Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization

By Jing-Chang Liang, B. Qu, Ponnuthurai Nagaratnam Suganthan. 2014

1,566 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/425ab097fc695265c3361d39d1f9a07a810fd595

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Water wave optimization: A new nature-inspired metaheuristic (2015, methodology): “Details definitions of the functions can be found in [39].”
INFO: An efficient optimization algorithm based on weighted mean of vectors (2022, methodology): “To further evaluate the INFO algorithm, widely-used and complicated CEC-BC-2017, benchmark problems were utilized, including rotated and shifted unimodal, multimodal, hybrid, and composite test functions [52].”
A social spider algorithm for global optimization (2015, methodology): “These benchmark functions are all the base functions from the latest Competition on Real-Parameter Single Objective Optimization Problems at CEC 2013 [34] and CEC 2014 [35].”
Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems (2017, methodology): “The computational complexity is calculated as described in [18].”
Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm (2024, background): “C14–F4 and C14–F30 present ideal choices for assessing the proficiency of metaheuristic algorithms in local search and exploitation due to their absence of local optima.”
Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm (2016, unknown): “Details definitions of the functions can be found in [62].”
Henry gas solubility optimization: A novel physics-based algorithm (2019, unknown): “The CEC’17 test suite consists of 29 benchmark functions because function (F2) has been excluded because it shows unstable behavior as mentioned in [76], that are divided into four classes: unimodal functions (F1 and F3), multimodal functions (F4-F10), hybrid functions (F11-F20), and composition…”
Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm (2022, background): “It has been shown that these test functions work well for situations where the quality of intelligent and resistant optimization algorithms are tested [41], [75]–[79].”
Improving the search performance of SHADE using linear population size reduction (2014, methodology): “We performed our evaluation following the guidelines of the CEC2014 benchmark competition [17].”
Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems (2022, unknown): “These benchmark functions are chosen from CEC2014 [50], CEC2015 [51], and CEC2019 [52].”