TY  - JOUR
T1  - Performance Analysis of SMDO Method with Benchmark Functions with Matlab Toolbox
TT  - Performance Analysis of SMDO Method with Benchmark Functions with Matlab Toolbox
AU  - Ateş, Abdullah
AU  - Akpamukçu, Mehmet
AU  - Alagöz, Barış Baykant
PY  - 2020
DA  - December
Y2  - 2020
DO  - 10.21597/jist.722427
JF  - Journal of the Institute of Science and Technology
JO  - J. Inst. Sci. and Tech.
PB  - Igdir University
WT  - DergiPark
SN  - 2536-4618
SP  - 2451
EP  - 2460
VL  - 10
IS  - 4
LA  - en
AB  - SMDO method is a set and trial based optimization algorithm that is developed for online fine-tuning of controller parameters. SMDO method is implemented for several controller tuning applications. 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 thus makes 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 with other well known heuristic optimization methods. This study aims to compare performances of Artificial Bee Colony (ABC), Cuckoo Search Optimization (CK), Particle Swarm Optimization (PSO) and Stochastic 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. This program can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/75043-smdo-method-with-benchmark-functions
KW  - SMDO
KW  - optimization
KW  - stochastic
KW  - benchmark functions
N2  - SMDO method is a set and trial based optimization algorithm that is developed for online fine-tuning of controller parameters. SMDO method is implemented for several controller tuning applications. 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 thus makes 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 with other well known heuristic optimization methods. This study aims to compare performances of Artificial Bee Colony (ABC), Cuckoo Search Optimization (CK), Particle Swarm Optimization (PSO) and Stochastic 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. This program can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/75043-smdo-method-with-benchmark-functions
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UR  - https://doi.org/10.21597/jist.722427
L1  - https://dergipark.org.tr/en/download/article-file/1058509
ER  -