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 presents an efficient and reliable swarm intelligence-based approach, namely elitist-mutated particle swarm optimization EMPSO technique, to derive reservoir operation policies for multipurpose reservoir systems. Particle swarm optimizers are inherently distributed algorithms, in which the solution for a problem emerges from the interactions between many simple individuals called par...

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...

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 study proposes a novel bacterial foraging swarm-based intelligent algorithm called the bacterial foraging particle swarm optimization (BFPSO) algorithm to design vector quantization (VQ)-based fuzzy-image compression systems. It improves compressed image quality when processing many image patterns. The BFPSO algorithm is an efficient evolutionary learning algorithm that manages complex glo...

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...

Efficient multiprocessor scheduling is essentially the problem of allocating a set of computational jobs to a set of processors to minimize the overall execution time. The main issue is how jobs are partitioned in which total finishing time and waiting time is minimized. Minimization of these two criteria simultaneously, is a multi objective optimization problem. There are many variations of th...