Citation

Abdul Aziz, Nor Hidayati and Jing Hao, Ooi (2025) Understanding Search Behavior in the Simulated Kalman Filter Algorithm. JOIV : International Journal on Informatics Visualization, 9 (1). p. 128. ISSN 2549-9610

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Abstract

In computational optimization, metaheuristic algorithms are crucial for solving complex and dynamic problems. It is important to fully understand how an algorithm searches, as it helps to improve the algorithm and its applications in various domains. This paper provides a detailed analysis of how the Simulated Kalman Filter algorithm searches for optimal solutions. The SKF algorithm is an optimization method inspired by the Kalman filter estimation techniques. The algorithm was introduced in 2015 to address unimodal problems. Since its inception, the SKF algorithm has undergone improvements and is used to solve a range of optimization problems. Our study aims to bridge the gap in existing research by investigating how SKF effectively balances the search space exploration and known solution exploitation. Through systematic experimentation using the Brown function as a benchmark, we explored the social dynamics and movement style of the SKF algorithm, in addition to the convergence efficiency and accuracy. When we applied the same approach as suggested in the referenced paper, we gained insights into SKF’s unique strengths and limitations of SKF when compared to other algorithms. The findings illustrate SKF’s unique capabilities in handling the exploration-exploitation trade-off. This study helps to set the foundation for creating more advanced algorithms and optimization strategies in the future. Future research will examine how enhancements to the SKF algorithm impact and enhance its search behavior.

Item Type:	Article
Uncontrolled Keywords:	Simulated Kalman Filter Algorithm; Metaheuristic Optimization; Search Behavior Analysis; Optimization Algorithms; Stochastic Optimization
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	10 Apr 2025 02:54
Last Modified:	10 Apr 2025 02:54
URII:	http://shdl.mmu.edu.my/id/eprint/13672

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