A new initialization procedure for the distributed estimation of distribution algorithms

Muelas, Santiago; Peña, José-María; LaTorre, Antonio; Robles, Víctor

doi:10.1007/s00500-010-0603-1

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…In particular, in centroidal VT, the generating solution of each Voronoi cell is also its centroid. The approach was examined for an evolutionary algorithm, namely estimation of distribution algorithm (EDA) in Muelas et al (2010). The authors defined a partition set of the solution space in which each island or node will start its own exploration.…”

Section: Clusteringmentioning

confidence: 99%

Initialization of metaheuristics: comprehensive review, critical analysis, and research directions

Sarhani

Voß

Jovanović

2022

Int Trans Operational Res

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Initialization of metaheuristics is a crucial topic that lacks a comprehensive and systematic review of the state of the art. Providing such a review requires in‐depth study and knowledge of the advances and challenges in the broader field of metaheuristics, especially with regard to diversification strategies, in order to assess the proposed methods and provide insights for initialization. Motivated by the aforementioned research gap, we provide a related review and begin by describing the main metaheuristic methods and their diversification mechanisms. Then, we review and analyze the existing initialization approaches while proposing a new categorization of them. Next, we focus on challenging optimization problems, namely constrained and discrete optimization. Lastly, we give insights on the initialization of local search approaches.

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Section: Clusteringmentioning

confidence: 99%

Initialization of metaheuristics: comprehensive review, critical analysis, and research directions

Sarhani

Voß

Jovanović

2022

Int Trans Operational Res

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“…For the initialization of particles (line 2), we have partially used the method proposed in Muelas et al (2010) to generate good diverse solutions. This method starts with the partition of the range of each dimension to sr subranges of equal size.…”

Section: Algorithm Proposal: Pso6-mtslsmentioning

confidence: 99%

Hybrid PSO6 for hard continuous optimization

García-Nieto

Alba

2014

Soft Comput

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In our previous works, we empirically showed that a number of 6 ±2 informants may endow particle swarm optimization (PSO) with an optimized learning procedure in comparison with other combinations of informants. In this way, the new version PSO6, that evolves new particles from six informants (neighbors), performs more accurately than other existing versions of PSO and is able to generate good particles for a longer time. Despite this advantage, PSO6 may show certain attraction to local basins derived from its moderate performance on non-separable complex problems (typically observed in PSO versions). In this paper, we incorporate a local search procedure to the PSO6 with the aim of correcting this disadvantage. We compare the performance of our proposal (PSO6-Mtsls) on a set of 40 benchmark functions against that of other PSO versions, as well as against the best recent proposals in the current state of the art (with and without local search). The results support our conjecture that the (quasi)-optimally informed PSO, hybridized with local search mechanisms, reaches a high rate of success on a large number of complex (non-separable) continuous optimization functions.

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“…In recent years, Estimation of Distribution Algorithms (EDAs) have received numerous attention [2][3][4][5][6]. In the procedures of research, explicitly learning and building a probabilistic model from the parental distribution, and then sampling new solutions according to the probabilistic model [7] has been a key focus of researchers.…”

Section: Introductionmentioning

confidence: 99%

EA/G-GA for Single Machine Scheduling Problems with Earliness/Tardiness Costs

Chen

Chang

et al. 2011

Entropy

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An Estimation of Distribution Algorithm (EDA), which depends on explicitly sampling mechanisms based on probabilistic models with information extracted from the parental solutions to generate new solutions, has constituted one of the major research areas in the field of evolutionary computation. The fact that no genetic operators are used in EDAs is a major characteristic differentiating EDAs from other genetic algorithms (GAs). This advantage, however, could lead to premature convergence of EDAs as the probabilistic models are no longer generating diversified solutions. In our previous research [1], we have presented the evidences that EDAs suffer from the drawback of premature convergency, thus several important guidelines are provided for the design of effective EDAs. In this paper, we validated one guideline for incorporating other meta-heuristics into the EDAs. An algorithm named "EA/G-GA" is proposed by selecting a well-known EDA, EA/G, to work with GAs. The proposed algorithm was tested on the NP-Hard single machine scheduling problems with the total weighted earliness/tardiness cost in a just-in-time environment. The experimental results indicated that the EA/G-GA outperforms the compared algorithms statistically significantly across different stopping criteria and demonstrated the robustness of the proposed algorithm. Consequently, this paper is of interest and importance in the field of EDAs.

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A new initialization procedure for the distributed estimation of distribution algorithms

Cited by 4 publications

References 16 publications

Initialization of metaheuristics: comprehensive review, critical analysis, and research directions

Initialization of metaheuristics: comprehensive review, critical analysis, and research directions

Hybrid PSO6 for hard continuous optimization

EA/G-GA for Single Machine Scheduling Problems with Earliness/Tardiness Costs

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