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Incorporating the Avoidance Behavior to the Standard Particle Swarm Optimization 2011ALTINOZ, O. T. , YILMAZ, A. E. , DUCA, A. , CIUPRINA, G.
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particle swarm optimization, social factors, cognitive informatics, performance evaluation
swarm(14), optimization(14), systems(5), evolutionary(5), computation(5)
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About this article
Date of Publication: 2015-05-31
Volume 15, Issue 2, Year 2015, On page(s): 51 - 58
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.02007
Web of Science Accession Number: 000356808900007
SCOPUS ID: 84979834398
Inspired from social and cognitive behaviors of animals living as swarms; particle swarm optimization (PSO) provides a simple but very powerful tool for researchers who are dealing with collective intelligence. The algorithm depends on modeling the very basic random behavior (i.e. exploration capability) of individuals in addition to their tendency to revisit positions of good memories (cognitive behavior) and tendency to keep an eye on and follow the majority of swarm members (social behavior). The balance among these three major behaviors is the key of success of the algorithm. On the other hand, there are other social and cognitive phenomena, which might be useful for improvement of the algorithm. In this paper, we particularly investigate avoidance from the bad behavior. We propose modifications about modeling the Standard PSO 2011 formulation, and we test performance of our proposals at each step via benchmark functions, and compare the results of the proposed algorithms with well-known algorithms. Our results show that incorporation of Social Avoidance behavior into SPSO11 improves the performance. It is also shown that in case the Social Avoidance behavior is applied in an adaptive manner at the very first iterations of the algorithm, there might be further improvements.
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 Energy Efficient Control of High Speed IPMSM Drives - A Generalized PSO Approach, GECIC, M., KAPETINA, M., MARCETIC, D., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 1, Volume 16, 2016.
Digital Object Identifier: 10.4316/AECE.2016.01004 [CrossRef] [Full text]
 Quantum-behaved PSO with neighborhood strategies for solving NDET inverse problems, Duca, Laurentiu-Cristian, Ioan, Daniel, Popescu, Cornel, 2017 10th International Symposium on Advanced Topics in Electrical Engineering (ATEE), ISBN 978-1-5090-5160-1, 2017.
Digital Object Identifier: 10.1109/ATEE.2017.7905178 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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