Ant colony optimization for continuous domains

Socha, Krzysztof; Dorigo, Marco

doi:10.1016/j.ejor.2006.06.046

Cited by 1,166 publications

(756 citation statements)

References 33 publications

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“…More generally, it should be interesting to deal with continuous and discrete parameters in the same algorithm. As a consequence, a mixed method has been developed, firstly presented by Socha [11] that keeps the same concepts as the discrete ACO method.…”

Section: Continuous Problemmentioning

confidence: 99%

“…In order to extend the process to continuous variables, Socha keeps the usual algorithm, but changes the discrete probability law into a continuous Probability Density Function (PDF) (figure 1) [11]. That is the principle of the ACOR (Ant Colony Optimization for Reals) method.…”

Section: Continuous Problemmentioning

confidence: 99%

“…As the original approach deals with discrete parameters, some recent modifications of the algorithm have been made to extend continuous variables to the mixed ones. One of the most contributions in this domain is the works of Socha [11]- [14].…”

Section: Introductionmentioning

confidence: 99%

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Transfer function fitting using a continuous Ant Colony Optimization (ACO) algorithm

Reineix

Guiffaut²

2015

AEM

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An original approach is proposed in order to achieve the fitting of ultra-wideband complex frequency functions, such as the complex impedances, by using the so-called ACO (Ant Colony Optimization) methods. First, we present the optimization principle of ACO, which originally was dedicated to the combinatorial problems. Further on, the extension to the continuous and mixed problems is explained in more details. The interest in this approach is proved by its ability to define practical constraints and objectives, such as minimizing the number of filters used in the model with respect to a fixed relative error. Finally, the establishment of the model for the first and second order filter types illustrates the power of the method and its interest for the time-domain electromagnetic computation.

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Section: Continuous Problemmentioning

confidence: 99%

Section: Continuous Problemmentioning

confidence: 99%

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Transfer function fitting using a continuous Ant Colony Optimization (ACO) algorithm

Reineix

Guiffaut²

2015

AEM

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“…The continuous version of ACO [168] was efficiently applied to optimize the FNN weight vector [169]. ACO trained FNN was found efficient in solving real-life applications, such as scheduling, prediction, image recognition, etc.…”

Section: Weight Optimizationmentioning

confidence: 99%

Metaheuristic design of feedforward neural networks: A review of two decades of research

Ojha

Abraham

Snel

2017

Engineering Applications of Artificial Intelligence

435

154

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Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era.

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“…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]. Hybrid ant algorithms combined with genetic algorithms [23] , artificial immune systems [25] and particle swarms (PSACO) [26] have also been proposed.…”

Section: Introductionmentioning

confidence: 99%

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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Ant colony optimization for continuous domains

Cited by 1,166 publications

References 33 publications

Transfer function fitting using a continuous Ant Colony Optimization (ACO) algorithm

Transfer function fitting using a continuous Ant Colony Optimization (ACO) algorithm

Metaheuristic design of feedforward neural networks: A review of two decades of research

Orthogonal Methods Based Ant Colony Search for Solving Continuous Optimization Problems

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