rainfall-runoff modeling is most important component in the water resource management of river basins. the successful application of a conceptual rainfall-runoff model depends on how well it is calibrated. the degree of difficulty in solving the global optimization method is generally dependent on the dimensionality of the model and certain of the characteristics of object function. the purpose...

In this paper we introduce an improvement in the path planning algorithm for the humanoid soccer playing robot which uses Ferguson splines and PSO (Particle Swarm Optimization). The objective of the algorithm is to find a path through other playing robots to the ball, which should be as short as possible and also safe enough. Ferguson splines create preliminary paths using random generated para...

In data grid, using reservation is accepted to provide scheduling and service quality. Users need to have an access to the stored data in geographical environment, which can be solved by using replication, and an action taken to reach certainty. As a result, users are directed toward the nearest version to access information. The most important point is to know in which sites and distributed sy...

this paper addresses the problem of minimizing the sum of maximum earliness and tardiness on identical parallel machines scheduling problem. each job has a processing time and a due date. since this problem is trying to minimize and diminish the values of earliness and tardiness, the results can be useful for just–in-time production systems. it is shown that the problem is np-hard. using effici...

Abstract— Particle Swarm Optimization (PSO) algorithm is swarm intelligence based algorithm which is used for solving optimization problem. PSO simulates the intelligent foraging behavior of a flock of birds. This paper presents a modification in PSO and develops an algorithm called Highly Convergent Particle Swarm Optimization (HCPSO) algorithm, in which the velocity of particle is made to be ...

Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem. Methods: In this...