×

Particle swarm optimization algorithm based on combing example learning and opposition learning. (Chinese. English summary) Zbl 1399.68234

Summary: In order to improve the optimization efficiency of the particle swarm optimization algorithm and prevent the algorithm from trapping into the local optima, based on combing example learning and opposition learning (EOPSO), this paper proposes a PSO Firstly, all non-optimal particles in the particle swarm are updated by a novel example learning mechanism to improve their search ability and to prevent the algorithm from trapping into the local optima. Secondly, the optimal particle is updated by a hybrid opposition learning way to improve its search ability and further avoid the algorithm’s trapping into the local optima. Finally, a self-mutation mechanism is also adopted to update the optimal particle to increase the population diversity. In addition, the self-mutation mechanism adopts an adaptive mutation rate to provide the good global search ability at the early search phase and accelerate the convergence speed at the late search phase in the algorithm process. The simulation experiments are made on 15 benchmark functions with different dimensions. The experiment results show that, compared with the state-of-the-art PSO variants such as ELPSO, SRPSO, LFPSO and HCLPSO, EOPSO obtains better optimization performance.

MSC:

68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
68T05 Learning and adaptive systems in artificial intelligence
90C59 Approximation methods and heuristics in mathematical programming
PDFBibTeX XMLCite
Full Text: DOI