Browsing by Author "Strelitz, Benjamin Steenveld"

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    Particle swarm optimization for constrained multimodal function optimization
    (Stellenbosch : Stellenbosch University, 2024-03) Strelitz, Benjamin Steenveld; Engelbrecht, Andries; Stellenbosch University. Faculty of Science. Dept. of Computer Science.
    ENGLISH ABSTRACT: This thesis investigates the efficiency of particle swarm optimization (PSO) algorithms at finding many feasible global optima for constrained multimodal optimization prob- lems. The proposed approach is the niching migratory multi-swarm optimizer with Deb's comparison criteria (NMMSO-DCC) algorithm. The NMMSO-DCC algorithm uses the same core architecture as the niching migratory multi-swarm optimization (NMMSO) al- gorithm, but uses Deb's comparison criteria as a constraint handling method. Deb's com- parison criteria allows the NMMSO-DCC algorithm to find many feasible global optima for constrained multimodal optimization problems (CMMOPs), whereas the NMMSO algorithm was designed only to find global optima for boundary constrained multimodal optimization problems (MMOPs). The NMMSO algorithm is one of the state-of-the-art multiomodal optimization algorithms, but cannot be used when constraints are placed on the objective function. Thus, the proposed algorithm addresses the inability of the NMMSO algorithm to solve constrained multimodal optimization problems. This study assumes that the objective function to be optimized remains static throughout the search process. This study also assumes that the constraints placed upon the objective func- tion remain static during the search process. All benchmark problems in this study contain boundary constraints. The results indicate that the NMMSO-DCC performs competitively compared to other state-of-the-art constrained multimodal optimization algorithms. The results in terms of success rate are particularly convincing, whereas NMMSO-DCC struggled more with respect to the peak ratio. This means that although the NMMSO-DCC algorithm is able to locate all global optima within a given tolerance level in some of the independent runs, it struggles to do so consistently across multiple independent runs.