Comparative Analysis of Binary Particle Swarm Optimization on Dynamic Value Methods for Cognitive and Social Aspects and Its Implementation in Hyper-Heuristic

Safan Capri(1*), Oscar Edward Guijaya(2), Antonius Filian Beato Istianto(3), Antoni Wibowo(4),

(1) Bina Nusantara University, Jakarta, Indonesia
(2) Bina Nusantara University, Jakarta, Indonesia
(3) Bina Nusantara University, Jakarta, Indonesia
(4) Bina Nusantara University, Jakarta, Indonesia
(*) Corresponding Author

Abstract


Particle Swarm Optimization (PSO) is a population-based optimization which include the use of cognitive and social terms. The cognitive term is represented with the variable of c1 while social term is represented with the variable of c2. Both values can be assigned between 0 and 1. The contribution of this research is to compare which role is superior in the Binary Particle Swarm Optimization (BPSO) metaheuristic with Dynamic Increase Cognitive Decrease Social (DICDS) and Dynamic Decrease Cognitive Increase Social (DDCIS) methods, as well as its implementation in the Modified Multi-Objective Agent-Based Hyper-Heuristic (MOABHH). The experiments were carried out 30 times on data set 2 from [1]. The result is that the DDCIS method is 0.4% better in objective value than the DICDS method. This is also proven with the average of number of solutions in the DDCIS method which is more 2.3 solutions than the DICDS method based on the evaluation results carried out by Modified MOABHH. In addition, Modified MOABHH which is run simultaneously with the DICDS and DDCIS methods provides better objective value results of 0.6% compared to the average of both results for each of these methods which are run separately.


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References


K. Augustin, “Multi-Objective Optimization untuk Penjadwalan Kru Penerbangan menggunakan Algoritma Hybrid Genetic Algorithm dan Particle Swarm Optimization”, M.I.T. Thesis, Bina Nusantara University, Jakarta, Indonesia, (2021).

S. Wang, J. Zhu, X. Shen, Q. Wang, R. Shu and J. Cai, “Research on Particle Swarm Optimization Algorithm”, Journal of Physics: Conference Series, vol. 1827, no. 012151, (2021).

J. Kennedy and R. Eberhart, “Particle Swarm Optimization”, Proceedings of ICNN'95 - International Conference on Neural Networks, Perth, Australia, (1995) November 27-December 01.

H. Gao, Y. Yang, X. Zhang, C. Li, Q. Yang and Y. Wang, “Dimension Reduction for Hyperspectral Remote Sensor Data Based on Multi-Objective Particle Swarm Optimization Algorithm and Game Theory”, Sensors, vol. 19(6), no. 1327, (2019).

A. Gupta and S. Srivastava, “Comparative Analysis of Ant Colony and Particle Swarm Optimization Algorithms for Distance Optimization”, Procedia Computer Science, vol. 173, (2020), pp. 245-253.

S. Talukder, “Mathematical Modelling and Applications of Particle Swarm Optimization”, M.Sc. Thesis, School of Engineering at Blekinge Institute of Technology, Karlskrona, Sweden, (2011).

F. van den Bergh, “An Analysis of Particle Swarm Optimizers”, Ph.D. Thesis, University of Pretoria, Pretoria, South Africa, (2001).

V. R. de Carvalho, “Using Multi-Agent Systems and Social Choice Theory to Design Hyper-Heuristics for Multi-Objective Optimization Problems”, Ph.D. Thesis, Escola Politécnica of Universidade de São Paulo, São Paulo, Brazil, (2022).

A. Hassanat, K. Almohammadi, E. Alkafaween, E. Abunawas, A. Hammouri and V. B. S. Prasath, “Choosing Mutation and Crossover Ratios for Genetic Algorithms-A Review with a New Dynamic Approach”, Information, vol. 10(12), no. 390, (2019).




DOI: https://doi.org/10.30645/kesatria.v4i4.264

DOI (PDF): https://doi.org/10.30645/kesatria.v4i4.264.g262

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