Recent advances in differential evolution: a survey and experimental analysis

By Ferrante Neri, Ville Tirronen. Artificial Intelligence Review, 2010

945 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/523226f53a5395749f5e77ebcac987e134afd201

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Citations in the corpus

A survey on optimization metaheuristics (2013, background): “For a more detailed description of many of the existing variants and major application areas of DE, readers should refer to [43,194,63].”
Recent advances in differential evolution - An updated survey (2016, background): “In order to present the flavor of the huge and multi-faceted literature on DE, in 2010, [134] reviewed a number of DE-variants for the single-objective optimization problems and also made an experimental comparison of these variants on a set of standard benchmark functions.”
Back propagation neural network with adaptive differential evolution algorithm for time series forecasting (2015, methodology): “…the maximum iteration number GenM is set to 50; the accuracy requirement l is set to 0.19; and the crossover rate CR is 0.1; Fmin and Fmax of ADE are also set according to the recommendation of (Neri & Tirronen, 2010; Qu et al., 2013; Wang, Qu, Liu, & Chen, 2014), i.e., Fmin = 0.2, and Fmax = 0.9.”
Extracting Optimal Policies of Hydropower Multi-Reservoir Systems Utilizing Enhanced Differential Evolution Algorithm (2017, background): “DE showed to be accurate, fast and robust, but with faster convergence yields in a higher probability searching space towards a local optimum or premature convergence (Neri and Tirronen 2010).”
LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems (2017, background): “Finally, the performance of DE deteriorates significantly when the problems of premature convergence and/or stagnation occur [8, 9].”
Success-history based parameter adaptation for Differential Evolution (2013, background): “Since this is a significant problem in practice, selfadaptive mechanisms for adjusting the control parameters online during the search process have been studied by many researchers [3], [2].”
A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms (2011, methodology): “…algorithm (PSO) (Clerc and Kennedy 2002; Trelea 2003; Yoshida et al. 2000; Juang 2004), Differential evolution algorithm (DE) (Storn and Price 1997; Ferrante and Ville 2010; Price and Storn 1997; Storn 1999; Liu and Lampinen 2005; Ali and Torn 2004; Shahryar et al. 2008; Das and Suganthan 2009;…”
Enhancing Differential Evolution Utilizing Proximity-Based Mutation Operators (2011, background): “The inappropriate choice of the mutation factor can therefore cause the deceleration of the algorithm and a reduction of population diversity [12], [15], [28].”
Differential Evolution: A Survey of the State-of-the-Art (2011, background): “In a recently published article [60], Neri and Tirronen reviewed a number of DE-variants for single-objective optimization problems and also made an experimental comparison of these variants on a set of numerical benchmarks.”
Differential Evolution With Neighborhood Mutation for Multimodal Optimization (2012, background): “In [86], Neri and Tirronen stated that DE needed some extra moves to improve the performance and the lack of these search moves can cause stagnation.”
Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm (2012, background): “…2005, 2009; Epitropakis et al., 2011; Fan and Lampinen, 2003a,2003b; Mininno et al., 2011; Neri et al., 2011; Neri and Tirronen, 2010; Neri and Mininno, 2010; Noman and Iba, 2008; Olorunda and Engelbrecht, 2007; Salvatore et al., 2010; Tirronen et al., 2008; Weber et al., 2010; Wang et al., 2011).”
Differential Evolution With Ranking-Based Mutation Operators (2013, background): “Interested readers can refer to two good surveys of DE in [6] and [7] and the references therein.”
Metaheuristics: review and application (2013, methodology): “A summary of these advancements is given in the study by Neri and Tirrone (2010).”
Backtracking Search Optimization Algorithm for numerical optimization problems (2013, background): “It is generally believed that in nature, the genetic diversity of a population results from basic genetic processes such as recombination, crossover, mutation, selection and adaptation [1,11,12].”
Single objective real-parameter optimization: Algorithm jSO (2017, methodology): “In recent decades, it has shown robustness, efficiency, and it was very competitive when it was applied to solve real-world optimization problems [3], [4].”