Bare Bones PSO was proposed by Kennedy as a model of PSO dynamics. Dependence on velocity is replaced by sampling from a Gaussian distribution. Although Kennedy’s original formulation is not competitive to standard PSO, the addition of a component-wise jumping mechanism, and a tuning of the standard deviation, can produce a comparable optimisation algorithm. This algorithm, Bare Bones with Jump...

Particle Swarm Optimization (PSO) is a swarm intelligence search method based on the behavior of birds flocking and fish schooling. It is known for its ability to perform fast computation compared to other evolutionary computational methods like Genetic Algorithms. Several parameter control methods have been developed to make the PSO algorithm faster and more accurate such as linearly decreasin...

This paper represents a Particle Swarm Optimization (PSO) algorithm, for grid job scheduling. PSO is a population-based search algorithm based on the simulation of the social behavior of bird flocking and fish schooling. Particles fly in problem search space to find optimal or near-optimal solutions. In this paper we used a PSO approach for grid job scheduling. The scheduler aims at minimizing ...

Particle swarm optimization (PSO) algorithm can be viewed as a series of iterative matrix computation and its population diversity can be considered as an observation of the distribution of matrix elements. In this paper, PSO algorithm is first represented in the matrix format, then the PSO normalized population diversities are defined and discussed based on matrix analysis. Based on the analys...

In this paper, an effective hybrid algorithm based on Particle Swarm Optimization (PSO) is proposed for solving the Traveling Salesman Problem (TSP), which is a well-known NPcomplete problem. The hybrid algorithm combines the high global search efficiency of fuzzy PSO with the powerful ability to avoid being trapped in local minimum. In the fuzzy PSO system, fuzzy matrices were used to represen...

improving mechanical properties of composites based on strength-to-weight ratio has been gained much attention in various applications. having the most resistant and at the same time the lightest and the most economical structure is believed as an aim in these applications. these three factors are usually opposed to each other. so, applying optimization algorithms for improving mechanical prope...

A new promising strategy for the PSO (Particle swarm optimization) algorithm is proposed and described in this paper. This new strategy presents alternative way of assigning new velocity to each individual in particle swarm (population). This new multiple choice particle swarm optimization (MC-PSO) algorithm is tested on two different shifted test functions to show the performance on problems t...

Simplified forms of the particle swarm algorithm are very beneficial in contributing to understanding how a particle swarm optimization (PSO) swarm functions. One of these forms, PSO with discrete recombination, is extended and analyzed, demonstrating not just improvements in performance relative to a standard PSO algorithm, but also significantly different behavior, namely, a reduction in burs...

Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the alg...

Particle swarm optimization (PSO) is a new evolutionary computation technique. Although PSO algorithm possesses many attractive properties, the methods of selecting inertia weight need to be further investigated. Under this consideration, the inertia weight employing random number uniformly distributed in [0,1] was introduced to improve the performance of PSO algorithm in this work. Three bench...