Evolutionary Algorithm for Solving Congestion Problem in Computer Networks

Computational Science and Its Applications -- ICCSA 2015

et al. 2015

Self Cite

Abstract. Ability of Multiple Classifier Systems (MCSs) to deliver correct prediction, even though all of the classifiers from its ensemble are wrong, confirms that MCS approach possess large potential in the field of pattern recognition. This work focuses on the problem of fuser design. While most MCSs use popular Voting combiner, authors of this work investigate prediction accuracy of MCS with either Evolutionary or Particle swarm algorithms applied as the fusers. Obtained results suggest, that aforementioned fusion algorithms increase accuracy performance on the evaluated datasets in comparison to the popular Voting combiner.

Section: Resultsmentioning

confidence: 99%

Multiple Classifier System with Metaheuristic Algorithms

Quoos

Pozniak-Koszalka

Computational Science and Its Applications -- ICCSA 2015

et al. 2015

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“…We would like to create the environment allowing the comparison of more algorithms and in consequence, for answering the question which of meta-heuristic and evolutionary approaches is the most convenient for solving the considered problem. Moreover, we are working on the improved version of the experimentation system with a module structure [18] which would give opportunities for taking into account the ideas presented in [19] and conducting complex experiments in automatic manner.…”

Section: Resultsmentioning

confidence: 99%

Ant Colony Optimization Algorithm for Solving the Provider - Modified Traveling Salesman Problem

Baranowski

Intelligent Information and Database Systems

Pozniak-Koszalka

et al. 2014

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Abstract. The paper concerns the introduced and defined problem which was called the Provider. This problem coming from practice and can be treated as a modified version of Travelling Salesman Problem. For solving the problem an algorithm (called ACO) based on ant colony optimization ideas has been created. The properties of the algorithm were tested using the designed and implemented experimentation system. The effectiveness of the algorithm was evaluated and compared to reference results given by another implemented Random Optimization algorithm (called RO) on the basis of simulation experiments. The reported investigations have shown that the ACO algorithm seems to be very effective for solving the considered problem. Moreover, the ACO algorithm can be recommended for solving other transportation problems.

“…Future development of the KSBAN system will focus on including shape rotation operators and genetic algorithm functionality. New KSBAN system is being prepared to further enhance automated multistage experiments [10,11].…”

Section: Conclusion and Suggestions For Further Researchmentioning

confidence: 99%

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

Bogalinski

Davies

Journal of Intelligent &Amp; Fuzzy Systems

et al. 2014

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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.