Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)

By Kenneth Price, R. Storn, Jouni Lampinen. 2005

689 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ba18a425f13b5e3f494a1cf423aeeb59c6ad59b0

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Citations in the corpus

Online Demand Under Limited Consumer Search (2010, unknown): “To find the global optimum value, we first use differential evolution (Price et al. 2005, Bajari et al. 2008), a stochastic search algorithm, to obtain good initial estimates.”
Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale (2009, background): “However, their performance deteriorates rapidly as the dimensionality of the problem increases [3] [19].”
A novel population initialization method for accelerating evolutionary algorithms (2007, methodology): “Some well-established and commonly used EAs are Genetic Algorithms (GA) [4] and Differential Evolution (DE) [6,5].”
A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms (2011, methodology): “15; φ = ⎛ ⎝ (1 + β) · sin π · β 2 1+β 2 · β · 2 β−1 2 ⎞ ⎠ 1 / β (15) In the standard software implementation of the CK algorithm (Yang and Deb 2010) Table 4 The BestOpt values of the CK, PSO, DE and ABC algorithms CK PSO DE ABC F1 0.9980038377944496 0.9980038377944496 0.9980038377944496 0.9980038377”
Opposition-Based Differential Evolution (2008, methodology): “• Mutation strategy [2]: DE/rand/1/bin (classic version of DE) [2], [3], [15], [16], [26].”
Enhancing Differential Evolution Utilizing Proximity-Based Mutation Operators (2011, methodology): “The inappropriate choice of the mutation factor can therefore cause the deceleration of the algorithm and a reduction of population diversity [12], [15], [28].”
A Multi-Facet Survey on Memetic Computation (2011, background): “Typical population-based search frameworks including genetic algorithm (GA) [8], evolution strategy (ES) [9], evolutionary programming [10], genetic programming [11], differential evolution (DE) [12], estimation of distribution algorithm (EDA) [13], ant colony optimization (ACO) [14], particle swarm”
Opposition versus randomness in soft computing techniques (2008, background): “We are still faced with this fundamental question: Why an opposite number is beneficial compared to an independent random number? In other words, why a second random number should not be used instead of an opposite number? Many optimization methods need to generate random numbers for initialization ”
Oppositional biogeography-based optimization (2009, methodology): “This comparison method is popular in the literature [19] since, generally, the computation of fitness/cost function consumes most of the CPU’s resources.”