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A States of Matter Algorithm for Global Optimization

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Advances of Evolutionary Computation: Methods and Operators

Part of the book series: Studies in Computational Intelligence ((SCI,volume 629))

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Abstract

The ability of an Evolutionary Algorithm (EA) to find a global optimal solution depends on its capacity to find a good relation between exploitation of the found-so-far elements and exploration of the search space. Inspired by natural phenomena, researchers have developed many successful evolutionary algorithms. In their original versions, such approaches define operators that mimic the way in which nature solves complex problems overlooking the exploration–exploitation balance. In this chapter, a novel nature-inspired algorithm called the States of Matter Search (SMS) is introduced. The evolutionary process is divided into three phases which emulate the three states of matter: gas, liquid and solid. In each state, the evolving elements, which are modeled as molecules, exhibit different movement capacities. As a result, the approach can substantially improve the balance between exploration–exploitation, yet preserving the good search capabilities of an EA. To illustrate the proficiency and robustness of the presented algorithm, it was compared with other well-known evolutionary methods including recent variants that incorporate diversity preservation schemas. Experimental results and examples show that the presented method achieves a good performance over its counterparts as a consequence of its better exploration–exploitation capabilities.

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Authors and Affiliations

  1. Departamento de Electrónica, CUCEI, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico

    Erik Cuevas, Margarita Arimatea Díaz Cortés & Diego Alberto Oliva Navarro

  2. Institut für Informatik, Freie Universität Berlin, Berlin, Germany

    Margarita Arimatea Díaz Cortés

  3. Tecnológico de Monterrey, Campus Guadalajara, Zapopan, Mexico

    Diego Alberto Oliva Navarro

Authors
  1. Erik Cuevas
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  2. Margarita Arimatea Díaz Cortés
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  3. Diego Alberto Oliva Navarro
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Correspondence to Erik Cuevas .

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Cuevas, E., Díaz Cortés, M.A., Oliva Navarro, D.A. (2016). A States of Matter Algorithm for Global Optimization. In: Advances of Evolutionary Computation: Methods and Operators. Studies in Computational Intelligence, vol 629. Springer, Cham. https://doi.org/10.1007/978-3-319-28503-0_3

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  • DOI: https://doi.org/10.1007/978-3-319-28503-0_3

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  • Publisher Name: Springer, Cham

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