in this paper, a new approach is proposed for the optimum design of single-phase induction motor. by using the classical design equations and the evolutionary algorithms such as genetic algorithms (ga), particle swarm optimization (pso) and modified particle swarm optimization (mpso), a single phase induction motor (spim) was designed with the maximum efficiency. the finite element method (fem)...

shear wave velocity is a basic engineering tool required to define dynamic properties of soils. in many instances it may be preferable to determine vs indirectly by common in-situ tests, such as the standard penetration test. many empirical correlations based on the standard penetration test are broadly classified as regression techniques. however, no rigorous procedure has been published for c...

In this age of wireless communication, micro strip antennas have drawn maximum attention of antenna community because of its compact size, light weight and low profile configuration. In this paper the problem of locating feed point of an inset fed microstrip patch antenna designed for wireless communication is dealt with. The optimization is done using three techniques: Genetic Optimization (GA...

In recent years, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are time-varying. In this paper, a triggered memory scheme is introduced into the particle swarm optimization to deal with dynamic environments. The triggered memory scheme enhances traditional memory scheme with a trigger...

In this paper, a technique based on hybrid particle swarm optimiser with breeding and subpopulation is presented for optimal design of reconfigurable dual-beam linear array antennas and planar arrays. In the amplitudephase synthesis, the design of a reconfigurable dual-pattern antenna array is based on finding a common amplitude distribution that can generate either a pencil or sector beam powe...

The particle swarm, which optimizes neural networks, has overcome its disadvantage of slow convergent speed and shortcoming of local optimum. The parameter that the particle swarm optimization relates to is not much. But it has strongly sensitivity to the parameter. In this paper, we applied PSO-BP to evaluate the environmental effect of an agricultural project, and researched application and P...

In this paper, we propose a hybridization of electromagnetism-like mechanism (EM) and particle swarm optimization algorithm (PSO) algorithms to design the proposed functional-link based Petri recurrent fuzzy neural system (FLPRFNS) for application of nonlinear system control. The FLPRFNS has a TSK-type fuzzy consequent part which uses functional-link based orthogonal basis functions and a Petri...

Particle swarm optimization (PSO) is a kind of evolutionary algorithm to find optimal solutions for continuous optimization problems. Updating kinetic equations for particle swarm optimization algorithm are improved to solve traveling salesman problem (TSP) based on problem characteristics and discrete variable. Those strategies which are named heuristic factor, reversion mutant and adaptive no...

Particle Swarm Optimization (PSO) is a metaheuristic optimization algorithm that owes much of its allure to its simplicity and its high effectiveness in solving sophisticated optimization problems. However, since the performance of the standard PSO is prone to being trapped in local extrema, abundant variants of PSO have been proposed by far. For instance, Fuzzy Adaptive PSO (FAPSO) algorithms ...

Distributed generator (DG) placement in distribution system is one of the important tasks for better operation of distribution system. Distribution system has high r/x ratio and high operating current resulting into higher power loss and higher voltage drop. The main objective of this paper is to minimize the active power loss and to improve voltage profile of overall system by optimal sizing a...