We investigate the runtime of the Binary Particle Swarm Optimization (PSO) algorithm introduced by Kennedy and Eberhart (1997). The Binary PSO maintains a global best solution and a swarm of particles. Each particle consists of a current position, an own best position and a velocity vector used in a probabilistic process to update the particle’s position. We present lower bounds for a broad cla...

Although Particle Swarm Optimization (PSO) is used in variety of applications; it has limitations in the training phase. In this work, a new enhancement for PSO is proposed to overcome such limitations. The proposed PSO optimization consists of two stages. In the first stage, a Gaussian Maximum Likelihood (GML) is added to PSO to update the last 25% of swarm particles, while in the second stage...

Swarm-diversity is an important factor influencing the global convergence of particle swarm optimization (PSO). In order to overcome the premature convergence, the paper introduced a negative feedback mechanism into particle swarm optimization and developed an adaptive PSO. The improved method takes advantage of the swarm-diversity to control the tuning of the inertia weight (PSO-DCIW), which i...

Particle Swarm Optimization (PSO) is an innovative and competitive optimization technique for numerical optimization with real-parameter representation. This paper examines the working mechanism of PSO in a principled manner with forma analysis and investigates the applicability of PSO on the Quadratic Assignment Problem (QAP). Particularly, the derived PSO operator for QAP is empirically studi...

In this paper an extensive empirical analysis of recently introduced Particle Swarm Optimization algorithm with Convergence Related parameters (CR-PSO) is presented. The algorithm is tested on extended set of benchmarks and the results are compared to the PSO with time-varying acceleration coefficients (TVAC-PSO) and the standard genetic algorithm (GA). Key-Words: Global Optimization, Particle ...

Particle swarm optimization (PSO) and fast evolutionary programming (FEP) are two widely used population-based optimisation algorithms. The ideas behind these two algorithms are quite different. While PSO is very efficient in local converging to an optimum due to its use of directional information, FEP is better at global exploration and finding a near optimum globally. This paper proposes a no...

Population topology of particle swarm optimization (PSO) will directly affect the dissemination of optimal information during the evolutionary process and will have a significant impact on the performance of PSO. Classic static population topologies are usually used in PSO, such as fully connected topology, ring topology, star topology, and square topology. In this paper, the performance of PSO...

This paper proposes a new Shrinking Hypersphere PSO (SHPSO) for continuous function optimization. The global best and personal best fly in the search space in the form of a hypersphere instead of particles. The hyperspheres keep on shrinking as the iterations proceed and the velocity and position update equations are applied at each iteration. The theoretical convergence of the SHPSO is proved....

In this paper, the behavior of different Particle Swarm Optimization (PSO) variants is analyzed when solving a set of well-known numerical constrained optimization problems. After identifying the most competitive one, some improvements are proposed to this variant regarding the parameter control and the constraint-handling mechanism. Furthermore, the on-line behavior of the improved PSO and som...

Although Particle Swarm Optimizers (PSO) have been successfully used in a wide variety of continuous optimization problems, their use has not been as widespread in discrete optimization problems, particularly when adopting non-binary encodings. In this chapter, we discuss three PSO variants (which are applied on a specific scheduling problem: the Single Machine Total Weighted Tardiness): a Hybr...