Diversity enhanced particle swarm optimization with neighborhood search

By Hui Wang, Hui Sun, Changhe Li, Shahryar Rahnamayan, Jeng‐Shyang Pan. Information Sciences, 2013

410 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/01c9330af094b7fba0f2d355d043270176b97614

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In Artificial Intelligence

In Computational Theory and Mathematics

In Control and Systems Engineering

Citations in the corpus

Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review (2017, methodology): “An algorithm called “Diversity enhanced with Neighborhood Search Particle Swarm Optimization,” DNSPSO, was proposed (Wang et al., 2013) in which the explorative behavior was controlled by enhancing the diversity of the particles in the swarm.”
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications (2015, positive): “[91] proposed a hybrid PSO algorithm called”
Feature selection for high-dimensional classification using a competitive swarm optimizer (2016, background): “In order to enhance the performance of PSO, a number of PSO variants have been proposed, including the parameter adaptation based variants [44,20], the new topological structure based variants [26,9], and the hybridization based variants [7,37], to name a few.”
Opposition based learning: A literature review (2017, neutral): “Several algorithms have utilized the various concepts of OBL by the proposed scheme in [210] in their initialization and during updating steps [246,108,109,339,285,115,318,166,347,374, 324,65,70,71,180,378,321,153,273,133,34,145,343,82,160,353,325, 346,336,103,12,4341,369,200,304,335,16,317,112,53,2”
Self-Adaptive Particle Swarm Optimization for Large-Scale Feature Selection in Classification (2019, background): “(Wang et al. 2013), which is described as follows: x t+1id = r1 ∗ x t id + r2 ∗ pдd + r3 ∗ ( x tad − x t bd ) , (13) where x t ad and x t bd are position vectors of two random particles.”
Metaheuristics in large-scale global continues optimization: A survey (2015, methodology): “A hybrid PSO algorithm [186] was proposed which is equipped with a new diversity method and neighborhood search strategies.”
Particle swarm optimization algorithm: an overview (2017, methodology): “…2002), Bayesian optimization model (Monson and Seppi 2005), chemical reaction optimization (Li et al. 2015b), neighborhood search mechanism (Wang et al. 2013), collision-avoiding mechanism (Blackwell and Bentley 2002), information sharing mechanism (Li et al. 2015a), local search technique…”
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”