The basic thought of particle swarm optimization is introduced firstly, then particle swarm optimization algorithm model is established. The application of the improved particle swarm optimization algorithm to power supply system fault diagnosis is analyzed in accordance with problem of the algorithm, and migration strategy is added to particle swarm optimization algorithm. Finally the paramete...

Regression Testing ensures that any enhancement made to software will not affect specified functionality of software. The execution of all test cases can be long and complex to run; this makes it a costlier process. The prioritization of test cases can help in reduction in cost of regression testing, as it is inefficient to rerun each and every test case. In this research paper, the criterion c...

Solving linear variational inequality by traditional numerical iterative algorithm not only can not satisfy parallel, but also its precision has much relationship with initial values. In this paper, a novel hybrid coevolutionary particle swarm optimization is used to solve linear variational inequality, which sufficiently exerts the advantage of particle swarm optimization such as group search,...

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...

This paper presents an effective hybrid coevolutionary particle swarm optimization algorithm for solving constrained engineering design problems, which is based on simulated annealing (SA) , employing the notion of co-evolution to adapt penalty factors. By employing the SAbased selection for the best position of particles and swarms when updating the velocity in co-evolutionary particle swarm o...

The development of reliable biological models has become an important issue in systems biology. These models are constructed using differential algebraic equations to represent the dynamic perturbation of the biochemical quantities within the cells. However, these models heavily depend on the set of parameters that signify the physiology of the systems such as reaction rates and kinetic constan...

this paper defines how a meta heuristic search engine called p.s.o can be used to maximize the objective function of a logistic regression model, describing the relationship between the response variable (product designs' score) and a set of explanatory variables (product design factors). at the first phase the processed data, classified and categorized, by kansie engineering is used as input t...

in this paper, a stochastic cell formation problem is studied using queuing theory framework and considering reliability. since cell formation problem is np-hard, two algorithms based on genetic and modified particle swarm optimization (mpso) algorithms are developed to solve the problem. for generating initial solutions in these algorithms, a new heuristic method is developed, which always cre...

one of the main aims of water resource planners and managers is to estimate and predict the parameters of groundwater quality so that they can make managerial decisions. in this regard, there have many models developed, proposing better management in order to maintain water quality. most of these models require input parameters that are either hardly available or time-consuming and expensive to...