On the computation of all global minimizers through particle swarm optimization

By Konstantinos E. Parsopoulos, Michael N. Vrahatis. IEEE Transactions on Evolutionary Computation, 2004

715 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/130b5e15ff803a4f8a6504d25f450de7d58acb06

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Citations in the corpus

Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives (2008, methodology): “An asymmetrical initialization procedure has been used here following the work reported in [29].”
Self regulating particle swarm optimization algorithm (2015, methodology): “The techniques include embedding the unique evolutionary operators: selection, crossover and mutation inside PSO [43, 25], differential evolution [80, 19], diversity enhancing mechanism [66, 72], avoidance mechanism [53], combination of multi-crossover with bee colony mechanism [44], PSO with levy f”
Particle Swarm Optimization With an Aging Leader and Challengers (2013, methodology): “In addition, to avoid locating previously detected solutions, techniques such as deflection, stretching, repulsion [32], self-organizing [12], [33], and dissipative methods [34] have also been used in the hybrid PSO algorithms.”
Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review (2017, background): “, personal best and global best vectors, it might keep oscillating between the two) (Parsopoulos and Vrahatis, 2004) and “two steps forward, one step back”(see Section 4.”
Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation (2015, neutral): “By using CL learning strategy and its velocity update equation [14], the particles can learn from different dimensions of different particles and avoid two problems, “ oscillation phenomenon “ [28] and “ two steps forward, one step back phenomenon “ [25].”
Multiswarms, exclusion, and anti-convergence in dynamic environments (2006, background): “Parsopoulos and Vrahatis [34] use repulsion to keep particles away from already detected optima in an attempt to detect new ones.”
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions (2006, neutral): “In [28], deﬂection, stretching, and repulsion techniques are used to ﬁnd as many minima as possible by preventing particles from moving to a previously discovered minimal region.”
PSO, an effective tool for harmonics elimination and optimization in multi-level inverters (2009, background): “Because of odd quarter-wave symmetric characteristic, harmonics with even order become zero.”
Particle swarm optimization algorithm: an overview (2017, methodology): “In order to obtain several better solutions for the optimized problem, Parsopoulos and Vrahatis (2004) used deﬂection, stretching andrepulsionandothertechniquestoﬁndasmanyaspossible minimum points by preventing the particles from moving to the minimum area ever found before.”
Orthogonal Learning Particle Swarm Optimization (2009, background): “For example, in one case, it may cause an “oscillation” phenomenon [17] because the guidance of the two experiences may be in opposite directions.”
Multiobjective robust optimization method for drawbead design in sheet metal forming (2010, background): “neutral network, pattern recognition, fuzzy system control and other fields [36–41].”
Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions (2009, background): “The objective of this class of multimodal optimization problems is to find multiple optima having either equal or unequal function values (Brits et al. 2002; Kennedy 2000; Parsopoulos and Vrahatis 2004).”
Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives (2018, methodology): “This approach was improved by the introduction of the Deflection and Repulsion techniques in [170] by Parsopoulous and Vrahatis.”
Synthesis of Circular Antenna Array Using Crossed Particle Swarm Optimization Algorithm (2006, background): “in 1995, which is an evolutionary algorithm based on the swarm intelligence [2, 3], which is motivated from the simulation of social behavior.”