Titlebook: Recent Advances in Computational Optimization; Results of the Works Stefka Fidanova Book 2019 Springer Nature Switzerland AG 2019 Computati

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Katarzyna Poczeta,?ukasz Kubu?,Alexander Yastrebov

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Book 2019achieved. Many real-world and industrial problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks. This volume presents a comprehensive collection of extended contributions from the 2017 Workshop on Computational Optimization. .Presenting recent

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The New Approach for Dynamic Optimization with Variability Constraints,itions with the complementarity constraints, the solution procedure combining SQP algorithm with the filter approach as a globalization procedure was designed. The efficiency of the presented methodology was tested on a?production process in chemical engineering.

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InterCriteria Analysis Approach for Comparison of Simple and Multi-population Genetic Algorithms Pend disagreement between the algorithms outcomes, namely convergence time and model accuracy, from one hand, and model parameters estimations, from the other hand, have been established. The obtained results after the application of intercriteria analysis have been compared and outlined relations have been thoroughly discussed.

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Fuzziness in the Berth Allocation Problem,oposed has been implemented in CPLEX and evaluated in a benchmark developed to this end. For both models, with a timeout of 60?min, CPLEX find the optimum solution to instances up?to 10 vessels; for instances between 10 and 45 vessels it find a non-optimum solution and for bigger instants no solution is founded.

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Identifying Clusters in Spatial Data Via Sequential Importance Sampling,rical experiments illustrate the effectiveness of the approach. We applied this method to artificially generated data set and compared with the results obtained via binary segmentation procedure. We also provide example with real data set to illustrate the usefulness of this method.

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Discovering Knowledge from Predominantly Repetitive Data by InterCriteria Analysis,function value and computation time. Some useful conclusions with respect to the selection of the appropriate ICrA algorithm for a given data are established. The considered example illustrates the applicability of the ICrA algorithms and demonstrates the correctness of the ICrA approach.