GRA Vacancy : Metaheuristic Optimization Research in MMU
University / Institute *
Multimedia University (Melaka campus)
Research Center/Lab (optional)
Website URL for the Research Center/Lab (optional)
A Novel Simulated Kalman Filter for Optimization Problems
Funding Agency *
Ministry of Higher Education (MOHE)
Ministry of Science, Technology and Innovation (MOSTI)
Grant Name *
Name of Grant [e.g. PRGS, ERGS, TRGS, Science Fund, Techno Fund, etc.]
Project Code from Awarding Agency (For internal use only; will not be published online) *
Contact Email(s) *
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Contact Phone(s) *
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06-2523280 / 0133976272
Nationality Requirements on Candidate(s) *
Applicants must be Malaysian citizens
Open to all nationalities
Describe the project in sufficient details, the requirements on the candidates, the type and amount of financial support provided, and other relevant details that can help to potential candidate assess their suitability.
Optimization is often required in solving all engineering problems. Exact optimization methods normally fail to solve complex nonlinear and multimodal problems that exist in most real-world applications in reasonable computational time. Thus, metaheuristic optimization methods are often sought to solve these kinds of problems. Although there are many metaheuristics optimization methods developed to solve all kinds of optimization problems, there is always a possibility to develop a new algorithm that would outperform other algorithms for solving some specific optimization problems. In this research proposal, a novel population-based metaheuristic optimization algorithm called Simulated Kalman Filter (SKF) is proposed. This new algorithm is inspired by the estimation capability of Kalman Filter. The principle of this new algorithm is different from the existing algorithms because SKF search for the global minimum/maximum based on the prediction-measurement-correction principle of a Kalman Filter. It is expected that the SKF algorithm will perform better than existing metaheuristic algorithms. Performance analysis will be carried out to justify the strength of the proposed mechanism against existing metaheuristic algorithms. It is hoped that the findings of this research will give a high impact in this field and later will be further employed as a mechanism to solve real-world optimization problems.