Digital filters have found important applications in an increasing number of fields in science and engineering, and design techniques have been developed to achieve desired filter characteristics. This paper presents an optimization technique for the design of optimal digital FIR low pass filter. The design of digital FIR filters possible by solving a system of linear equations. In this paper, ...

The genetic algorithm (GA) is an evolutionary optimization algorithm operating based upon reproduction, crossover and mutation. On the other hand, particle swarm optimization (PSO) is a swarm intelligence algorithm functioning by means of inertia weight, learning factors and the mutation probability based upon fuzzy rules. In this paper, particle swarm optimization in association with genetic a...

in practical situations, distribution network loads are the mixtures of residential, industrial, and commercial types. this paper presents a hybrid optimization algorithm for the optimal placement of shunt capacitor banks in radial distribution networks in the presence of different voltage-dependent load models. the algorithm is based on the combination of genetic algorithm (ga) and binary part...

nowadays using of new energies in the form of dispersed resources in the worlds is wide spreading. in this article we will design a dispersed production source in the form of a solar/wind hybrid power plant in order to supply the energy of a residential unit according to a sample load pattern. the aim of aforementioned design is to reduce its costs in a period of 20 years. in order to optimi...

fuzzy time series have been developed during the last decade to improve the forecast accuracy. many algorithms have been applied in this approach of forecasting such as high order time invariant fuzzy time series. in this paper, we present a hybrid algorithm to deal with the forecasting problem based on time variant fuzzy time series and particle swarm optimization algorithm, as a highly effici...

This paper describes an evolutionary strategy called PSOGA-NN, which uses Neural Network (NN) for selfadaptive control of hybrid Particle Swarm Optimization and Adaptive Plan system with Genetic Algorithm (PSO-APGA) to solve large scale problems and constrained real-parameter optimization. This approach combines the search ability of all optimization techniques (PSO, GA) for stability of conver...

This paper proposes a hybrid particle swarm approach called Simple Multi-Objective Particle Swarm Optimizer (SMOPSO) which incorporates Pareto dominance, an elitist policy, and two techniques to maintain diversity: a mutation operator and a grid which is used as a geographical location over objective function space. In order to validate our approach we use three well-known test functions propos...