| Summary: | 碩士 === 國立中央大學 === 電機工程學系 === 107 === With the development of artificial intelligence and the need of automatic reasoning, humans have created a great number of algorithms in computer science. Various forms of algorithms complete troublesome and difficult tasks automatically in different fields. The improved algorithm in the thesis is the particle swarm optimization in the multi-particle search in the optimization algorithm. Particle swarm optimization uses a mobile approach that mimics bird foraging to perform optimal searches. Through the guiding of the particle best solutions and the group best solution, the particles can effectively and quickly converge to a local optimum. We modify the existing particle swarm optimization algorithm to achieve a better performance through simple and important improvements without increasing the complexity of the programming implementation. An improved algorithm called Jumping Particle Swarm Optimization based on adaptive inertia weight is proposed. This particle swarm optimization method combines position formula improvements and inertia weight design to improve the accuracy of the solution. It is suitable for the implementation of multi-particle searches. There are two versions of position formula improvements: P-PSO and G-PSO. The weight design also has adaptive inertia weight and normal distribution cumulative decreasing inertia weight. Users can decide the combination to be used according to their own needs. The thesis also discusses the influence of the initial velocity on the iterations, allowing users to use different initial velocities in different situations. Finally, through the experimental simulation, we verify the effect and performance of the proposed methods. Among the 16 test functions, the jumping particle swarm algorithm has the best performance in most functions, which enables users to achieve an excellent performance in implementation and application.
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