Test Functions for Global Optimization : A Comprehensive Survey

Test functions are important to validate and compare the performance of various optimization algorithms. In previous years, there have been many test or benchmark functions reported in the literature. However, there is no standard list or set of benchmark functions with diverse properties that algorithms may be tested upon. On the other hand, any new optimization algorithm should be tested by a diverse range of test or benchmark functions so as to see if it can solve certain types of problems or not. For this purpose, we compile here 140 benchmark functions for unconstrained optimization problems.

[1]  Sudhanshu K. Mishra,et al.  Performance of the Barter, the Differential Evolution and the Simulated Annealing Methods of Global Optimization on Some New and Some Old Test Functions , 2006 .

[2]  Sudhanshu K. Mishra Repulsive Particle Swarm Method on Some Difficult Test Problems of Global Optimization , 2006 .

[3]  L. Darrell Whitley,et al.  Evaluating Evolutionary Algorithms , 1996, Artif. Intell..

[4]  Xin-She Yang,et al.  Firefly algorithm, stochastic test functions and design optimisation , 2010, Int. J. Bio Inspired Comput..

[5]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[6]  Sudhanshu K. Mishra,et al.  Performance of Repulsive Particle Swarm Method in Global Optimization of Some Important Test Functions: A Fortran Program , 2006 .

[7]  Sudhanshu K. Mishra Performance of Differential Evolution and Particle Swarm Methods on Some Relatively Harder Multi-Modal Benchmark Functions , 2006 .

[8]  Sudhanshu K. Mishra,et al.  Some New Test Functions for Global Optimization and Performance of Repulsive Particle Swarm Method , 2006 .

[9]  Neculai Andrei,et al.  An Unconstrained Optimization Test Functions Collection , 2008 .

[10]  Nicholas I. M. Gould,et al.  CUTEr and SifDec: A constrained and unconstrained testing environment, revisited , 2003, TOMS.

[11]  N. Garc'ia-Pedrajas,et al.  CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features , 2005, J. Artif. Intell. Res..

[12]  Zelda B. Zabinsky,et al.  A Numerical Evaluation of Several Stochastic Algorithms on Selected Continuous Global Optimization Test Problems , 2005, J. Glob. Optim..