Improvement Strategies for Multi-swarm PSO in Dynamic Environments

• Pavel Novoa-Hernández ^[1] ; David A. Pelta ^[2] ; Carlos Cruz Corona ^[2]
  1. [1] Universidad de Holguín

     Universidad de Holguín

     Cuba

     
  2. [2] Universidad de Granada

     Universidad de Granada

     Granada, España

     
• Localización: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) / coord. por Juan R. González, David Alejandro Pelta Mochcovsky, Carlos Cruz, Germán Terrazas, Natalio Krasnogor, 2010, ISBN 978-3-642-12537-9, págs. 371-383
• Idioma: inglés
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• Resumen

  • Many real world optimization problems are dynamic, meaning that their optimal solutions are time-varying. In recent years, an effective approach to address these problems has been the multi-swarm PSO (mPSO). Despite this, we believe that there is still room for improvement and, in this contribution we propose two simplestrategies to increase the effectiveness of mPSO. The first one faces the diversity loss in the swarm after an environment change; while the second one increases the efficiency through stopping swarms showing a bad behavior. From the experiments performed on the Moving Peaks Benchmark, we have confirmed the benefits of our strategies.