Opposition-Based Differential Evolution

By Shahryar Rahnamayan, Hamid R. Tizhoosh, Magdy M. A. Salama. IEEE Transactions on Evolutionary Computation, 2008

1,586 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/70546e184e144f4802f011d5e1e856d26bf292e7

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Citations in the corpus

Recent advances in differential evolution - An updated survey (2016, result): “a manner similar to the opposition DE algorithm [157].”
Metaheuristics in large-scale global continues optimization: A survey (2015, methodology): “It should be noted that in [181,182], GODE was executed on 19 high-dimensional problems for D = 50, 100, 200, 500, and 1000 and was compared with DE, CHC and G-CMA-ES and it was concluded that GODE is significantly better than CHC and G-CMA-ES on all dimensions.”
Recent advances in differential evolution: a survey and experimental analysis (2010, methodology): “…Parameters, see Brest et al. (2006a,b, 2007), Zamuda et al. (2007), and Brest et al. (2008) – Opposition Based DE, see Rahnamayan et al. 2006a,b, 2007, 2008a, Rahnamayan and Wang (2008), and Rahnamayan et al. (2008b) – Global-Local Search DE, see Chakraborty et al. (2006) and Das et al. (2009) –”
A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms (2011, methodology): “…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; Kaelo and Ali 2006; Swagatam et al. 2009; Janez et al. 2007), Artificial bee colony algorithm (Karaboga…”
Differential evolution based on covariance matrix learning and bimodal distribution parameter setting (2014, background): “In this case, like [18] and [20], F and CR were fixed to 0.5 and 0.9 during the evolution, respectively.”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, background): “The books [54,150] on DE, research work on adaptive DE [55,56] and opposition-based DE [57,58] made the DE quite popular amongst researchers.”
Particle Swarm Methods (2018, methodology): “The opposition-based algorithms are grounded on the concept of opposite point [120].”
Enhancing particle swarm optimization using generalized opposition-based learning (2011, methodology): “Hence, we calculate opposite particles by using dynamically updated interval boundaries [aj(t),bj(t)] as follows [29].”
A self-adaptive artificial bee colony algorithm based on global best for global optimization (2017, methodology): “Gao and Liu (2012), we also designed a novel initialization algorithm that uses chaotic systems (Alatas 2010) and opposition-based learning method (Rahnamayan et al. 2008).”
Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions (2011, methodology): “To compare the convergence speeds, we use the acceleration rate (AR) [55], which is defined as follows, based on the number of function evaluations (NFEs) for the two algorithms ABC and RABC:”
Differential evolution with multi-population based ensemble of mutation strategies (2016, background): “This is because some mutation strategies are effective for the global 10 search [39] and some others are useful for rotated problems [12], and that some control parameter settings can promote the 11 convergence [40] and some other settings are more efficient for solving separable functions [41].”