Particle swarm optimization (PSO) is a well-known algorithm for global optimization over continuous search spaces. However, this algorithm is limited by the intense conflict between exploration and exploitation search processes. This improper adjustment of exploration and exploitation search processes can introduce an inappropriate level of diversity into the swarm, thereby either decelerating ...

Graph coloring problem (GCP) is one of the most studied combinatorial optimization problems. It is important in theory and practice. There have been many algorithms for graph coloring problem, including exact algorithms and (meta-)heuristic algorithms. In this paper, we attempt another meta-heuristic method⎯particle swarm optimization to graph coloring problem. Particle swarm optimization (PSO)...

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 need for solving multi-modal optimization problems in high dimensions is pervasive in many practical applications. Particle swarm optimization (PSO) is attracting an ever-growing attention and more than ever it has foundmany application areas formany challenging optimization problems. It is, however, a known fact that PSO has a severe drawback in the update of its global best (gbest) partic...

The investigation of the performance of the Particle Swarm Optimization (PSO) method in Integer Programming problems, is the main theme of the present paper. Three variants of PSO are compared with the widely used Branch and Bound technique, on several Integer Programming test problems. Results indicate that PSO handles e ciently such problems, and in most cases it outperforms the Branch and Bo...

This paper presents a modified particle swarm optimization (PSO) with simulated annealing (SA) technique. An improved PSO-based artificial neural network (ANN) is developed. The results show that the proposed SAPSO-based ANN has a better ability to escape from a local optimum and is more effective than the conventional PSO-based ANN. r 2004 Elsevier B.V. All rights reserved.

In this paper we show how to design a powerful set of Particle Swarm optimizers to be applied in inverse modeling. The design is based on the interpretation of the swarm dynamics as a stochastic damped mass-spring system, the so-called PSO continuous model. Based on this idea we derived a family of PSO optimizers (GPSO, CC-PSO and CP-PSO) having different exploitation and exploration capabiliti...

BACKGROUND Rod failure has been reported clinically in pedicle subtraction osteotomy (PSO) to correct flat back deformity. OBJECTIVE To characterize the fatigue life of posterior screw-rod constructs in the setting of PSO as a function of the severity of rod contour angle. METHODS A modified ASTM F1717 to 04 was used. Rods were contoured to the appropriate angle for the equivalent 20-, 40-,...

In vitro and in vivo studies have shown that punicic acid, a type of conjugated fatty acid and the main constituent of pomegranate seed oil (PSO), has anti-atherogenic effects. The present study aimed at determining the effect of PSO treatment on serum lipid profiles. This double-blind placebo-controlled randomised clinical trial included fifty-one hyperlipidaemic subjects, diagnosed according ...