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Ensemble particle swarm optimizer
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DOI：10.1016/j.asoc.2017.02.007.png)
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摘要
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According to the No Free Lunch (NFL) theorem, there is no single optimization algorithm to solve every problem effectively and efficiently. Different algorithms possess capabilities for solving different types of optimization problems. It is difficult to predict the best algorithm for every optimization problem. However, the ensemble of different optimization algorithms could be a potential solution and more efficient than using one single algorithm for solving complex problems. Inspired by this, we propose an ensemble of different particle swarm optimization algorithms called the ensemble particle swarm optimizer (EPSO) to solve real-parameter optimization problems. In each generation, a self-adaptive scheme is employed to identify the top algorithms by learning from their previous experiences in generating promising solutions. Consequently, the best-performing algorithm can be determined adaptively for each generation and assigned to individuals in the population. The performance of the proposed ensemble particle swarm optimization algorithm is evaluated using the CEC2005 real-parameter optimization benchmark problems and compared with each individual algorithm and other state-of-the-art optimization algorithms to show the superiority of the proposed ensemble particle swarm optimization (EPSO) algorithm. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Ensemble
Particle swarm optimization
Real-parameter optimization
Self-adaptive
Strategy adaptation
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6.6
论文数:
1.4W
被引数:
4.8W
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