Parallel BMDA with probability model migration

Jaroš, Jiří; Schwarz, Josef

doi:10.1109/cec.2007.4424587

Cited by 10 publications

(7 citation statements)

References 22 publications

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“…It was also shown that the migration of a probabilistic model is better than the migration of individuals especially when setting β adaptively. Schwarz et al (2007) and Jaros and Schwarz (2007) proposed the use of a parallel bivariate marginal distribution algorithm (BMDA). The island model was used in a directed ring topology.…”

Section: Exchanging Probabilistic Modelsmentioning

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 Probabilistic Modelsmentioning

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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“…This second approach opens a new challenge: how should the different probabilistic models be combined? In general, the combination of the resident model with the immigrant one can be formalized by the following rule [19]:…”

Section: Distributed Estimation Of Distribution Algorithms: Dedasmentioning

confidence: 99%

“…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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“…migration operation) among different sub-groups at a certain evolutionary generation to generate new population and evolve the next generation on the basis of this cycle until the algorithm termination condition is satisfied. A migration operation is proven to be effective for EDAs and is known to yield good results [5,6,7,8,9].…”

Section: Introductionmentioning

confidence: 99%

Variable Classification-based Co-estimation of Distribution Algorithm Assisted by a Cooperative Particle Swarm Optimizer

Liu¹,

Yan²

2017

dtetr

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Since estimation of distribution algorithms (EDAs) were proposed, many methods have been made to improve EDAs' performance. In this paper, a variable classification-based cooperative estimation of distribution algorithm (VC-CEDA) is proposed to balance the global and local searching ability of estimation of distribution algorithms. This study proposes a variable classification method to improve the efficiency and accuracy of probability model development. And a cooperative particle swarm optimizer that can improve the local searching ability is proposed. The VC-CEDA combines the advantages of traditional algorithm and cooperative operations. VC-CEDA shows significantly better performance on 10 test functions. In this study, the experimental results show that the VC-CEDA performs better than traditional EDAs.

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Parallel BMDA with probability model migration

Cited by 10 publications

References 22 publications

A cooperative particle swarm optimizer with migration of heterogeneous probabilistic models

A cooperative particle swarm optimizer with migration of heterogeneous probabilistic models

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

Variable Classification-based Co-estimation of Distribution Algorithm Assisted by a Cooperative Particle Swarm Optimizer

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