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Software Open Access

The Stochastic Optimisation Software (SOS) platform

Fabio Caraffini

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

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

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