Hard and Soft Adjusting of a Parameter With Its Known Boundaries by the Value Based on the Experts’ Estimations Limited to the Parameter

Romanuke, Vadim

doi:10.1515/ecce-2016-0003

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Nevertheless, the same task is completed within at most 100 seconds for two clusters with 500 nodes in each. On the other hand, if the cluster contains a few thousand nodes or far more, the transition nodes can be selected manually by observing a visualization of the clusters [25,26]. The manual selection may be worse than the true best Pareto-efficient points but the difference is likely to be nonsignificant.…”

Section: Discussion Of the Contributionmentioning

confidence: 99%

Traveling salesman problem parallelization by solving clustered subproblems

Romanuke

2023

Foundations of Computing and Decision Sciences

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A method of parallelizing the process of solving the traveling salesman problem is suggested, where the solver is a heuristic algorithm. The traveling salesman problem parallelization is fulfilled by clustering the nodes into a given number of groups. Every group (cluster) is an open-loop subproblem that can be solved independently of other subproblems. Then the solutions of the respective subproblems are aggregated into a closed loop route being an approximate solution to the initial traveling salesman problem. The clusters should be enumerated such that then the connection of two “neighboring” subproblems (with successive numbers) be as short as possible. For this, the destination nodes of the open-loop subproblems are selected farthest from the depot and closest to the starting node for the subsequent subproblem. The initial set of nodes can be clustered manually by covering them with a finite regular-polygon mesh having the required number of cells. The efficiency of the parallelization is increased by solving all the subproblems in parallel, but the problem should be at least of 1000 nodes or so. Then, having no more than a few hundred nodes in a cluster, the genetic algorithm is especially efficient by executing all the routine calculations during every iteration whose duration becomes shorter.

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Section: Discussion Of the Contributionmentioning

confidence: 99%

Traveling salesman problem parallelization by solving clustered subproblems

Romanuke

2023

Foundations of Computing and Decision Sciences

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“…Subsequently, a final set of expert estimations is grouped and it can be thought of as if each expert proposes its own pattern matrix. The pattern matrix can be then deduced straightforwardly by just averaging over those expert estimations [8,9]. However, averages are not always the best way to process expert estimations [10].…”

Section: Technical Diagnostics Based On Expert Estimationsmentioning

confidence: 99%

Optimal Construction of the Pattern Matrix for Probabilistic Neural Networks in Technical Diagnostics Based on Expert Estimations

Romanuke

2021

ІC&ІS

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In the field of technical diagnostics, many tasks are solved by using automated classification. For this, such classifiers like probabilistic neural networks fit best owing to their simplicity. To obtain a probabilistic neural network pattern matrix for technical diagnostics, expert estimations or measurements are commonly involved. The pattern matrix can be deduced straightforwardly by just averaging over those estimations. However, averages are not always the best way to process expert estimations. The goal is to suggest a method of optimally deducing the pattern matrix for technical diagnostics based on expert estimations. The main criterion of the optimality is maximization of the performance, in which the subcriterion of maximization of the operation speed is included. First of all, the maximal width of the pattern matrix is determined. The width does not exceed the number of experts. Then, for every state of an object, the expert estimations are clustered. The clustering can be done by using the k-means method or similar. The centroids of these clusters successively form the pattern matrix. The optimal number of clusters determines the probabilistic neural network optimality by its performance maximization. In general, most results of the error rate percentage of probabilistic neural networks appear to be near-exponentially decreasing as the number of clustered expert estimations is increased. Therefore, if the optimal number of clusters defines a too “wide” pattern matrix whose operation speed is intolerably slow, the performance maximization implies a tradeoff between the error rate percentage minimum and maximally tolerable slowness in the probabilistic neural network operation speed. The optimal number of clusters is found at an asymptotically minimal error rate percentage, or at an acceptable error rate percentage which corresponds to maximally tolerable slowness in operation speed. The optimality is practically referred to the simultaneous acceptability of error rate and operation speed.

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“…4. Primitive forms of expert judgments (similar to likes and dislikes given through social computer networks [40,41]).…”

Section: Figure 1 the Task Of Interval Uncertainty Reductionmentioning

confidence: 99%

Division-by-q dichotomization for interval uncertainty reduction by cutting off equal parts from the left and right based on expert judgments under short-termed observations

Romanuke

2020

Foundations of Computing and Decision Sciences

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AbstractA problem of reducing interval uncertainty is considered by an approach of cutting off equal parts from the left and right. The interval contains admissible values of an observed object’s parameter. The object’s parameter cannot be measured directly or deductively computed, so it is estimated by expert judgments. Terms of observations are short, and the object’s statistical data are poor. Thus an algorithm of flexibly reducing interval uncertainty is designed via adjusting the parameter by expert procedures and allowing to control cutting off. While the parameter is adjusted forward, the interval becomes progressively narrowed after every next expert procedure. The narrowing is performed via division-by-q dichotomization cutting off the q−1-th parts from the left and right. If the current parameter’s value falls outside of the interval, forward adjustment is canceled. Then backward adjustment is executed, where one of the endpoints is moved backwards. Adjustment is not executed when the current parameter’s value enclosed within the interval is simultaneously too close to both left and right endpoints. If the value is “trapped” like that for a definite number of times in succession, the early stop fires.

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Hard and Soft Adjusting of a Parameter With Its Known Boundaries by the Value Based on the Experts’ Estimations Limited to the Parameter

Cited by 3 publications

References 15 publications

Traveling salesman problem parallelization by solving clustered subproblems

Traveling salesman problem parallelization by solving clustered subproblems

Optimal Construction of the Pattern Matrix for Probabilistic Neural Networks in Technical Diagnostics Based on Expert Estimations

Division-by-q dichotomization for interval uncertainty reduction by cutting off equal parts from the left and right based on expert judgments under short-termed observations

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