A study of particle swarm optimization particle trajectories

By Frans van den Bergh, Andries Petrus Engelbrecht. Information Sciences, 2006

1,386 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/270d24da13010c775f3b09b9fc53b2e612fc97aa

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Citations in the corpus

Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems (2019, background): “[72], this behavior can guarantees that a Swarm-basedmethod eventually converges to a point in search space.”
Diversity enhanced particle swarm optimization with neighborhood search (2013, background): “Bergh and Engelbrecht [2] presented a comprehensive study on the parameters of PSO, and provided a formal proof that each particle converges to a stable point.”
A New Particle Swarm Algorithm and Its Globally Convergent Modifications (2011, methodology): “In [17] and [18], it was assumed that attractors were fixed and coefficients were constant so as to perform a detailed movement analysis of one particle; then, a conclusion that the PSO algorithm was not guaranteed to converge to the global optimum was drawn.”
Enhancing particle swarm optimization using generalized opposition-based learning (2011, background): “Bergh and Engelbrecht [1] proposed a cooperative approach to PSO (CPSO-H) for solving multimodal problems.”
Self-adaptive learning based particle swarm optimization (2011, methodology): “investigate the influence of parameters, such as the inertia term, from the perspective of particle trajectories for general swarms [5].”
The Ant Lion Optimizer (2015, neutral): “[69], this behaviour can guarantee that an algorithm eventually convergences to a point and searches locally in a search space.”
Particle Swarm Methods (2018, background): “The first theoretically sound convergence analysis of PSO in [21] was succeeded by a number of theoretical studies [49, 54, 113, 124, 143, 147].”
Measuring exploration/exploitation in particle swarms using swarm diversity (2008, background): “Such a swarm can be said to converge [14], [15] when:”
Swarm Intelligence in Optimization (2008, neutral): “In a more recent work [172], Bergh and Engelbrecht generalized the above analysis by including the inertia weight w, and also provided a formal convergence proof of particles in this representation.”
Harris hawks optimization: Algorithm and applications (2019, background): “Referring to Van Den Bergh and Engelbrecht 49 [59], these activities can guarantee that a P-metaheuristic finally 50 convergences to a position and exploit the target region.”
Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems (2022, background): “Berg et al. [57] have mentioned that the unexpected change in the movement of search agents of the initial steps of the optimization algorithm is required.”
Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients (2007, methodology): “[16]. This is inspired by the flocking behavior of birds, which is very simple to simulate and has been found to be quite efficient in handling the single objective optimization (SOO) problems [12, 32 ].”