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    Analyzing of Usage Effect of the Distribution Functions for SMDO Algorithm via Benchmark Function with Matlab Toolbox
    (2020) AKPAMUKÇU, Mehmet; ATEŞ, ABDULLAH
    This paper presents solution comparisons of benchmark functions by using stochastic multi-parametersdivergence (SMDO) method with different distribution functions. Using benchmark functions is animportant method in measuring the effectiveness of algorithms. Because benchmark functions are used byall algorithm producers while trying their algorithms and this provides a good tool for the others to comparetheir algorithms with similar procedures. Benchmark functions are used in this paper for the main purposeof analyzing randomization process. It is known that distribution functions take place a vital role in gettingrandom numbers. These random numbers are used in stochastic methods through specifying step size. Itis believed that a suitable random number acquisition process can support the search processes ofalgorithms. In this study the effects of distribution functions on benchmark functions are analyzed. Forthis purpose, a program is developed with MATLAB. The comparisons via the help of this program isshown in tabular form. The results are analyzed from the viewpoint of whether developing therandomization process makes contribution to problem solving power of algorithms. In this study SMDOalgorithm is analyzed with different distribution functions by using different benchmark functions. Inaddition, in the study, a useful friend-friendly Matlab toolbox is proposed in which SMDO algorithm canbe tested over different benchmark functions according to different distribution functions.(https: //www.mathworks.com/matlabcentral/fileexchange/75044-smdo-with-distribution-function-forbenchmarking)
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    Performance Analysis of SMDO Method with Benchmark Functions with Matlab Toolbox
    (2020) ALAGÖZ, Barış Baykant; AKPAMUKÇU, Mehmet; ATEŞ, ABDULLAH
    Öz: SMDO method is a set and trial based optimization algorithm that is developed for onlinefine-tuning of controller parameters. SMDO method is implemented for several controller tuningapplications. It can search parameter space with random backward and forward steps of each parameter.This property reduces risk of testing unstable control system configurations in controller design and thusmakes the SMDO method more suitable for online parameter tuning of experimental systems. However,performance of SMDO has not been evaluated previously for benchmark functions in comparison withother well known heuristic optimization methods. This study aims to compare performances of ArtificialBee Colony (ABC), Cuckoo Search Optimization (CK), Particle Swarm Optimization (PSO) andStochastic Multi-parameters Divergence Optimization (SMDO) methods for benchmark functions.Therefore, a benchmark tests program that is a user-friendly MATLAB GUI is introduced for user. Thisprogram can be downloaded from https: //www.mathworks.com/matlabcentral/fileexchange/75043-smdo-method-with-benchmark-functions.