Differential Evolution Using a Neighborhood-Based Mutation Operator

By Swagatam Das, Ajith Abraham, Uday Kumar Chakraborty, Amit Konar. IEEE Transactions on Evolutionary Computation, 2009

1,190 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/55238adbd3d78dc83cf906240727be02f6560470

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Citations in the corpus

Enhancing Differential Evolution Utilizing Proximity-Based Mutation Operators (2011, methodology): “DEGL is inspired from PSO and incorporates the concept of index neighborhoods [25], [42].”
Ensemble of Constraint Handling Techniques (2010, background): “Depending on the application different variants of DE have been proposed to balance the exploration and exploitation capability [47].”
Diversity enhanced particle swarm optimization with neighborhood search (2013, methodology): “To tackle this problem, this paper presents one local and one global neighborhood search strategies, which are inspired by the neighborhood mutation operator in Differential Evolution (DE) [11].”
Recent advances in differential evolution - An updated survey (2016, methodology): “Index-based topologies [37] have been commonly used while Euclidean distance based topologies [85,153,154,176] have not been frequently used.”
Firefly algorithm with neighborhood attraction (2017, methodology): “Based on [6], the k-neighborhood of Xi consists of 2k + 1 fireflies {Xi−k, .”
A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms (2011, methodology): “The standard mutation strategies implemented in the original DE algorithm (Storn and Price 1997; Price and Storn 1997; Price et al. 2005; Das and Suganthan 2009; Swagatam et al. 2009) are given in Eq.”
Chaotic Local Search-Based Differential Evolution Algorithms for Optimization (2021, methodology): “…compared with some state-of-the-art optimization algorithms, such as DEahcSPX which uses an adaptive search method, IPOP-CMA-ES [64] (a) (b) which obtains the best performance in CEC2005 special session on real parameter optimization and DEGL/saw [65] which utilizes the neighborhood of population.”
Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters (2011, methodology): “It has been clear that some trial vector generation strategies are suitable for the global search [4] and some others are useful for rotated problems [5], and that some control parameter settings can speed up the convergence [6] and some other settings are effective for separable functions [7].”
An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization (2012, neutral): “…with F = 0.8 and Cr = 0.9; 2) DE/rand/1/bin with F = 0.8 and Cr = 0.9; 3) JADE with c = 0.1, p = 0.05, and optional external archive [20]; 4) jDE with F l = 0.1, F u = 0.9, and τ 1 = τ 2 = 0.1[13]; 5) SaDE [12]; 6) DEGL/SAW [21] with α = β = F = 0.8, Cr = 0.9, and neighborhood size = 0.1 * Np.”
Differential evolution based on covariance matrix learning and bimodal distribution parameter setting (2014, methodology): “In the method proposed by Das et al. [15], two trial vectors are produced by the global and local neighborhood-based mutation.”