Comparison Of Particle Swarm And Differential Evolution Optimization Algorithms Considering Various Benchmark Functions
Abstract
This study aims to compare Particle Swarm
Optimization (PSO) and Differential Evolution (DE) methods for various input
parameters. Both optimization methods show high performance in optimization of
any physical system including simple and complex constraints and objectives.
Average and standard values of both methods were evaluated by utilizing 8
benchmark functions and a graphical representation and comparison of
corresponding methods was presented for 50x50 and
100x100 population sizes and dimensionalities. It is concluded that DE and PSO
show the best fitness value for Sum of Different Powers benchmark function for
both number of populations. Approach to the optimum is found to be
faster through the PSO method. Both methods are flexible to be used for simple
and complex engineering problems with high performances with ease of
programming.
Keywords
References
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Details
Primary Language
Turkish
Subjects
-
Journal Section
Research Article
Authors
Publication Date
December 20, 2017
Submission Date
October 2, 2016
Acceptance Date
-
Published in Issue
Year 2017 Volume: 20 Number: 4
Cited By
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https://doi.org/10.35234/fumbd.1105174