Local and global search based PSO algorithm

Abstract: In this paper, a new algorithm for particle swarm optimisation (PSO) is proposed. In this algorithm, the particles are divided into two groups. The two groups have different focuses when all the particles are searching the problem space. The first group of particles will search the area around the best experience of their neighbours. The particles in the second group are influenced by the best experience of their neighbors and the individual best experience, which is the same as the standard PSO. Simulation results and comparisons with the standard PSO 2007 demonstrate that the proposed algorithm effectively enhances searching efficiency and improves the quality of searching.Originally presented at Fourth International Conference on Swarm Intelligence (ICSI 2013), Harbin, China, 12-15, June, 2013

Generalized predictive control based on particle swarm optimization for linear/nonlinear process with constraints

Abstract: This paper presents an intelligent generalized predictive controller (GPC) based on particle swarm optimization (PSO) for linear or nonlinear process with constraints. We propose several constraints for the plants from the engineering point of view and the cost function is also simplified. No complicated mathematics is used which originated from the characteristics ofPSO. This method is easy to be used to control the plants with linear or/and nonlinear constraints. Numerical simulations are used to show the performance of this control technique for linear and nonlinear processes, respectively. In the first simulation, the control signal is computed based on an adaptive linear model. In the second simulation, the proposed method is based on a fixed neural network model for a nonlinear plant. Both of them show that the proposed control scheme can guarantee a good control performance

Planeamiento dinámico de la expansión de los sistemas de transmisión usando algoritmos meméticos

El objetivo del Planeamiento Dinámico de la Expansión de los Sistemas de Transmisión (PDEST) es determinar qué infraestructura eléctrica, en qué lugar y en qué momento debe ser agregada a un sistema eléctrico de potencia dentro de un horizonte de tiempo determinado. La adición de nuevos componentes al sistema procura satisfacer el crecimiento de la demanda, y cumplir con parámetros de eficiencia, calidad de servicio, confiabilidad y economía, dentro del horizonte de planeamiento. En este trabajo de investigación se propone e implementa un método de solución usando una técnica basada en Algoritmos Meméticos, los cuales resuelven el problema del PDEST combinando técnicas de solución basadas en población y búsqueda local. El algoritmo propuesto combina la técnica de Enjambre de Partículas (PSO) y la técnica de búsqueda local Hill Climbing. Los sistemas de prueba para obtener los resultados fueron los sistemas Garver y el IEEE de 24 nodos. Los escenarios de prueba para estos sistemas fueron con y sin redespacho.The objective of Dynamic Transmission Expansion Planning (DTEP) is to determine what electrical infrastructure, where and when it should be added to a power system within a given time horizon. The addition of new components to the system seeks to meet the growth in demand, and to comply with parameters of efficiency, quality of service, reliability and economy, within the planning horizon. In this research work, a solution method is proposed and implemented using a technique based on Memetic Algorithms, which solves the DTEP problem by combining solution techniques based on population and local search. The proposed algorithm combines the Particle Swarm Optimization (PSO) technique and Hill Climbing local search technique. The test systems to obtain the results were the Garver and the 24-node IEEE systems. The test scenarios for these systems were with and without redispatch.Ingeniero EléctricoCuenc