Differential Evolution: A Practical Approach to Global Optimization

By K. Price, R. Storn, Jouni Lampinen. 2014

5,563 citations recorded by the source corpus.

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Citations in the corpus

A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification (2018, background): “Overall, there are four major steps in a DE algorithm, which are initialisation, mutation, crossover and selection [12].”
Parameter extraction of solar cell models using repaired adaptive differential evolution (2013, methodology): “Due to several ad vantages like ease of use, simple structure, and robustness , DE has been successfully used in diverse fields, such as data m ining, sensor fusion, engineering design [30], etc.”
A Survey on Evolutionary Neural Architecture Search (2020, methodology): “The other categories of the EC methods are also important parts for realizing ENAS algorithms, such as GP, evolutionary strategy (ES), PSO, ant colony optimization (ACO) [29], differential evolution (DE) [30], and firefly algorithm (FA) [31].”
Nature-Inspired Optimization Algorithms: Challenges and Open Problems (2020, unknown): “…algorithms in the current literature, it is estimated there are more than 100 diﬀerent algorithms and variants (Reyes-Sierra and Coello Coello, 2006; Kennedy et al., 2001; Price et al., 2005; Fister et al., 2013; Yang and He, 2013; Alyasseri et al., 2018; Abdel-Basset and Shawky, 2019).”
Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer (2020, background): “, simulated binary crossover [25] and polynomial mutation [26]), and DE [64], respectively.”
Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems (2021, background): “Differential Evolution (DE) algorithm is another popular evolutionary optimization algorithm that has a good ability to optimize non-differentiable nonlinear functions, which has been introduced as a powerful and fast way to optimize problems in continuous spaces [6].”
From evolutionary computation to the evolution of things (2015, background): “Today, the family of EAs includes historical members: genetic algorithms, evolution strategies, evolutionary programming, and genetic programming; and younger siblings, such as differential evolution and particle swarm optimization 17,18,19,20,21,22,23,24,25 .”
A bilevel optimization approach to automated parameter tuning (2014, methodology): “The algorithms that we have used in this paper are: Differential Evolution (DE) [33] and Nelder-Mead (NM) [31].”
Asset Allocation with Conditional Value-at-Risk Budgets (2013, unknown): “We used the differential evolution algorithm developed by Price et al (2005).”
Spider Monkey Optimization algorithm for numerical optimization (2014, methodology): “[28] also drawn the same conclusions regarding DE.”
Feature selection for high-dimensional classification using a competitive swarm optimizer (2016, background): “…solving complex combinatorial optimization problems, various metaheuristics have been proposed, such as genetic algorithms (GAs) (Han and Kim 2002), differential evolution (DE) (Price et al. 2006), and particle swarm optimization (PSO) (Banks et al. 2008), among many others (Bianchi et al. 2009).”
The Wind Driven Optimization Technique and its Application in Electromagnetics (2013, background): “Along with the GA, Particle Swarm Optimization (PSO) [2], Ant Colony Optimization (ACO) [3], Differential Evolution (DE) [4], [5], Clonal Selection Algorithm (CLONALG) [6], Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) [7] and many others have been proposed and successfully implemented”