Adapting Particle Swarm Optimizationto Dynamic Environments

By A. Carlisle, G. Dozier. 2001

301 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a36f95cef25133e3cb0f1592e993c5c6270149f9

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Citations in the corpus

An analysis of particle swarm optimizers (2002, methodology): “Experiments performed by Carlisle and Dozier indicated that the PSO may have trouble tracking a non-stationary minimiser [16].”
Parallel global optimization with the particle swarm algorithm (2004, methodology): “Although several modifications to the original swarm algorithm have been made to improve performance [12, 13, 14, 15, 16] and adapt it to specific types of problems [6, 17, 18], a parallel version has not been previously implemented.”
Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients (2007, methodology): “The issue of adapting the PSO parameters has also been addressed in [ 2 ,3,34], for SOO problems.”
Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives (2018, result): “This was reported by Kennedy [22] and subsequently the subpar performance of the model was confirmed by the works of Carlisle and Dozier [23].”
Multi-swarm Optimization in Dynamic Environments (2004, background): “Recently, the application of PSO to dynamic problems has been explored [18, 15, 12, 1].”
A review of particle swarm optimization. Part I: background and development (2007, methodology): “At the time, experiments were only conducted on a single test function, but the weight has been applied many times since, and is generally considered useful (Carlisle and Dozier 2000; Trelea 2003).”
Adaptive Particle Swarm Optimization (2008, background): “In addition, departing from mutation [23], reset [25], [26], or reinitialization [27], [28] operations, the ELS is pro-”
Multiswarms, exclusion, and anti-convergence in dynamic environments (2006, background): “Most of them assume that the time of a change in the environment is known to the algorithm, or can be detected, e.g., by a reevaluation of the objective function at one or several of the attractors [16], [23].”
Particle swarm optimization algorithm: an overview (2017, background): “Later, Carlisle and Dozier (2000) used a random point in the search space to determine whether the environment changed or not, but it requiredcentralizedcontrol,anditwasinconsistentwithPSO algorithm’s distributed processing model.”
Fitness-distance-ratio based particle swarm optimization (2003, background): “However, if the global optimum for the problem doesn’t lie on a path between original particle positions and such a local optimum, then this convergence behavior prevents effective search for the global optimum.”
A Study of Global Optimization Using Particle Swarms (2005, methodology): “Recent contributions by, amongst others, Carlisle and Dozier [ 12 ] and Eberhart and Shi [13], include modifications which allow the PSOA to track a changing extremum over time in a dynamic environment.”