A centralised cooperative strategy for continuous optimisation: The influence of cooperation in performance and behaviour

  • Authors:

  • Antonio David Masegosa;David Alejandro Pelta;José Luis Verdegay

  • Affiliations:

  • Models of Decision and Optimisation Research Group, Center for Research on ICT, University of Granada, 18071 Granada, Spain and Models of Decision and Optimisation Research Group, Department of Co ...;Models of Decision and Optimisation Research Group, Center for Research on ICT, University of Granada, 18071 Granada, Spain and Models of Decision and Optimisation Research Group, Department of Co ...;Models of Decision and Optimisation Research Group, Center for Research on ICT, University of Granada, 18071 Granada, Spain and Models of Decision and Optimisation Research Group, Department of Co ...

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2013

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Abstract

The necessity of developing high-performance resolution methods for continuous optimisation problems has given rise to the emergence of cooperative strategies which combine different self-contained metaheuristics that exchange information among them. However, the majority of the proposals found in the literature make use of population-based algorithms and/or employ a cooperation scheme with a pipeline or decentralised information flow. In this work we proposed a centralised cooperative strategy, where a set of trajectory-based methods are controlled by a rule-driven coordinator. In this context, we also present a new analysis that allows to study the behaviour induced by a determined type of cooperation in the strategy. A comprehensive experimentation has been accomplished over CEC2005 and CEC2008 benchmarks in order to assess the performance of the method with different cooperation schemes. The results show that these cooperation schemes, apart from having a different performance, lead the strategy to distinct exploration and exploitation patterns. In addition, the proposed method presents competitive results with respect to state-of-the-art algorithms for both benchmarks.