The Artificial Bee Colony Algorithm (ABC) is a heuristic optimization method based on the foraging behavior of honey bees. It has been confirmed that this algorithm has good ability to search for the global optimum, but it suffers from the fact that the global best solution is not directly used, but the ABC stores it at each iteration, unlike the particle swarm optimization (PSO) that can direc...

Live virtual migration is a way for achieving system load balancing in a cloud environment by transferring an active VM from one physical host to another. This way has been developed to decrease the downtime for migrating overloaded VMs, but it still consumes timeand cost, and a huge amount of memory is involved in this migration process. To overcome these drawbacks, we propose a Load Balancing...

As a novel stochastic optimization technique, the Particle Swarm Optimization technique (PSO) has gained much attention towards several applications during the past decade for solving the global optimization problem or to set up a good approximate solution to the given problem with a high probability. PSO was first introduced by Eberhart and Kennedy [Kennedy and Eberhart, 1997]. It belongs to t...

The basic Particle Swarm Optimization (PSO) algorithm and its principle have been introduced, the Particle Swarm Optimization has low accelerate speed and can be easy to fall into local extreme value, so the Particle Swarm Optimization based on the improved inertia weight is presented. This method means using nonlinear decreasing weight factor to change the fundamental ways of PSO. To allow ful...

BACKGROUND CpG islands have been demonstrated to influence local chromatin structures and simplify the regulation of gene activity. However, the accurate and rapid determination of CpG islands for whole DNA sequences remains experimentally and computationally challenging. METHODOLOGY/PRINCIPAL FINDINGS A novel procedure is proposed to detect CpG islands by combining clustering technology with...

Presenting a satisfactory and efficient training algorithm for artificial neural networks (ANN) has been a challenging task in the supervised learning area. Particle swarm optimization (PSO) is one of the most widely used algorithms due to its simplicity of implementation and fast convergence speed. On the other hand, Cuckoo Search (CS) algorithm has been proven to have a good ability for findi...

Particle swarm Optimization (PSO) is mainly inspired by social behavior patterns of organisms that live and interact within large groups. The term PSO refers to a relatively new family of algorithms that is used to find optimal or near to optimal solutions to numerical and qualitative problems. It is an optimization paradigm that simulates the ability of human to process knowledge. The capabili...

This paper presents use of Particle Swarm Optimization (PSO) algorithm introduced by Kennedy and Eberhart [1] for solving Integer and Mixed Integer Optimization problems. In PSO, The potential solutions, called particles, are flown through the problem space by learning from the current optimal particle and its memory. PSO is started with a group of feasible solutions and a feasibility function ...

In this paper, a two-phase hybrid particle swarm optimization (PSO) approach is used to solve optimal reactive power dispatch (ORPD) problem. In this hybrid approach, PSO is used to explore the optimal region and direct search is used as local optimization technique for finer convergence. The performance of the proposed hybrid approach is demonstrated with the IEEE 30-bus and IEEE 57-bus system...

Applying particle swarm optimization (PSO) algorithm has become a widespread heuristic technique in many fields of engineering. In this paper, we apply PSO algorithm in additive white Gaussian noise (AWGN) and multipath fading channels. In the proposed method, PSO algorithm was applied to solve joint multiuser and inter-symbol interference (ISI) suppression problems in the code-division multipl...