In this paper a new effective optimization algorithm called hybrid particle swarm optimizer with breeding and subpopulation is presented. This algorithm is essentially, as PSO and GA, a population-based heuristic search technique, now in use for the optimization of electromagnetic structures, modeled on the concepts of natural selection and evolution (GA) but also based on cultural and social r...

crack identification is a very important issue in mechanical systems, because it is a damage that if develops may cause catastrophic failure. in the first part of this research, modal analysis of a multi-cracked variable cross-section beam is done using finite element method. then, the obtained results are validated usingthe results of experimental modal analysis tests. in the next part, a nove...

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 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)...

This paper presents a proposal based on binary particle swarm optimization to design combinational logic circuits at the gate-level. The algorithm is validated using several examples from the literature, and is compared against a genetic algorithm (with integer representation), and against human designers who used traditional circuit design aids (e.g., Karnaugh Maps). Results indicate that part...

The multiuser detection (MUD) problem was addressed as a pattern classification problem. Due to their strength in solving nonlinear separable problems, radial basis functions, aided by soft particle swarm optimization, were proposed to perform MUD for a synchronous direct sequence code division multiple access system. The proposed solution was shown to exhibit performance better than a number o...

In this paper, a hybrid particle swarm optimization algorithm (HPSOM) that uses the mutation process to improve the standard particle swarm optimization (PSO) algorithm is presented. The main idea of the HPSOM is to integrate the PSO with genetic algorithm mutation method. As a result, the proposed algorithm has the automatic balance ability between global and local searching abilities. The val...

In the present study, in order to predict the activity coefficient of inorganic ions, 12 cases of aqueous chloride solution were considered (AClx=1,2; A=Li, Na, K, Rb, Mg, Ca, Ba, Mn, Fe, Co, Ni). For this study, the UNIQUAC thermodynamic model is desired and its adjustable parameters are optimized with the Genetic + PSO algorithm. The optimization of the UNIQUAC model with PSO+ genetic algorit...

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...

Currently, the researchers have made a lot of hybrid particle swarm algorithm in order to solve the shortcomings that the Particle Swarm Algorithms is easy to converge to local extremum, these algorithms declare that there has been better than the standard particle swarm. This study selects three kinds of representative hybrid particle swarm optimizations (differential evolution particle swarm ...