Accelerating Differential Evolution Using an Adaptive Local Search

By Nasimul Noman, Hitoshi Iba. IEEE Transactions on Evolutionary Computation, 2008

628 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/071a6cd442706e424ea09bc8852eaa2e901c72f3

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Citations in the corpus

Comprehensive Learning Particle Swarm Optimization Algorithm With Local Search for Multimodal Functions (2019, methodology): “In terms of the combination of LS and metaheuristic algorithms, several studies have been performed with the help of a genetic algorithm [31] and DE [32].”
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization (2009, background): “Moreover, researchers improved the performance of DE by implementing opposition-based learning [40] or local search [41].”
Enhancing Differential Evolution Utilizing Proximity-Based Mutation Operators (2011, background): “In [73], FIR is enhanced through a Fig.”
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).”
LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems (2017, background): “Generally, DE has a good global exploration ability that can reach the region of global optimum, but it is slow at exploitation of the solution [7].”
A Multi-Facet Survey on Memetic Computation (2011, background): “Reference [146] also proposed a crossover-based adaptive local search that adapts the refinement intensity of the individual learning process based on some hill-climbing heuristic.”
Generalizing Surrogate-Assisted Evolutionary Computation (2010, background): “For a greater exposition of local meta-heuristics in optimization, the reader is referred to [47][48][49].”
Differential Evolution Using a Neighborhood-Based Mutation Operator (2009, methodology): “Noman and Iba [35], [36] proposed the Fittest Individual Refinement (FIR); a crossover-based local search method for DE.”
Differential Evolution: A Survey of the State-of-the-Art (2011, methodology): “The experimental results reported by Noman and Iba [64] indicated that DEahcSPX could outperform the classical DE (DE/rand/1/bin) in terms of convergence speed over a set of carefully chosen numerical benchmarks [95].”
Chaotic Local Search-Based Differential Evolution Algorithms for Optimization (2021, methodology): “In [25], DECLS is compared with some state-of-theart optimization algorithms, such as DEahcSPX [63] which uses an adaptive local search method, IPOP-CMA-ES [64] (a)”
Memetic algorithms and memetic computing optimization: A literature review (2012, background): “For example, in [162] a MA which integrates a simplex crossover within the DE framework has been proposed in order to solve LSPs, see also [163].”
DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization (2010, methodology): “However, it is slow at exploitation of the solution [5].”
Oppositional biogeography-based optimization (2009, background): “Note that the Penalty 1 and Penalty 2 functions, also called Generalized Penalized Functions [12], have typographical errors in most of the literature [14], [15], [16], [17], includ ing some heavily-referenced articles [12], [13].”
Evolutionary Computation Meets Machine Learning: A Survey (2011, methodology): “Noman et al. [ 70 ] used Lamarckian learning strategies to adaptively determine the length of the search by taking feedback from the search process.”
Recent advances in differential evolution: a survey and experimental analysis (2010, methodology): “4 gives a description of the following algorithmic families: – DE with Trigonometric Mutation, see Fan and Lampinen (2002, 2003b), Hu et al. (2005) Angira and Santosha (2007), and Angira and Santosh (2008) – DE with Simplex Crossover Local Search, see Noman and Iba (2005, 2008) – DE with Population ”
Differential evolution based on covariance matrix learning and bimodal distribution parameter setting (2014, background): “Noman and Iba [7] proposed a crossover-based adaptive local search operator to enhance the convergence rate of DE.”
SCA: A Sine Cosine Algorithm for solving optimization problems (2016, methodology): “Popular methods in this class are: chaotic maps [13-17], evolutionary operators [18-23], and local searches [24-27].”
Differential Evolution With Ranking-Based Mutation Operators (2013, methodology): “However, since all parents are chosen randomly, it may lead to the DE algorithm be good at exploring the search space and locating the region of glob al minimum, but be slow at exploitation of the solutions [8].”