Two improved differential evolution schemes for faster global search

By Swagatam Das, Amit Konar, Uday Kumar Chakraborty. Annual Conference on Genetic and Evolutionary Computation, 2005

393 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/6eb6a1e543c874271217911f0cb0786090d462ae

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Citations in the corpus

DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization (2010, background): “Das et al. [24] proposed two variants of DE, DERSF and DETVSF, that use varying scale factors.”
Recent advances in differential evolution - An updated survey (2016, background): “Instead of some prior works which mainly were based on randomization of these parameters without any learning mechanism (see for example [36]), most of these recent studies try to involve some form of learning from the past instances of successes/failures while adapting F and Cr.”
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization (2009, background): “Inappropriate choice of strategies and parameters may lead to premature convergence or stagnation , which have been extensively demonstrated in [8], [16], [17], [24], and [29].”
Extracting Optimal Policies of Hydropower Multi-Reservoir Systems Utilizing Enhanced Differential Evolution Algorithm (2017, background): “Although the new mutation strategy could improve the local and global search capability, it could not avoid trapping a local optimal, especially in high dimensional problems (Das et al. 2005).”
Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm (2012, methodology): “…to solve arise due to the rapid development of technology, we still need development of new optimization algorithms (Caponio et al., 2007, 2009; Das et al., 2005, 2009; Epitropakis et al., 2011; Fan and Lampinen, 2003a,2003b; Mininno et al., 2011; Neri et al., 2011; Neri and Tirronen, 2010;…”
LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems (2017, methodology): “Alternatively, the parameters values are changing with the progress of generations using increasing / decreasing linear or nonlinear deterministic function such as [12, 14].”
Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives (2008, methodology): “In [ 32 ], deflection and stretching techniques as well as a repulsion technique.”
Opposition-Based Differential Evolution (2008, result): “The proposed idea in this section is similar to Das et al. work [ 20 ].”
Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters (2011, methodology): “[14] introduced two schemes to adapt the scaling factor F in DE.”
Accelerating Differential Evolution Using an Adaptive Local Search (2008, neutral): “[37] have proposed two variants of”
Recent advances in differential evolution: a survey and experimental analysis (2010, methodology): “A simple modification has been proposed to handle noisy problems in Das and Konar (2005) and Das et al. (2005).”
Enhancing Differential Evolution Utilizing Proximity-Based Mutation Operators (2011, neutral): “In the original DE algorithm, the mutation factor F is a fixed and user defined parameter, while in many adaptive DE variants each individual is associated with a different adaptive mutation factor [23], [26]–[31], [33], [37], [38].”
Differential evolution with multi-population based ensemble of mutation strategies (2016, methodology): “In other words, the inappropriate selection of strategies and parameters may lead to a premature convergence, stagnation, or a waste of computational resources [13,16,27,38,65].”