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Metaheuristic Algorithm Based on Hybridization of Invasive Weed Optimization asnd Estimation Distribution Methods

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Recent Metaheuristic Computation Schemes in Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 948))

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Abstract

Hybrid metaheuristic methods combine approaches extracted from different techniques to build a single optimization method. The design of such systems represents a current trend in the metaheuristic optimization literature. In hybrid algorithms, the objective is to extend the potential advantages of the integrated approaches and eliminates their main drawbacks. In this chapter, a hybrid method for solving optimization problems is presented. The presented approach combines (1) the explorative characteristics of the invasive weed optimization (IWO) method, (2) the probabilistic models of the estimation distribution algorithms (EDA), and (3) the dispersion capacities of a mixed Gaussian-Cauchy distribution to produce its own search strategy. With these mechanisms, the method conducts an optimization strategy over search areas that deserve a special interest according to a probabilistic model and the fitness value of the existent solutions. In the presented method, each individual of the population generates new elements around its own location, dispersed according to the mixed distribution. The number of new elements depends on the relative fitness value of the individual regarding the complete population. After this process, a group of promising solutions is selected from the set compound by the (1) new elements and the (2) original individuals. Based on the selected solutions, a probabilistic model is built from which a certain number of members (3) is sampled. Then, all the individuals of the sets (1), (2), and (3) are joined in a single group and ranked in terms of their fitness values. Finally, the best elements of the group are selected to replace the original population. This process is repeated until a termination criterion has been reached. To test the performance of the method, several comparisons to other well-known metaheuristic methods has been conducted. The comparison consists of analyzing the optimization results over different standard benchmark functions within a statistical framework. Conclusions based on the comparisons exhibit the accuracy, efficiency, and robustness of the presented approach.

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Authors and Affiliations

  1. CUCEI, Universidad de Guadalajara, Guadalajara, Mexico

    Erik Cuevas & Alma Rodríguez

  2. Facultad de Ingeniería, Universidad Panamericana, Zapopan, Jalisco, Mexico

    Alma Rodríguez, Avelina Alejo-Reyes & Carolina Del-Valle-Soto

  3. Centro de Eneñanza Técnica Industrial, Universidad de Guadalajara, Guadalajara, Mexico

    Alma Rodríguez

Authors
  1. Erik Cuevas
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  2. Alma Rodríguez
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  3. Avelina Alejo-Reyes
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  4. Carolina Del-Valle-Soto
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Correspondence to Erik Cuevas .

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Cuevas, E., Rodríguez, A., Alejo-Reyes, A., Del-Valle-Soto, C. (2021). Metaheuristic Algorithm Based on Hybridization of Invasive Weed Optimization asnd Estimation Distribution Methods. In: Recent Metaheuristic Computation Schemes in Engineering. Studies in Computational Intelligence, vol 948. Springer, Cham. https://doi.org/10.1007/978-3-030-66007-9_3

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