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Parallel cooperative micro-particle swarm optimization: A master-slave model

APPLIED SOFT COMPUTING 2012年第11期12卷 3552-3579页

作者： Parsopoulos, Konstantinos E. Univ Ioannina Dept Comp Sci GR-45110 Ioannina Greece

A parallel master-slave model of the recently proposed cooperative micro-particle swarm optimization approach is introduced. The algorithm is based on the decomposition of the original search space in subspaces of smaller dimension. Each subspace is probed by a subswarm of small size that identifies suboptimal partial solution components. A context vector that serves as repository for the best attained partial solutions of all subswarms is used for the evaluation of the particles. The required modifications to fit the original algorithm within a parallel computation framework are discussed along with their impact on performance. Also, both linear and random allocation of direction components to subswarms are considered to render the algorithm capable of capturing possible correlations among decision variables. The proposed approach is evaluated on two types of computer systems, namely an academic cluster and a desktop multicore system, using a popular test suite. Statistical analysis of the obtained results reveals that, besides the expected run-time superiority of the parallel model, significant improvements in solution quality can also be achieved. Different factors that may affect performance are pointed out, offering intuition on the expected behavior of the parallel model. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Particle swarm optimization Parallel algorithms Cooperative algorithms Master-slave model micro-evolutionary algorithms

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Cooperative micro-Particle Swarm Optimization 09

Cooperative Micro-Particle Swarm Optimization

引用

World Summit on Genetic and evolutionary Computation (GEC 09)

作者： Parsopoulos, Konstantinos E. Univ Patras Dept Math GR-26110 Patras Greece

ISBN: (纸本)9781605583266

Cooperative approaches have proved to be very useful in evolutionary computation due to their ability to solve efficiently high-dimensional complex problems through the cooperation of low-dimensional subpopulations. On the other hand, micro-evolutionary approaches employ very small populations of just a few individuals to provide solutions rapidly. However, the small population size renders them prone to search stagnation. This paper introduces Cooperative micro-Particle Swarm, Optimization, which employs cooperative low-dimensional and low-cardinality subswarms to concurrently adapt different subcomponents of high-dimensional optimization problems. The algorithm is applied on high-dimensional instances of five widely used test problems with very promising results. Comparisons with the standard Particle Swarm Optimization algorithm are also reported and discussed.

关键词： Particle Swarm Optimization Cooperative micro-evolutionary algorithms Swarm Intelligence

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