Multi Particle Swarm Optimization Algorithm Research Articles

MPSO-based PID control design for power factor correction in an AC-DC boost converter

This paper presents the implementation for the first time of a Multi-Particle Swarm Optimization (MPSO) algorithm in the tuning of a PID controller for Power Factor Correction (PFC), applied to a 100W AC-DC boost converter. MPSO algorithm navigates in a search space where each dimension of the space corresponds to the controller constants (Proportional, Integral, Derivative and the Derivative Filter), prioritizing communication over exploration in the algorithm. The controller parameters are randomly initialized in a reduced sector of the space [,,,], to optimize the search for a PID solution. In the first step, the algorithm is validated using a simulation model in Simulink and Matlab. Subsequently, a final implementation using a real converter is implemented with the PID tuned by MPSO, improving the PFC obtained in previous work. Although previous works have used evolutionary algorithms applied to heuristic optimization to tunning PID controllers, the MPSO algorithm is not usually used for this purpose, particularly to tunning a PID controller in a power electronics system. One advantage of MPSO over the PSO classical algorithm is the search at different points if the vectorial field looks for an optimal solution. PSO presents problems such as getting stuck in a locally optimal solution. The PID controller is trained offline, with the advantage of allowing the risk of damage in the Boost converter for transitory response, increasing the performance of the Power Factor Correction in the converter. This research opens the possibility to use the extended version of the PSO bioinspired algorithm to tune offline controllers to improve the power converter's performance, minimizing the risk presented in the real-time tuning process.

Design of Two-Dimensional Recursive Digital Filter Using Multi Particle Swarm Optimization Algorithm

Particle Swarm Optimization (PSO) is an evolutionary algorithm widely used in optimization problems. It is characterized by a fast convergence, which can lead the algorithm to stagnate in local optima. In the present paper, a new Multi-PSO algorithm for the design of two-dimensional infinite impulse response (IIR) filters is built. It is based on the standard PSO and uses a new initialization strategy. This strategy is relayed to two types of swarms: a principal and auxiliaries. To improve the performance of the algorithm, the search space is divided into several areas, which allows a best covering and leading to a better exploration in each zone separately. This solved the problem of fast convergence in standard PSO. The results obtained demonstrate the effectiveness of the Multi-PSO algorithm in the filter coefficients optimization.

Application of Multimodal PSO to Energy Supply Systems

In many engineering applications, it is necessary to find more than one solution to an optimization problem with complex multi dimensional objective function. For example, energy supply systems require the most attractive solution in cost but also a solution near to the actual regime among multiple solutions. Metaheuristics is paid to attention as a method for solving such a problem. The parallel searching Niche PSO (Particle Swarm Optimization) algorithm can find multiple solutions in multi dimensional problems in an acceptable time limit. In this paper, we propose the repetitive searching Multi PSO algorithm which is based on PSO and detects more precise solutions in the same time. Furthermore, we show the results of applying Multi PSO and Niche PSO to energy supply systems, and evaluate the validities of these methods.