Benchmark Functions for the CEC'2013 Special Session and Competition on Large-Scale Global Optimization

By Ke Tang, Xin Yao, Ponnuthurai Nagaratnam Suganthan, C. MacNish, Ying-ping Chen, C. M. Chen, Zhenyu Yang. 2008

893 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/903b2790c04d7c3450b0983a987586e3365775b8

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Citations in the corpus

Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization (2014, methodology): “An alternative approach called delta grouping [28] was shown to outperform random grouping on most functions from a set of 20 largescale benchmark problems [20].”
Lévy-Flight Moth-Flame Algorithm for Function Optimization and Engineering Design Problems (2016, background): “The same process is followed, in which nineteen standard benchmark functions are employed from the literature [27, 28] as test beds for comparison.”
DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization (2017, methodology): “Table V contains the experimental results using 25 independent runs on f4–f11 from the CEC’2013 large-scale benchmark suite [65].”
Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization (2014, background): “Hybrid Functions Considering that in the real-world optimization problems, different subcomponents of the variables may have different properties([5]).”
Test Problems for Large-Scale Multiobjective and Many-Objective Optimization (2017, background): “This can partly be attributed to the lack of large-scale multiobjective and many-objective optimization test problems (LSMOPs), unlike the rich literature on large-scale single-objective optimization [41]–[44].”
A social learning particle swarm optimization algorithm for scalable optimization (2015, methodology): “The proposed SL-PSO has been tested on 47 functions, including 12 widely used basic test functions [91,4,97,96,8] (f 1 to f 12, refer to Table 1), 28 functions taken from the whole test suite for CEC’13 special session on real-parameter optimization (f 13 to f 40, refer to [47]), and 7 functions tak”
Bio-inspired computation: Where we stand and what's next (2019, methodology): “[277], which was specially designed by combining functions with different lev-”
Improving the search performance of SHADE using linear population size reduction (2014, background): “pointed out that most previous benchmark sets did not include partially separable functions, and consisted of functions where all variables have uniform features [26].”
Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale (2009, methodology): “6 Conclusion In this report we have proposed a set of 15 large-scale benchmark problems as an extension to the existing CEC’2010 benchmark suite [42] for better evaluation of large-scale global optimization algorithms and to present some new challenges to the existing algorithms in order to boost th”
A Competitive Swarm Optimizer for Large Scale Optimization (2015, methodology): “As described in [43], f7 is a very special function which has large amount of random noise and its global optimum is unknown.”
Diversity enhanced particle swarm optimization with neighborhood search (2013, background): “By the suggestions of [49], the maximum number of fitness evaluations (MAX_FEs) is set to 3.”
Differential Evolution: A Survey of the State-of-the-Art (2011, methodology): “The algorithm achieved third rank in CEC 2008 special session and competition on high-dimensional real-parameter optimization [98] that included non-separable functions like Schwefel’s problem 2.”