1. There are now two categories of particles, active (moving and interacting kinetically) and passive (remaining still and inactive). An active particle holds a recipe of the swarm (i.e., a list of kinetic parameter sets) in it (Fig. 1(a)). 2. A recipe is transmitted from an active particle to a passive particle when they collide, making the latter active (Fig. 1(b)). 3. The activated particle ...

This paper presents an algorithm of particle swarm optimization with reduction for global optimization problems. Particle swarm optimization is an algorithm which refers to the collective motion such as birds or fishes, and a multi-point search algorithm which finds a best solution using multiple particles. Particle swarm optimization is so flexible that it can adapt to a number of optimization...

A particle-swarm is a set of indivisible processing elements that traverse a network in order to perform a distributed function. This paper will describe a particular implementation of a particle-swarm that can simulate the behavior of the popular PageRank algorithm in both its global-rank and relative-rank incarnations. PageRank is compared against the particleswarm method on artificially gene...

This paper has been inspired by two quite different works in the field of Particle Swarm theory. Its main aims are to obtain particle swarm equations via genetic programming which perform better than hand-designed ones on the group-foraging problem, and to provide insight into behavioural ecology. With this work, we want to start a new research direction: the use of genetic programming together...

While the robot is inmotion, path planning should follow the three aspects: (1) acquire the knowledge from its environmental conditions. (2) determine its position in the environment and (3) decision-making and execution to achieve its highest-order goals. The present research work aims to develop an efficient particle swarm optimizationbased path planner of an autonomous mobile robot. In this ...

In this paper, we analyze the behavior of particle swarm optimization (PSO) on the facet of particle interaction. We firstly propose a statistical interpretation of particle swarm optimization in order to capture the stochastic behavior of the entire swarm. Based on the statistical interpretation, we investigate the effect of particle interaction by focusing on the social-only model and derive ...

Vehicle routing problem is a NP hard problem. To solve the premature convergence problem of the particle swarm optimization, an improved particle swarm optimization method was proposed. In the first place, introducing the neighborhood topology, defining two new concepts lepton and hadron. Lepton are particles within the scope of neighborhood, which have weak interaction between each other, so t...

in this paper, the static var compensator (svc) has been used to improve dynamic behaviour of power system. to do this, a new objective function is formulated considering power loss reduction, voltage profile improvement and loadability margin decrease. other contribution of this research is proposing a novel structure for particle swarm optimization (pso) algorithm through modifying the initia...

the issue of unit commitment is one of the most important economic plans in power system. in modern and traditional power systems, in addition to being economical of the planning, the issue of security in unit operation is also of great importance. hence power system operation confronts units’ participation and input considering network security constrains. the issue of units’ participation is ...

this paper presents an efficient hybrid method, namely fuzzy particleswarm optimization (fpso) and fuzzy c-means (fcm) algorithms, to solve the fuzzyclustering problem, especially for large sizes. when the problem becomes large, thefcm algorithm may result in uneven distribution of data, making it difficult to findan optimal solution in reasonable amount of time. the pso algorithm does find ago...