The purpose of this research is predicting the stock prices using the Particle Swarm Optimization Algorithm and Box-Jenkins method. In this way, the information of 165 corporations is collected from 2001 to 2016. Then, this research considers price to earnings per share and earnings per share as main variables. The relevant regression equation was created using two variables of earnings per sha...

This paper presented a new particle swarm optimization based on evolutionary game theory (EPSO) for the traveling salesman problem (TSP) to overcome the disadvantages of premature convergence and stagnation phenomenon of traditional particle swarm optimization algorithm (PSO). In addition ,we make a mapping among the three parts discrete particle swarm optimization (DPSO)、 evolutionary game the...

The particle swarm optimization technique is one of the promising tools to find a proper optimum for an unknown function optimization. Especially, global search capability of the method is very powerful. The particle swarm optimization utilizes common knowledge of the group and individual experiences effectively. That is, direction for the best estimator that a particle has ever reached, direct...

in this paper, the static var compensator (svc) has been used to improve dynamic behaviour of power system. to do this, a new objective function is formulated considering power loss reduction, voltage profile improvement and loadability margin decrease. other contribution of this research is proposing a novel structure for particle swarm optimization (pso) algorithm through modifying the initia...

the issue of unit commitment is one of the most important economic plans in power system. in modern and traditional power systems, in addition to being economical of the planning, the issue of security in unit operation is also of great importance. hence power system operation confronts units’ participation and input considering network security constrains. the issue of units’ participation is ...

this paper presents an efficient hybrid method, namely fuzzy particleswarm optimization (fpso) and fuzzy c-means (fcm) algorithms, to solve the fuzzyclustering problem, especially for large sizes. when the problem becomes large, thefcm algorithm may result in uneven distribution of data, making it difficult to findan optimal solution in reasonable amount of time. the pso algorithm does find ago...

permanent magnet synchronous motors are efficient motors, which have widespread applications in electric industry due to their noticeable features. one of the interesting applications of such motors is in underwater vehicles. in these cases, reaching to minimum volume and high torque of the motor are the major concern. design optimization can enhance their merits considerably, thus reduce volum...

This paper presents an algorithm of particle swarm optimization with reduction for global optimization problems. Particle swarm optimization is an algorithm which refers to the collective motion such as birds or fishes, and a multi-point search algorithm which finds a best solution using multiple particles. Particle swarm optimization is so flexible that it can adapt to a number of optimization...

The aim of this paper is to propose a new particle swarm optimization algorithm to solve a hybrid flowshop scheduling with sequence-dependent setup times problem, which is of great importance in the industrial context. This algorithm is called diversified particle swarm optimization algorithm which is a generalization of particle swarm optimization algorithm and inspired by an anarchic society ...