A Parallel Island Model for Estimation of Distribution Algorithms

Madera, Julio; Alba, Enrique; Ochoa, Alberto

doi:10.1007/3-540-32494-1_7

Cited by 12 publications

(12 citation statements)

References 19 publications

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“…In Madera et al (2006), the authors proposed the use of a distributed version of EDA (dEDA) and applied it to both combinatorial and numerical problems. They used the island model in which each processor executed a Univariate Marginal Distribution Algorithm (UMDA), (Mühlenbein 1998).…”

Section: Exchanging Individualsmentioning

confidence: 99%

“…The introduction of Parallel EDAs is a new research direction that has been pursued in the past few years (Hiroyaso et al 2003;Ahn et al 2004;de la Ossa et al 2004de la Ossa et al , 2006Madera et al 2006;Schwarz et al 2007;Jaros and Schwarz 2007). The general idea is to have different EDAs running in parallel and exchanging information among them.…”

Section: Introductionmentioning

confidence: 99%

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A cooperative particle swarm optimizer with migration of heterogeneous probabilistic models

El-Abd

Kamel

2009

Swarm Intell

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Particle Swarm Optimization (PSO) is a stochastic optimization approach that originated from simulations of bird flocking, and that has been successfully used in many applications as an optimization tool. Estimation of distribution algorithms (EDAs) are a class of evolutionary algorithms which perform a two-step process: building a probabilistic model from which good solutions may be generated and then using this model to generate new individuals. Two distinct research trends that emerged in the past few years are the hybridization of PSO and EDA algorithms and the parallelization of EDAs to exploit the idea of exchanging the probabilistic model information. In this work, we propose the use of a cooperative PSO/EDA algorithm based on the exchange of heterogeneous probabilistic models. The model is heterogeneous because the cooperating PSO/EDA algorithms use different methods to sample the search space. Three different exchange approaches are tested and compared in this work. In all these approaches, the amount of information exchanged is adapted based on the performance of the two cooperating swarms. The performance of the cooperative model is compared to the existing state-of-the-art PSO cooperative approaches using a suite of well-known benchmark optimization functions.

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Section: Exchanging Individualsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A cooperative particle swarm optimizer with migration of heterogeneous probabilistic models

El-Abd

Kamel

2009

Swarm Intell

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“…In addition to IslandEDA, other variants of islandbased EDAs that migrate individuals rather than model parameters, have also been investigated [23]. As discussed above, IslandEDA is more efficient than other island-based EDA, especially when there are fewer migrating individuals.…”

Section: Island Modelmentioning

confidence: 99%

“…However, since GMM is estimated explicitly by expectation maximization (EM) algorithm [19], the computational cost is usually high. Some researchers thus suggested quite a few alternative (and computationally more efficient) approaches, e.g., clustering techniques [15], [20], [21], niching methods [16], and parallel island models [22], [23] to manage multiple Gaussian models. Among them, clustering based EDAs apply clustering techniques to divide a population into several clusters, and form a sub-model for each cluster.…”

Section: Introductionmentioning

confidence: 99%

Improving Estimation of Distribution Algorithm on Multimodal Problems by Detecting Promising Areas

Yang

Tang

2015

IEEE Trans. Cybern.

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In this paper, a novel multiple sub-models maintenance technique, named maintaining and processing sub-models (MAPS), is proposed. MAPS aims to enhance the ability of estimation of distribution algorithms (EDAs) on multimodal problems. The advantages of MAPS over the existing multiple sub-models based EDAs stem from the explicit detection of the promising areas, which can save many function evaluations for exploration and thus accelerate the optimization speed. MAPS can be combined with any EDA that adopts a single Gaussian model. The performance of MAPS has been assessed through empirical studies where MAPS is integrated with three different types of EDAs. The experimental results show that MAPS can lead to much faster convergence speed and obtain more stable solutions than the compared algorithms on 12 benchmark problems.

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“…This information can be made up of individuals (as done in other EAs), or probabilistic models (following the rationale that EDAs use them to extract and gather information about the population). Migration of individuals is a classic approach and has proven to obtain successful results in these and other Evolutionary Algorithms [2,4,9,28]. In addition, migration of models was explicitly developed for the distributed Estimation of Distribution Algorithms [1,11,12,19,20].…”

Section: Introductionmentioning

confidence: 99%

Distributed Estimation of Distribution Algorithms for continuous optimization: How does the exchanged information influence their behavior?

Muelas

Mendiburu

LaTorre

et al. 2014

Information Sciences

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One of the most promising areas in which probabilistic graphical models have shown an incipient activity is the field of heuristic optimization and, in particular, in Estimation of Distribution Algorithms. Due to their inherent parallelism, different research lines have been studied trying to improve Estimation of Distribution Algorithms from the point of view of execution time and/or accuracy. Among these proposals, we focus on the so-called distributed or island-based models. This approach defines several islands (algorithms instances) running independently and exchanging information with a given frequency. The information sent by the islands can be either a set of individuals or a probabilistic model. This paper presents a comparative study for a distributed univariate Estimation of Distribution Algorithm and a multivariate version, paying special attention to the comparison of two alternative methods for exchanging information, over a wide set of parameters and problems -the standard benchmark developed for the IEEE Workshop on Evolutionary Algorithms and other Metaheuristics for Continuous Optimization Problems of the ISDA 2009 Conference. Several analyses from different points of view have been conducted to analyze both the influence of the parameters and the relationships between them including a characterization of the configurations according to their behavior on the proposed benchmark.

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A Parallel Island Model for Estimation of Distribution Algorithms

Cited by 12 publications

References 19 publications

A cooperative particle swarm optimizer with migration of heterogeneous probabilistic models

A cooperative particle swarm optimizer with migration of heterogeneous probabilistic models

Improving Estimation of Distribution Algorithm on Multimodal Problems by Detecting Promising Areas

Distributed Estimation of Distribution Algorithms for continuous optimization: How does the exchanged information influence their behavior?

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