Parallel Differential Evolution with Variable Population Size for Global Optimization

• Iztok Fister ^{[1] [3]} ; Andres Iglesias ^{[2] [3]} ; Akemi Galvez ^{[2] [3]} ; Dušan Fister ^[1]
  1. [1] University of Maribor

     University of Maribor

     Eslovenia

     
  2. [2] Toho University

     Toho University

     Japón

     
  3. [3] Universidad de Cantabria

     Universidad de Cantabria

     Santander, España

     

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• Localización: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020): Burgos, Spain ; September 2020 / coord. por Álvaro Herrero Cosío , Carlos Cambra Baseca , Daniel Urda Muñoz , Javier Sedano Franco , Héctor Quintián Pardo , Emilio Santiago Corchado Rodríguez , 2021, ISBN 978-3-030-57802-2, págs. 89-99
• Idioma: inglés
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• Resumen

  • The results of evolutionary algorithms depends on population diversity that normally decreases by increasing the selection pressure from generation to generation. Usually, this can lead evolution process to get stuck in local optima. The study is focused on mechanisms to avoid this undesired phenomenon by introducing parallel differential evolution that decompose a monolithic population into more variable-sized subpopulations, which evolve independently of each other. The proposed parallel algorithm operates with individuals having some characteristics of agents, e.g., they act autonomously by selecting actions, with which they affect the state of environment. This incorporates two additional mechanisms: aging, and adaptive population growth, which direct the decision-making by individuals. The proposed parallel differential evolution was applied to the CEC’18 benchmark function suite, while the produced results were compared with some traditional stochastic natureinspired population-based and state-of-the-art algorithms.