Dongping Tian
ABSTRACT
As one of the most classical intelligent algorithms, particle swarm optimization (PSO) has been extensively used to deal with various optimization issues. Compared with other algorithms, PSO has merit of being simple to implement, but at the same time, it is difficult to get tradeoff between global and local search, so it does not handle optimization problems with multi-peaks well, and it is easy to entrap into local optimal. Hence, an adaptive PSO based on competitive and balanced learning strategy (APSO-CB) is put forward in the work. First, chaotic inertia weight and time-varying acceleration factors are leveraged in exploration state to speed up convergence process. Second, the competitive and balanced learning mechanism is exploited to update velocity for particles, competition and average mechanism can alleviate the situation that PSO is easy to entrap into local optimal in late stage of evolution. Next, adaptive location renewal mechanism is applied to trade off exploration and exploitation. Finally, APSO-CB is tested based on CEC2013 test suite and compared with five competitive PSOs. Experiments indicate that APSO-CB markedly outperforms other PSO variants.
CCS CONCEPTS • Computing methodologies • Artificial intelligence • Search methodologies
Index Terms
- Adaptive Particle Swarm Optimization based on Competitive and Balanced Learning Strategy
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