콜로니 수준의 상호작용으로는 양성 페로몬 효과와 음성 페로몬 효과의 두 가지 페로몬 효과가 도입되었습니다. 식민지 수준의 상호 작용의 결과로 식민지는 문제 해결을 위한 좋은 도식을 교환할 수 있으며 검색 과정에서 자체적인 변형을 유지할 수 있습니다. 제안된 알고리즘은 식민지 수준의 상호 작용 도입을 제외하고 두 알고리즘에서 사용된 거의 동일한 에이전트 전략으로 원래 알고리즘보다 더 나은 성능을 보여줍니다.
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부
Hidenori KAWAMURA, Masahito YAMAMOTO, Keiji SUZUKI, Azuma OHUCHI, "Multiple Ant Colonies Algorithm Based on Colony Level Interactions" in IEICE TRANSACTIONS on Fundamentals,
vol. E83-A, no. 2, pp. 371-379, February 2000, doi: .
Abstract: Recently, researchers in various fields have shown interest in the behavior of creatures from the viewpoint of adaptiveness and flexibility. Ants, known as social insects, exhibit collective behavior in performing tasks that can not be carried out by an individual ant. In ant colonies, chemical substances, called pheromones, are used as a way to communicate important information on global behavior. For example, ants looking for food lay the way back to their nest with a specific type of pheromone. Other ants can follow the pheromone trail and find their way to baits efficiently. In 1991, Colorni et al. proposed the ant algorithm for Traveling Salesman Problems (TSPs) by using the analogy of such foraging behavior and pheromone communication. In the ant algorithm, there is a colony consisting of many simple ant agents that continuously visit TSP cities with opinions to prefer subtours connecting near cities and they lay strong pheromones. The ants completing their tours lay pheromones of various intensities with passed subtours according to distances. Namely, subtours in TSP tourns that have the possibility of being better tend to have strong pheromones, so the ant agents specify good regions in the search space by using this positive feedback mechanism. In this paper, we propose a multiple ant colonies algorithm that has been extended from the ant algorithm. This algorithm has several ant colonies for solving a TSP, while the original has only a single ant colony. Moreover, two kinds of pheromone effects, positive and negative pheromone effects, are introduced as the colony-level interactions. As a result of colony-level interactions, the colonies can exchange good schemata for solving a problem and can maintain their own variation in the search process. The proposed algorithm shows better performance than the original algorithm with almost the same agent strategy used in both algorithms except for the introduction of colony-level interactions.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e83-a_2_371/_p
부
@ARTICLE{e83-a_2_371,
author={Hidenori KAWAMURA, Masahito YAMAMOTO, Keiji SUZUKI, Azuma OHUCHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Multiple Ant Colonies Algorithm Based on Colony Level Interactions},
year={2000},
volume={E83-A},
number={2},
pages={371-379},
abstract={Recently, researchers in various fields have shown interest in the behavior of creatures from the viewpoint of adaptiveness and flexibility. Ants, known as social insects, exhibit collective behavior in performing tasks that can not be carried out by an individual ant. In ant colonies, chemical substances, called pheromones, are used as a way to communicate important information on global behavior. For example, ants looking for food lay the way back to their nest with a specific type of pheromone. Other ants can follow the pheromone trail and find their way to baits efficiently. In 1991, Colorni et al. proposed the ant algorithm for Traveling Salesman Problems (TSPs) by using the analogy of such foraging behavior and pheromone communication. In the ant algorithm, there is a colony consisting of many simple ant agents that continuously visit TSP cities with opinions to prefer subtours connecting near cities and they lay strong pheromones. The ants completing their tours lay pheromones of various intensities with passed subtours according to distances. Namely, subtours in TSP tourns that have the possibility of being better tend to have strong pheromones, so the ant agents specify good regions in the search space by using this positive feedback mechanism. In this paper, we propose a multiple ant colonies algorithm that has been extended from the ant algorithm. This algorithm has several ant colonies for solving a TSP, while the original has only a single ant colony. Moreover, two kinds of pheromone effects, positive and negative pheromone effects, are introduced as the colony-level interactions. As a result of colony-level interactions, the colonies can exchange good schemata for solving a problem and can maintain their own variation in the search process. The proposed algorithm shows better performance than the original algorithm with almost the same agent strategy used in both algorithms except for the introduction of colony-level interactions.},
keywords={},
doi={},
ISSN={},
month={February},}
부
TY - JOUR
TI - Multiple Ant Colonies Algorithm Based on Colony Level Interactions
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 371
EP - 379
AU - Hidenori KAWAMURA
AU - Masahito YAMAMOTO
AU - Keiji SUZUKI
AU - Azuma OHUCHI
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E83-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2000
AB - Recently, researchers in various fields have shown interest in the behavior of creatures from the viewpoint of adaptiveness and flexibility. Ants, known as social insects, exhibit collective behavior in performing tasks that can not be carried out by an individual ant. In ant colonies, chemical substances, called pheromones, are used as a way to communicate important information on global behavior. For example, ants looking for food lay the way back to their nest with a specific type of pheromone. Other ants can follow the pheromone trail and find their way to baits efficiently. In 1991, Colorni et al. proposed the ant algorithm for Traveling Salesman Problems (TSPs) by using the analogy of such foraging behavior and pheromone communication. In the ant algorithm, there is a colony consisting of many simple ant agents that continuously visit TSP cities with opinions to prefer subtours connecting near cities and they lay strong pheromones. The ants completing their tours lay pheromones of various intensities with passed subtours according to distances. Namely, subtours in TSP tourns that have the possibility of being better tend to have strong pheromones, so the ant agents specify good regions in the search space by using this positive feedback mechanism. In this paper, we propose a multiple ant colonies algorithm that has been extended from the ant algorithm. This algorithm has several ant colonies for solving a TSP, while the original has only a single ant colony. Moreover, two kinds of pheromone effects, positive and negative pheromone effects, are introduced as the colony-level interactions. As a result of colony-level interactions, the colonies can exchange good schemata for solving a problem and can maintain their own variation in the search process. The proposed algorithm shows better performance than the original algorithm with almost the same agent strategy used in both algorithms except for the introduction of colony-level interactions.
ER -