Integrating Particle Swarm Optimization with Stochastic Point Location method in noisy environment

Document Type

Conference Proceeding

Publication Date

2-6-2017

Abstract

Particle Swarm Optimization (PSO) deteriorates when facing a high-noise environment. To address this issue, one popular mechanism is the resampling method that is based on re-evaluations to find the true fitness value. However, the budget for re-evaluations in PSO is limited. In this paper, we intend to integrate a Stochastic Point Location (SPL) method into PSO to alleviate the impacts of noise on the evaluation of true fitness. SPL deals with the problem of a learning mechanism locating a target point on the line in noisy environment. Up to now, Adaptive Step Searching is the fastest algorithm in solving the SPL problem and shows great anti-noise performance. This paper investigates two effective hybrid PSO approaches, by integrating PSO and PSO-Equal Resampling with Adaptive Step Searching. The simulation results and comparisons on 20 large-scale benchmark optimization functions in noisy environments demonstrate the superiority of the proposed approaches in terms of optimization accuracy and convergence rate.

Identifier

85015749273 (Scopus)

ISBN

[9781509018970]

Publication Title

2016 IEEE International Conference on Systems Man and Cybernetics Smc 2016 Conference Proceedings

External Full Text Location

https://doi.org/10.1109/SMC.2016.7844544

First Page

2067

Last Page

2072

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