Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization

By Ponnuthurai Nagaratnam Suganthan, Nikolaus Hansen, Jing J. Liang, Kalyanmoy Deb, Ying-Ping Chen, Anne Auger, Santosh Tiwari. 2005

3,161 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/754faf787ad3c7290e62f34eea282f96ff5f8757

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Citations in the corpus

A restart CMA evolution strategy with increasing population size (2005, methodology): “The restart-(µW, )-CMA-ES with increasing population size, IPOP-CMA-ES, has been investigated on the 25 test functions described in [ 8 ] for dimension 10, 30 and 50. For each function a bounded subset [A,B]n of Rn is prescribed.”
Frankenstein's PSO: A Composite Particle Swarm Optimization Algorithm (2009, unknown): “In most cases, we used the shift values proposed in the set of benchmark functions used for the special session on real parameter optimization of the IEEE CEC 2005 [19].”
Quasi-oppositional Differential Evolution (2007, methodology): “05 • Maximum number of function calls, MAX NFC = 10 6 • Value to reach, VTR = 10 − 8 [17] The jumping rate for QODE is set to a smaller value ( J rQODE = 16 J rODE ) because our trials showed that the higher jumping rates can reduce diversity of the population very fast and cause a premature…”
An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization (2012, methodology): “For the sake of space economy, we provide results for the first 14 functions in each case from the CEC 2005 benchmark set [34], [35].”
The Whale Optimization Algorithm (2016, background): “These benchark functions are shifted, rotated, expanded, and combined varints of the most complicated mathematical optimization problems resented in literature [71] .”
Differential Evolution: A Survey of the State-of-the-Art (2011, methodology): “Mallipeddi and Suganthan [50] empirically investigated the effect of population size on the quality of solutions and the computational effort required by DE with a set of five problems chosen from the test-suite of CEC 2005 Special Session on Real-Parameter Optimization [95].”
Grasshopper Optimisation Algorithm: Theory and application (2017, methodology): “The test functions are 25 taken from the CEC2005 special session [45].”
Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems (2015, background): “The last group of test functions, composite functions, are mostly the combined, rotated, shifted, and biased version of other unimodal and multi-modal test functions [52, 53].”
Emperor penguin optimizer: A bio-inspired algorithm for engineering problems (2018, background): “The fourth category comprises six composite test functions ( − F F 24 29) which are the shifted, rotated, expanded, and combined version of classical functions [54].”
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms (2011, background): “As benchmark suite, we have selected the 25 test problems of dimension 10 that appeared in the CEC’2005 special session on real parameter optimization [6].”
Particle Swarm Optimization With an Aging Leader and Challengers (2013, background): “Please refer to [48] and also the website http://www3.”
Multi-Verse Optimizer: a nature-inspired algorithm for global optimization (2015, background): “F 19, are six composite benchmark functions provided by CEC 2005 special session [24, 25].”
Benchmark Functions for CEC'2013 Special Session and Competition on Niching Methods for Multimodal Function Optimization' (2013, background): “It has to be noted that, similar implementation of composition functions with multiple global optima was proposed in [18].”
Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters (2011, background): “A detailed description of these test instances can be found in [20].”
Diversity enhanced particle swarm optimization with neighborhood search (2013, methodology): “For this test suite, the MAX_FEs is set to 3.00E+05 according to the suggestions of [46].”