Foraging behavior in ant colonies has come to be viewed as a prototypical example to describe how complex group behavior can arise from simple individuals. In order to research the feature of self-organization in swar...Foraging behavior in ant colonies has come to be viewed as a prototypical example to describe how complex group behavior can arise from simple individuals. In order to research the feature of self-organization in swarm intelligence (SI), a mean field model is given and analyzed in foraging process with three sources in this paper. The distance of trails and the richness of each source are considered. Both of the theoretical numerical analysis and Monte Carlo simulation show the power law relationship between the completion time and the flux of foragers. The work presented here guides a better understanding on self-organization and swarm intelligence. It can be used to design more efficient, adaptive, and reliable intelligent systems.展开更多
Biologic behaviors are the principal source for proposing new intelligent algorithms. Based on the mechanism of the bio-subsistence and the bio-migration, this paper proposes a novel algorithm—Living Migration Algori...Biologic behaviors are the principal source for proposing new intelligent algorithms. Based on the mechanism of the bio-subsistence and the bio-migration, this paper proposes a novel algorithm—Living Migration Algorithm (LMA). The original contributions of LMA are three essential attributes of each individual: the minimal life-needs which are the necessaries for survival, the migrating which is a basal action for searching new living space, and the judging which is an important ability of deciding whether to migrate or not. When living space of all individuals can satisfy the minimal life-needs at some generation, they are considered as the optimal living places where objective functions will obtain the optima. LMA may be employed in large-scale computation and engineering field. The paper mostly operates LMA to deal with four non-linear and heterogeneous optimizations, and experiments prove LMA has better performances than Free Search algorithm.展开更多
Inspired by the swarm intelligence in self-organizing behavior of real ant colonies, various ant-based algorithms were proposed recently for many research fields in data mining such as clustering. Compared with the pr...Inspired by the swarm intelligence in self-organizing behavior of real ant colonies, various ant-based algorithms were proposed recently for many research fields in data mining such as clustering. Compared with the previous clustering approaches such as K-means, the main advantage of ant-based clustering algorithms is that no additional information is needed, such as the initial partitioning of the data or the number of clusters. In this paper, we present an adaptive ant clustering algorithm ACAD. The algorithm uses a digraph where the vertexes represent the data to be clustered. The weighted edges represent the acceptance rate between the two data it connected. The pheromone on the edges is adaptively updated by the ants passing it. Some edges with less pheromone are progressively removed under a threshold in the process. Strong connected components of the final digraph are extracted as clusters. Experimental results on several real datasets and benchmarks indicate that ACAD is conceptually simpler, more efficient and more robust than previous research such as the classical K-means clustering algorithm and LF algorithm which.is also based on ACO展开更多
基金Sponsored by the National High Technology Research and Development Program 863(Grant No.2009AA04Z215)the National Natural Science Foundation of China(Grant No.60975071)the Fund for Basic Research from Harbin Engineering University(Grant No.002060260750)
文摘Foraging behavior in ant colonies has come to be viewed as a prototypical example to describe how complex group behavior can arise from simple individuals. In order to research the feature of self-organization in swarm intelligence (SI), a mean field model is given and analyzed in foraging process with three sources in this paper. The distance of trails and the richness of each source are considered. Both of the theoretical numerical analysis and Monte Carlo simulation show the power law relationship between the completion time and the flux of foragers. The work presented here guides a better understanding on self-organization and swarm intelligence. It can be used to design more efficient, adaptive, and reliable intelligent systems.
文摘Biologic behaviors are the principal source for proposing new intelligent algorithms. Based on the mechanism of the bio-subsistence and the bio-migration, this paper proposes a novel algorithm—Living Migration Algorithm (LMA). The original contributions of LMA are three essential attributes of each individual: the minimal life-needs which are the necessaries for survival, the migrating which is a basal action for searching new living space, and the judging which is an important ability of deciding whether to migrate or not. When living space of all individuals can satisfy the minimal life-needs at some generation, they are considered as the optimal living places where objective functions will obtain the optima. LMA may be employed in large-scale computation and engineering field. The paper mostly operates LMA to deal with four non-linear and heterogeneous optimizations, and experiments prove LMA has better performances than Free Search algorithm.
基金This project is supported in part by National Natural Science Foundation of China （60673060）, Science Foundation of Jiangsu Province （BK2005047）.
文摘Inspired by the swarm intelligence in self-organizing behavior of real ant colonies, various ant-based algorithms were proposed recently for many research fields in data mining such as clustering. Compared with the previous clustering approaches such as K-means, the main advantage of ant-based clustering algorithms is that no additional information is needed, such as the initial partitioning of the data or the number of clusters. In this paper, we present an adaptive ant clustering algorithm ACAD. The algorithm uses a digraph where the vertexes represent the data to be clustered. The weighted edges represent the acceptance rate between the two data it connected. The pheromone on the edges is adaptively updated by the ants passing it. Some edges with less pheromone are progressively removed under a threshold in the process. Strong connected components of the final digraph are extracted as clusters. Experimental results on several real datasets and benchmarks indicate that ACAD is conceptually simpler, more efficient and more robust than previous research such as the classical K-means clustering algorithm and LF algorithm which.is also based on ACO
