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Performance comparison of different swarm intelligence methods towards benchmark functions

Song, Wen Huan (2020) Performance comparison of different swarm intelligence methods towards benchmark functions. Final Year Project, UTAR.

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    Optimization problems are associated with different kinds of complicated and constraints which make optimization still being so important until today. This is because optimization is able to help researchers and organisations reached an optimal solution on different research works or applications using limited resources. In the past 20 years, Swarm Intelligence (SI) methods have been trendy in solving different kinds of complex problems. However, researchers or organisations still did not consider on the performance of the SI methods as there are various SI methods and not everyone contains the knowledge on the methods. Hence, the objective of this research is to analyse different Particle Swarm Optimization (PSO) models and to identify the best method in SI. The original version of PSO, Inertia Weight PSO (IWPSO), Linearly Decrease Inertia Weight PSO (LDIW-PSO), Random Inertia Weight PSO (RIW-PSO), Constriction Factor PSO (CF-PSO) along with and without velocity clamping (VC) are analyzed and compared with Grey Wolf Optimizer (GWO) and Bat Algorithm (BA). The performance of SI method is tested using ten benchmark functions. The results in Experiment 1 show that CF-PSO with VC is performed more significant compared to the other PSO models. Hence, it is considered as the best PSO model in Experiment 1. Therefore, Experiment 2 is conducted and compared with GWO and BA using CF-PSO with VC. The results in Experiment 2 also reveal that CF-PSO with VC is the best SI method when it is compared towards the other SI methods. The result produced can help researchers to acknowledge and have better understanding on the SI methods so that better performance SI method with good accuracy can be applied on their research.

    Item Type: Final Year Project / Dissertation / Thesis (Final Year Project)
    Subjects: Q Science > Q Science (General)
    Divisions: Faculty of Information and Communication Technology > Bachelor of Computer Science (Hons)
    Depositing User: ML Main Library
    Date Deposited: 07 Jan 2021 14:59
    Last Modified: 07 Jan 2021 15:00
    URI: http://eprints.utar.edu.my/id/eprint/3912

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