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Predicting the Costs of Forwarding Contracts: Analysis of Data Mining Competition Results

Janusz Andrzej, Jamiołkowski Antoni, Okulewicz Michał

Annals of Computer Science and Information Systems

2022

Vol. 30

399--402

We describe an international data mining competition FedCSIS 2022 Challenge: Predicting the Costs of Forwarding Contracts that was organized in association with the FedCSIS conference series at the KnowledgePit platform. We explain the competition scope and briefly discuss the results obtained by the most successful teams. We also share the most interesting findings of our post-competition research assisted by the BrightBox technology, and describe our own prediction model that was used as the competition baseline. Finally, we show the results of our experiment conducted with the solution ensembling mechanism provided by KnowledgePit. The goal of this experiment was to find a mixture of submitted predictions that is the most accurate estimation of real execution costs of forwarding contracts.

Analysis of statistical model-based optimization enhancements in Generalized Self-Adapting Particle Swarm Optimization Framework

Zaborski Mateusz, Okulewicz Michał, Mańdziuk Jacek

Foundations of Computing and Decision Sciences

2020

Vol. 45, No. 3

234--254

This paper presents characteristics of model-based optimization methods utilized within the Generalized Self-Adapting Particle Swarm Optimization (GA– PSO) – a hybrid global optimization framework proposed by the authors. GAPSO has been designed as a generalization of a Particle Swarm Optimization (PSO) algorithm on the foundations of a large degree of independence of individual particles. GAPSO serves as a platform for studying optimization algorithms in the context of the following research hypothesis: (1) it is possible to improve the performance of an optimization algorithm through utilization of more function samples than standard PSO sample-based memory, (2) combining specialized sampling methods (i.e. PSO, Differential Evolution, model-based optimization) will result in a better algorithm performance than using each of them separately. The inclusion of model-based enhancements resulted in the necessity of extending the GAPSO framework by means of an external samples memory - this enhanced model is referred to as M-GAPSO in the paper. We investigate the features of two model-based optimizers: one utilizing a quadratic function and the other one utilizing a polynomial function. We analyze the conditions under which those model-based approaches provide an effective sampling strategy. Proposed model-based optimizers are evaluated on the functions from the COCO BBOB benchmark set.