A particle filtering-based estimation of distribution algorithm for multi-objective optimisation. (2016)

• Record Type: Journal Article
• Authors: Shi, Xiaoran; Celik, Nurcin
• Is Part Of: International journal of simulation and process modelling. Volume 11:Number 3/4(2016)
• Page Start: 176
• Page End: 191
• Publication Date: 2016
• Subjects: particle filtering -- estimation of distribution algorithm -- EDA -- multi-objective optimisation; Management -- Computer simulation -- Periodicals; Mathematical models -- Periodicals; Operations research -- Periodicals; Simulation methods -- Periodicals; 003.05
• Languages: English
• ISSNs: 1740-2123

Abstract

A novel particle filtering-based estimation of distribution algorithm (EDA) is proposed to address multi-objective optimisation problems. Specifically, the particles drawn from a sampling distribution are considered as the candidate solutions. This sampling distribution is computed recursively based on the performance of the prior particle set and the newly arrived observations. As the iteration progresses, the distribution function gradually concentrates on the promising region(s) of the solution space, indicating higher probabilities to obtain solutions with good performances in terms of the objective values. In order to validate the performance of the proposed algorithm, a case study of an environmental economic load dispatch (EELD) is conducted where the bi-objective EELD optimisation problem is solved via the proposed algorithm, and the performance of the proposed algorithm is benchmarked against several algorithms studied in the literature. Experimental results have revealed that the proposed algorithm produces very promising results against those in the literature.