Nonlinear systems analysis (2nd ed.)
Nonlinear systems analysis (2nd ed.)
Swarm intelligence
Digital Control Systems
Automatic Control Systems
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Understanding particle swarms through simplification: a study of recombinant PSO
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
A review of particle swarm optimization. Part I: background and development
Natural Computing: an international journal
Natural Computing: an international journal
Theoretical Analysis of Initial Particle Swarm Behavior
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Particle swarm optimization with preference order ranking for multi-objective optimization
Information Sciences: an International Journal
Mean and variance of the sampling distribution of particle swarm optimizers during stagnation
IEEE Transactions on Evolutionary Computation
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
Cellular particle swarm optimization
Information Sciences: an International Journal
Self-adaptive learning based particle swarm optimization
Information Sciences: an International Journal
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Stability analysis of the particle dynamics in particle swarm optimizer
IEEE Transactions on Evolutionary Computation
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
IEEE Transactions on Evolutionary Computation
Performance assessment of foraging algorithms vs. evolutionary algorithms
Information Sciences: an International Journal
Ockham's Razor in memetic computing: Three stage optimal memetic exploration
Information Sciences: an International Journal
Information Sciences: an International Journal
Diversity enhanced particle swarm optimization with neighborhood search
Information Sciences: an International Journal
Engineering Applications of Artificial Intelligence
Compact Particle Swarm Optimization
Information Sciences: an International Journal
Computational Intelligence and Neuroscience
Computational Optimization and Applications
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Particle Swarm Optimization (PSO) is arguably one of the most popular nature-inspired algorithms for real parameter optimization at present. The existing theoretical research on PSO focuses on the issues like stability, convergence, and explosion of the swarm. However, all of them are based on the gbest (global best) communication topology, which usually is susceptible to false or premature convergence over multi-modal fitness landscapes. The present standard PSO (SPSO 2007) uses an lbest (local best) topology, where a particle is stochastically attracted not towards the best position found in the entire swarm, but towards the best position found by any particle in its topological neighborhood. This article presents a first step towards a probabilistic analysis of the particle interaction and information exchange in an lbest PSO with variable random neighborhood topology (as found in SPSO 2007). It addresses issues like the distribution of particles over neighborhoods, the probability distributions of the social and cognitive terms in lbest model, and the explorative power of the lbest PSO. It also presents a state-space model of the lbest PSO and draws important conclusions regarding the stability and convergence of the particle dynamics in the light of control theory.