Integrating Evolutionary Computation Components in Ant Colony Optimization

Alonso, Sergio; Cordon, Oscar; Viana, Iñaki Fernández de; Herrera, Francisco

doi:10.4018/9781591403128.ch007

Cited by 2 publications

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“…Parallel ant algorithms often fall under the classes of parallel ants [8,24,29], parallel ant colonies [8,14,18,20], or hybridization of various parallel techniques [1,14,21,28]. In the parallel ants approach, each ant occupies a separate processor and sends pheromone updates to others at each step of the algorithm.…”

Section: Previous Workmentioning

confidence: 99%

Parallel shared memory strategies for ant-based optimization algorithms

Bui

Nguyen

Rizzo

2009

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation

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This paper describes a general scheme to convert sequential ant-based algorithms into parallel shared memory algorithms. The scheme is applied to an ant-based algorithm for the maximum clique problem. Extensive experimental results indicate that the parallel version provides noticeable improvements to the running time while maintaining comparable solution quality to that of the sequential version.

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Section: Previous Workmentioning

confidence: 99%

Parallel shared memory strategies for ant-based optimization algorithms

Bui

Nguyen

Rizzo

2009

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation

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“…A good comparison between evolutionary algorithm (EA) and ACO ones, both as bio-inspired algorithms, was presented in [20]. Also the similarities and differences between population-based incremental learning (PBIL), as an EA, and ACO were discussed in [20]. Both of these methods are memoristic information based to guide the search process.…”

Section: Continuous Ant Colony Optimizationmentioning

confidence: 99%

Extracting Interpretable Fuzzy Models for Nonlinear Systems Using Gradient-based Continuous Ant Colony Optimization

Eftekhari

Zeinalkhani

2013

Fuzzy Information and Engineering

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This paper exploits the ability of a novel ant colony optimization algorithm called gradient-based continuous ant colony optimization, an evolutionary methodology, to extract interpretable first-order fuzzy Sugeno models for nonlinear system identification. The proposed method considers all objectives of system identification task, namely accuracy, interpretability, compactness and validity conditions. First, an initial structure of model is obtained by means of subtractive clustering. Then, an iterative two-step algorithm is employed to produce a simplified fuzzy model in terms of number of fuzzy sets and rules. In the first step, the parameters of the model are adjusted by utilizing the gradient-based continuous ant colony optimization. In the second step, the similar membership functions of an obtained model merge. The results obtained on three case studies illustrate the applicability of the proposed method to extract accurate and interpretable fuzzy models for nonlinear system identification.

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Integrating Evolutionary Computation Components in Ant Colony Optimization

Cited by 2 publications

References 0 publications

Parallel shared memory strategies for ant-based optimization algorithms

Parallel shared memory strategies for ant-based optimization algorithms

Extracting Interpretable Fuzzy Models for Nonlinear Systems Using Gradient-based Continuous Ant Colony Optimization

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