DEPSO: hybrid particle swarm with differential evolution operator

By Wenjun Zhang, Xiao-Feng Xie. SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483), 2003

441 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/4859c7b3130d25baaaea5946e40645bf0179ba9e

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Citations in the corpus

A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications (2008, methodology): “Zhang and Xie (2003) also used different techniques in tandem, rather than combining them, in their Differential Evolution (DE) PSO (DEPSO).”
Large scale evolutionary optimization using cooperative coevolution (2008, methodology): “DE has been combined with other algorithms to produce various hybrid algorithms [20,29].”
Accelerating Differential Evolution Using an Adaptive Local Search (2008, background): “Besides, some other hybrids of DE with PSO have also been proposed [32], [33].”
Diversity enhanced particle swarm optimization with neighborhood search (2013, methodology): “In [60], mutations provided by DE algorithm were conducted on the pbest .”
A differential evolution particle swarm optimizer for various types of multi-area economic dispatch problems (2016, methodology): “457 Index Algorithms DEPSO1 DEPSO2 DEPSO3 DEPSO4 IDEPSO1 IDEPSO3 IDEPSO4 HSLSO Best 125299.5631 125179.5581 127386.3364 128641.7046 125594.007 127226.188 127457.4462 125100.2621 Mean 125474.4525 125421.1636 128757.9549 128957.7981 126238.8349 127742.0182 127744.5247 125384.4464 Std 173.9205 157.2532”
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems (2008, methodology): “eliminate the particles falling into local minima (DEPSO) [113], [114], [115] or 2) it can be used to find the optimal parameters (inertia and acceleration constants) for the canonical PSO (composite PSO) [116].”
An improved differential evolution algorithm and its application in optimization problem (2021, background): “mixed PSO and DE to improve the search ability of DE (Zhang and Xie 2003; Hu et al. 2020).”
A comparative study of differential evolution variants for global optimization (2006, methodology): “Two discrete recombination operators are used: binomial and ex- ponential.”
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization (2009, methodology): “The advantage of using two-difference-vectors-based strategies was also discussed in [35] in the particle swarm optimization (PSO) context, which empirically demonstrated that the statistical distribution of the summation of all one-difference vectors had a triangle shape, while the statistical…”
Differential Evolution: A Survey of the State-of-the-Art (2011, methodology): “In [58], Moore and Venayagamoorthy proposed a new hybrid of DE and PSO, which is similar in spirit to the algorithm proposed in [119], but the DE and PSO in it are DE/rand/2/bin and a modified PSO with “Ring” topology respectively.”
Differential Evolution Using a Neighborhood-Based Mutation Operator (2009, background): “Earlier attempts to hybridize DE with different operators of the PSO algorithm may be traced to [40] and [41].”
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”
Adaptive Particle Swarm Optimization (2008, methodology): “, selection [21], crossover [22], and mutation [23], other techniques such as local search [24] and differential evolution [39] have been used to combine with PSO.”
Simulation and Hardware Implementation of New Maximum Power Point Tracking Technique for Partially Shaded PV System Using Hybrid DEPSO Method (2015, methodology): “The DEPSO algorithm keeps individuals from being trapped in the local optima by combining the DE operator with the PSO algorithm, which diversifies the PSO technique [41].”
A Competitive Swarm Optimizer for Large Scale Optimization (2015, background): “Among those variants, adaptation of the parameters [7], [10]–[15], hybridation with other search techniques [16]–[22] and introduction of various topological structures [23]–[29] are three most widely used approaches [30].”
Particle Swarm Optimization With an Aging Leader and Challengers (2013, neutral): “In the literature, various types of search operators have been used for this, including genetic operators such as selection [19], crossover [20], and mutation [12], [21]–[23]; evolutionary computation paradigms such as genetic algorithm (GA) [24], differential evolution [25], and ant colony optimiza”
Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives (2018, methodology): “t the new algorithm required more fitness evaluations and that it would be feasible to use the component swarm based algorithm for problems with computationally heavy fitness functions. Zhang and Xie [65] introduced another variant of a hybrid DE-PSO. Their strategy employed different operations at ”