Optimization Letters

, Volume 11, Issue 4, pp 699–713 | Cite as

Global optimization test problems based on random field composition

  • Ramses SalaEmail author
  • Niccolò Baldanzini
  • Marco Pierini
Original Paper


The development and identification of effective optimization algorithms for non-convex real-world problems is a challenge in global optimization. Because theoretical performance analysis is difficult, and problems based on models of real-world systems are often computationally expensive, several artificial performance test problems and test function generators have been proposed for empirical comparative assessment and analysis of metaheuristic optimization algorithms. These test problems however often lack the complex function structures and forthcoming difficulties that can appear in real-world problems. This communication presents a method to systematically build test problems with various types and degrees of difficulty. By weighted composition of parameterized random fields, challenging test functions with tunable function features such as, variance contribution distribution, interaction order, and nonlinearity can be constructed. The method is described, and its applicability to optimization performance analysis is described by means of a few basic examples. The method aims to set a step forward in the systematic generation of global optimization test problems, which could lead to a better understanding of the performance of optimization algorithms on problem types with particular characteristics. On request an introductive MATLAB implementation of a test function generator based on the presented method is available.


Global optimization Metaheuristics Random fields Variable interactions Performance test function Artificial landscapes Problem features Optimization test problem 



The presented method is a generalized derivative of strategies developed for efficient optimization on structural simulations in vehicle design in the scope of the GRESIMO Project Funded by the European Commission under the 7th framework program under Grant Agreement 290050.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Ramses Sala
    • 1
    Email author
  • Niccolò Baldanzini
    • 1
  • Marco Pierini
    • 1
  1. 1.Dipartimento di Ingegneria IndustrialeUniversità degli Studi di FirenzeFlorenceItaly

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