The Stochastic Optimisation Software (SOS) platform
Fabio Caraffini · Zenodo (CERN European Organization for Nuclear Research) · 2019
The SOS platform facilitates the design of optimisation algorithms such as (both stochastic and deterministic) metaheuristics for (but not limited to) real-valued single objective problems thanks to: the possibility of easily combining together already implemented algorithmic components, such as several variation operators (e.g. crossover, mutation, etc. ) and selection mechanisms from Evolutionary Computation, Memetic Algorithms/Computing and Hyper-Heuristics ; the availability of several ancillary methods for manipulating matrices, performing mathematical operations, handling the computational budget, parameter tuning, executing and comparing between algorithms and versions of the same algorithm; and it helps to produce and interpreting results thanks to: the presence of several ready-to-use popular benchmarks suites (e.g. BBOB2010--2019, CEC2015--2017, popular functions), examples of published real-world applications, and benchmark real-world problems; the presence of ancillary modules executing algorithms, over the aforementioned (or newly implemented) problems, also in Multi-thread to accelerate the production of numerical results; the availability of methods collecting results and creating PDF and LaTeX tables with the outcome of several statistic tests and classic AVG=- STD comparison (see examples in http://www.cse.dmu.ac.uk/~fcaraf00/NumericalResults/) SOS is meant for stochastic optimisation but it is not limited to it: deterministic metaheuristic algorithms can be implemented and compared against a large number of algorithms already present in this repository. Examples of studies performed via SOS are: Infeasibility and structural bias in differential evolution (2019), Information Sciences, DOI: 10.1016/j.ins.2019.05.019 HyperSPAM: A study on hyper-heuristic coordination strategies in the continuous domain (2019), Information Sciences, DOI: 10.1016/j.ins.2018.10.033 Compact Optimization Algorithms with Re-Sampled Inheritance (2019), in LNCS, DOI: 10.1007/978-3-030-16692-2_35 Improving (1+1) covariance matrix adaptation evolution strategy: A simple yet efficient approach (2019), AIP Conference Proceedings, DOI: 10.1063/1.5089971 Structural bias in differential evolution: A preliminary study (2019) AIP Conference Proceedings, DOI: 10.1063/1.5089972 A study on rotation invariance in differential evolution (2018), Swarm and Evolutionary Computation, DOI: 10.1016/j.swevo.2018.08.013 Rotation Invariance and Rotated Problems: An Experimental Study on Differential Evolution (2018), LNCS including LNAI, DOI: 10.1007/978-3-319-77538-8_41 Large scale problems in practice: The effect of dimensionality on the interaction among variables (2017), LNCS including LNAI, DOI: 10.1007/978-3-319-55849-3_41 Cluster-Based Population Initialization for differential evolution frameworks (2015), Information Sciences, DOI: 10.1016/j.ins.2014.11.026 Continuous parameter pools in ensemble differential evolution (2015), IEEE SSCI'15, DOI: 10.1109/SSCI.2015.216 Multicriteria adaptive differential evolution for global numerical optimization (2015), Integrated Computer-Aided Engineering, DOI: 10.3233/ICA-150481 Structural bias in population-based algorithms (2015), Information Sciences, DOI: 10.1016/j.ins.2014.11.035