Abstract

During the last decade, metaheuristic algorithms have occupied an important place in the field of optimization. Function minimization is of importance to researchers since many real-world problems can be modeled mathematically and be solved effectively through metaheuristic algorithms. Due to the growing scientific interest in the field of optimization and the good performances shown by the algorithms on function minimization, the practical and quick implementation concept is necessary to select the most appropriate algorithms on function minimization, and to assist researchers in analyzing the performance of the algorithms. In this study, a tool is developed to minimize user-defined functions in a specified range according to the chosen metaheuristic algorithms, which allows analyzing the algorithms in the general experimental environment. The tool, which has a user-friendly interface, can provide single and comparative solutions by simultaneously executing the algorithms. Each solution and computational time obtained by the algorithms is given numerically, and the convergence behavior of the algorithms is shown graphically in the tool interface. Minimization of functions can be made fast, easily and effectively through the developed tool.

References

[1] Deb K. Multi-objective optimization using evolutionary algorithms. UK: Wiley; 2001.
[2] Haupt RL. Thinned arrays using genetic algorithms. IEEE Transactions Antennas Propagation1994; 42:993-999.
[3] Rao RV, Pawar PJ. Modelling and optimization of process parameters of wire electricaldischarge machining. Proceedings of the Institution of Mechanical Engineers, Part B: Journal ofEngineering Manufacture 2009; 223:1431-1440.
[4] Karaboga D, Basturk B. A powerful and efficient algorithm for numerical functionoptimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization 2007; 39:459-471.
[5] Civicioglu P. Backtracking search optimization algorithm for numerical optimizationproblems. Applied Mathematics and Computation 2013; 21:8121-8144.
[6] Yang XS, Deb S. Cuckoo search via levy flights. Nature & Biologically Inspired Computing2009; 210:9-11.
[7] Yang XS. Firefly algorithm, levy flights and global optimization. Research and Developmentin Intelligent Systems 2010; 26:209-218.
[8] Yang XS. Harmony search as a metaheuristic algorithm. Music-inspired harmony searchalgorithm. Berlin: Springer; 2009.
[9] Doğan B, Ölmez T. A new metaheuristic for numerical function optimization: Vortex searchalgorithm. Information Science 2015; 293:125-145.
[10] MATLAB, The MathWorks, Inc. 2013.