On how Pachycondyla apicalis ants suggest a new search algorithm

Monmarché, Nicolas; Venturini, Gilles; Slimane, Mohamed

doi:10.1016/s0167-739x(00)00047-9

Cited by 204 publications

(94 citation statements)

References 11 publications

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“…For this kind of problems, a number of methods have been proposed in the literature. They include some antrelated methods (Bilchev and Parmee, 1995;Monmarché et al, 2000;Dréo and Siarry, 2002), as well as a more generic swarm inspired method such as Particle Swarm Optimization (Kennedy and Eberhart, 1995). There are also many others: many metaheuristics have been originally developed for combinatorial optimization and later adapted to the continuous case.…”

Section: Positioning Of Aco Rmentioning

confidence: 99%

Ant colony optimization for continuous domains

Socha

Dorigo

2008

European Journal of Operational Research

1,165

681

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Section: Positioning Of Aco Rmentioning

confidence: 99%

Ant colony optimization for continuous domains

Socha

Dorigo

2008

European Journal of Operational Research

1,165

681

View full text Add to dashboard Cite

“…In this subsection, the performance of our proposed continuous orthogonal ant colony (COAC) algorithm will be compared with two other ant algorithms, namely, API [15] and continuous ACO (CACO) [13] . The dimensions for the functions are all set as four.…”

Section: Comparisons Between Coac and Other Ant Algorithmsmentioning

confidence: 99%

“…Please refer to [15] for more details about API. The parameter settings in CACO are: the number of regions µ = 200, the number of local ants L = 10, the number of global ants G = 90.…”

Section: Comparisons Between Coac and Other Ant Algorithmsmentioning

confidence: 99%

“…The CACO combines both ant colony and genetic algorithm (GA) [14] techniques together. Monmarché et al [15] proposed a method termed API in 2000, where the ant colony search mechanism consists of a set of parallel local searches on hunting sites with sensitivity to successful sites and the ants' nest is periodically moved [15] . Other methods developed recently include the continuous interacting ant colony (CIAC) algorithm [16,17] , the extended ACO to continuous domains (ACO R ) [18,19] , the continuous ant colony system (CACS) [20] , the binary ant system (BAS) [21] , the direct application of ACO (DACO) [22] , and those algorithms reported in [23,24].…”

Section: Introductionmentioning

confidence: 99%

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Orthogonal Methods Based Ant Colony Search for Solving Continuous Optimization Problems

Zhang

2008

J. Comput. Sci. Technol.

100

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Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide range of optimization problems, including continuous optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed "continuous orthogonal ant colony" (COAC), whose pheromone deposit mechanisms would enable ants to search for solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and efficiently. By implementing an "adaptive regional radius" method, the proposed algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is compared with two other ant algorithms for continuous optimization -API and CACO by testing seventeen functions in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others.

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“…For instance, genetic algorithms have been used in many research fields, such as clustering problems [1], [2] and optimization [3]. Other examples can be found in the modeling of collective behaviors of ants as in the well-known algorithmic approach Ant Colony Optimization (ACO)( [4]) in which pheromone trails are used.…”

Section: Introductionmentioning

confidence: 99%

AntClust: Ant Clustering and Web Usage Mining

Labroche

Monmarché

Venturini

2003

Lecture Notes in Computer Science

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Abstract. In this paper, we propose a new ant-based clustering algorithm called AntClust. It is inspired from the chemical recognition system of ants. In this system, the continuous interactions between the nestmates generate a "Gestalt" colonial odor. Similarly, our clustering algorithm associates an object of the data set to the odor of an ant and then simulates meetings between ants. At the end, artificial ants that share a similar odor are grouped in the same nest, which provides the expected partition. We compare AntClust to the K-Means method and to the AntClass algorithm. We present new results on artificial and real data sets. We show that AntClust performs well and can extract meaningful knowledge from real Web sessions.

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On how Pachycondyla apicalis ants suggest a new search algorithm

Cited by 204 publications

References 11 publications

Ant colony optimization for continuous domains

Ant colony optimization for continuous domains

Orthogonal Methods Based Ant Colony Search for Solving Continuous Optimization Problems

AntClust: Ant Clustering and Web Usage Mining

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