Dynamic cognitive-social particle swarm optimization

Kassoul, Khelil (Haute école de gestion de Genève, HES-SO // Haute Ecole Spécialisée de Suisse Occidentale) ; Belhaouari, Samir Brahim (Hamad Bin Khalifa University, Doha, Qatar) ; Cheikhrouhou, Naoufel (Haute école de gestion de Genève, HES-SO // Haute Ecole Spécialisée de Suisse Occidentale)

Particle Swarm Optimization (PSO) is a heuristic optimization algorithm based on the modeling of the behavior of fishes and birds flock. This paper proposes a better version of PSO, named Dynamic Cognitive-Social PSO “DCS-PSO”, for global minima search by introducing optimal and dynamic cognitive and social scaling parameters without taking into consideration the inertia term. Furthermore, the velocity of each particle is controlled systematically at each iteration to avoid local minimum traps and to converge quickly and reliably towards the global minimum. The proposed algorithm is more suitable for high dimensional optimization problems and it has gotten over the limitations of classical Particle Swarm Optimization. Several experiments have been carried out, using the proposed DCS-PSO, to optimize thirteen benchmark functions and an important improvement has been observed, not only in terms of reaching the best global solutions but also in terms of convergence speed, compared to the existing benchmark approaches.


Keywords:
Conference Type:
published full paper
Faculty:
Economie et Services
School:
HEG - Genève
Institute:
CRAG - Centre de Recherche Appliquée en Gestion
Subject(s):
Economie/gestion
Publisher:
Prague, Czech Republic, 4-6 February 2021
Date:
2021-02
Prague, Czech Republic
4-6 February 2021
Pagination:
6 p.
Published in:
Proceedings of the 7th International Conference on Automation, Robotics and Applications (ICARA)
DOI:
ISBN:
978-1-6654-0469-3
Appears in Collection:

Note: The file is under embargo until: 2023-02-04


 Record created 2021-03-26, last modified 2021-03-26

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