Version 1
: Received: 24 March 2020 / Approved: 26 March 2020 / Online: 26 March 2020 (04:03:41 CET)
How to cite:
Caraffini, F. The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms. Preprints2020, 2020030381. https://doi.org/10.20944/preprints202003.0381.v1
Caraffini, F. The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms. Preprints 2020, 2020030381. https://doi.org/10.20944/preprints202003.0381.v1
Caraffini, F. The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms. Preprints2020, 2020030381. https://doi.org/10.20944/preprints202003.0381.v1
APA Style
Caraffini, F. (2020). The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms. Preprints. https://doi.org/10.20944/preprints202003.0381.v1
Chicago/Turabian Style
Caraffini, F. 2020 "The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms" Preprints. https://doi.org/10.20944/preprints202003.0381.v1
Abstract
The Stochastic Optimisation Software (SOS) is a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. It reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including 1) customised implementations of statistical tests, such as the Wilcoxon Rank-Sum test and the Holm-Bonferroni procedure, for comparing performances of optimisation algorithms and automatically generate result tables in PDF and LaTeX formats; 2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; 3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation operator. each testbed function. Moreover, this article comments on the current state of the literature in stochastic optimisation and highlights similarities shared by modern metaheuristics inspired by nature. It is argued that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them.
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
