An active research direction in Particle Swarm Optimization (PSO) is the integration of PSO variants in adaptive, or self-adaptive schemes, in an attempt to aggregate their characteristics and their search dynamics. In this work we borrow ideas from adaptive filter theory to develop an “online” algorithm adaptation framework. The proposed framework is based on tracking the parameters of a multinomial distribution to capture changes in the evolutionary process. As such, we design a multinomial distribution tracker to capture the successful evolution movements of three PSO variants. Extensive experimental results on ten benchmark functions and comparisons with five state-of-the-art algorithms indicate that the proposed framework is competitive and very promising. On the majority of tested cases, the proposed framework achieves substantial performance gain, while it seems to identify accurately the most appropriate algorithm for the problem at hand.
['Michael G. Epitropakis', 'Dimitris K. Tasoulis', 'Nicos G. Pavlidis', 'Vassilis P. Plagianakos', 'Michael N. Vrahatis']
Tracking Particle Swarm Optimizers: An adaptive approach through multinomial distribution tracking with exponential forgetting