Computer System for Making Efficiency Analysis of Meta-heuristic Algorithms to Solving Nesting Problem

Bogalinski, Pawel; Koszalka, Leszek; Pozniak-Koszalka, Iwona; Kasprzak, Andrzej

doi:10.1007/978-3-642-12101-2_24

Cited by 1 publication

(12 citation statements)

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“…begin Comparing the results obtained to the results presented in [3], it is worth to mention, that the temperature decrement rule of the logarithmic type was determined as the best, either.…”

Section: Simulated Annealing Algorithm (Sa)mentioning

confidence: 66%

“…(iii) Shapes are placed in the strip according to the top left (TL) placement rule [12] -this rule means shapes are firstly moved horizontally as far to the left as possible in the available space, and then moved vertically so that they are situated as high up as possible. The principle is analogous (but different) to the Bottom Left rule [3,6] for which each piece is to be moved horizontally as far to the left as possible and then vertically down until it is able to move horizontally again or touches another piece or bottom. (iv) A solution refers to a complete placement of shapes.…”

Section: Assumptions and Terminology Regarding The Strip Nesting Problemmentioning

confidence: 99%

“…Evaporation factor (pe): It simulates how quickly pheromone evaporates on ant paths -it is used to calculate and update pheromone trial intensity [3].…”

Section: Ant Colony Optimization Algorithm (Aco)mentioning

confidence: 99%

“…In the slightly modified implementation (in comparison to that presented in [3]), all possible the visibility values are calculated at start of the algorithm performance and held in a cache. It gives an opportunity for improving the speed of ACO algorithm.…”

Section: Ant Colony Optimization Algorithm (Aco)mentioning

confidence: 99%

“…In the paper, an extension of the research described in [3] is presented. It consists in improving the modules of the system.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of strip nesting algorithms: An experimentation system for the practical users

Bogalinski

Davies

Koszalka

et al. 2014

Journal of Intelligent &Amp; Fuzzy Systems

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Strip nesting problems are well known to be of NP-hard complexity, even in simple cases. A multitude of algorithms now exist that aim at solving such problems efficiently. The experimentation system described here helps users with industrial applications to decide which algorithm suits their requirements best. As an illustration, it is applied to three algorithms, based on: simulated annealing (SA), tabu search (TS), and ant colony optimization (ACO). The system can be treated as an input-output system, in which the parameters of the problem and the algorithms for solving the problem are the inputs, and the introduced measures of algorithms' quality are the outputs. Experiments are conducted in accordance with a two-stage approach. At the first stage, the tuning process for each algorithm is made. At the second stage, the efficiencies of the optimized algorithms are compared. In the paper, the results of the comparison obtained with the benchmark databases are discussed, and conclusions concerning efficiency of the considered algorithms are drawn accordingly.

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Section: Simulated Annealing Algorithm (Sa)mentioning

confidence: 66%

Section: Assumptions and Terminology Regarding The Strip Nesting Problemmentioning

confidence: 99%

“…Evaporation factor (pe): It simulates how quickly pheromone evaporates on ant paths -it is used to calculate and update pheromone trial intensity [3].…”

Section: Ant Colony Optimization Algorithm (Aco)mentioning

confidence: 99%

Section: Ant Colony Optimization Algorithm (Aco)mentioning

confidence: 99%

“…In the paper, an extension of the research described in [3] is presented. It consists in improving the modules of the system.…”

Section: Introductionmentioning

confidence: 99%

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