Abstract
Particle Swarm Optimization (PSO) was built by mimicking the navigation pattern of entities, such as flock of birds or school of fishes. The algorithm uses established particles that wing over a search space for global optima location. Throughout the PSO iteration process, each particle updates its location based on the preceding knowledge or experience as well as the knowledge obtained from the neighborhood search. Detailed information on PSO and swarming behaviour of creatures is presented. Application of PSO in numerical optimization was implemented in the software MATLAB. The PSO convergence characteristic is presented, with best fitness function value obtained with the PSO model was –170 corresponding to the updated gbest (5 –5 5).
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