Arz-Talep Tabanli Opti̇mi̇zasyon Algori̇tmasinin FDB Yöntemi̇ İle İyi̇leşti̇ri̇lmesi̇: Mühendi̇sli̇k Tasarim Problemleri̇ Üzeri̇ne Kapsamli Bi̇r Araştirma

Katı, Mehmet; Kahraman, Hamdi Tolga

doi:10.21923/jesd.829508

Cited by 10 publications

(3 citation statements)

References 38 publications

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“…In the roulette wheel technique, the pieces of the wheel are adjusted according to their fitness values, and the candidate represented by that piece is selected. In the double tournament technique, two randomly selected values from the population are compared and the one with the higher fitness value is taken [25].…”

Section: Methodsmentioning

confidence: 99%

Global Optimizasyonu için Uygunluk Mesafe Dengesi Tabanlı Rehber Mekanızmasıyla Slime Mould Optimize Edicinin İyileştirilmesi

SUİÇMEZ

Kahraman

Yılmaz³

et al. 2021

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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In this study, the performance of Slime-Mould-Algorithm (SMA), a current Meta-Heuristic Search algorithm, is improved. In order to model the search process lifecycle process more effectively in the SMA algorithm, the solution candidates guiding the search process were determined using the fitness-distance balance (FDB) method. Although the performance of the SMA algorithm is accepted, it is seen that the performance of the FDB-SMA algorithm developed thanks to the applied FDB method is much better. CEC 2020, which has current benchmark problems, was used to test the performance of the developed FDB-SMA algorithm. 10 different unconstrained comparison problems taken from CEC 2020 are designed by arranging them in 30-50-100 dimensions. Experimental studies were carried out using the designed comparison problems and analyzed with Friedman and Wilcoxon statistical test methods. According to the results of the analysis, it has been seen that the FDB-SMA variations outperform the basic algorithm (SMA) in all experimental studies.

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Section: Methodsmentioning

confidence: 99%

Global Optimizasyonu için Uygunluk Mesafe Dengesi Tabanlı Rehber Mekanızmasıyla Slime Mould Optimize Edicinin İyileştirilmesi

SUİÇMEZ

Kahraman

Yılmaz³

et al. 2021

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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“…F5, F6 and F7 functions test whether it performs a balanced search during the search process. F8, F9 and F10 functions are used for performance testing in high search space [23,24,25,26,27].…”

Section: B Benchmark Problemsmentioning

confidence: 99%

“…Powerful hybrid metaheuristic search algorithms have been developed using the FDB selection method. A few of these are Fitness Distance Balance-based Adaptive Guided Differential Evolution (FDBAGDE) [24], Lévy flight and FDB-based coyote optimization algorithm (LRFDBCOA) [25], Fitness Distance Balance-based Symbiotic Organism Search (FDBSOS) [23] and Fitness Distance Balance-based Stochastic Fractal Search (FDBSFS) [26] Fitness Distance Balance-based Supply-Demand-Optimizer (FDBSDO) [27].…”

Section: Introductionmentioning

confidence: 99%

Improved Runge Kutta Optimizer with Fitness Distance Balance-Based Guiding Mechanism for Global Optimization of High-Dimensional Problems

Cengiz

Yılmaz²,

Kahraman

et al. 2021

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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Runge Kutta (RUN) is an up-to-date and well-founded metaheuristic algorithm. The RUN algorithm aims to find the global best in solving problems by going beyond the traps of metaphors. For this purpose, enhanced solution quality mechanism is used to avoid local optimum solutions and increase the convergence speed. Although the RUN algorithm offers promising solutions, it is seen that this algorithm has shortcomings, especially in solving high dimensional multimodal problems. In this study, the solution candidates that guide the search process in the RUN algorithm are developed using the Fitness-Distance Balance (FDB) method. Thus, using the FDB-based RUN algorithm, the global optimum value of many optimization problems will be obtained in the future. CEC 2020 which has current benchmark problems was used to test the performance of the developed FDB-RUN algorithm. 10 different unconstrained benchmark problems taken from CEC 2020 were designed by arranging them in 30/50/100 dimensions. Experimental studies were carried out using the designed benchmark problems and analyzed with Friedman and Wilcoxon statistical test methods. According to the results of the analysis, it was seen that the FDB-RUN variations showed a superior performance compared to the base algorithm (RUN) in all experimental studies. In particular, it has been shown to provide more effective results for the continuous optimization of high-dimensional problems.

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An Improved War Strategy Optimization Algorithm for Big Data Analytics

Han,

Xu,

2023

Communications in Computer and Information Science

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Arz-Talep Tabanli Opti̇mi̇zasyon Algori̇tmasinin FDB Yöntemi̇ İle İyi̇leşti̇ri̇lmesi̇: Mühendi̇sli̇k Tasarim Problemleri̇ Üzeri̇ne Kapsamli Bi̇r Araştirma

Cited by 10 publications

References 38 publications

Global Optimizasyonu için Uygunluk Mesafe Dengesi Tabanlı Rehber Mekanızmasıyla Slime Mould Optimize Edicinin İyileştirilmesi

Global Optimizasyonu için Uygunluk Mesafe Dengesi Tabanlı Rehber Mekanızmasıyla Slime Mould Optimize Edicinin İyileştirilmesi

Improved Runge Kutta Optimizer with Fitness Distance Balance-Based Guiding Mechanism for Global Optimization of High-Dimensional Problems

An Improved War Strategy Optimization Algorithm for Big Data Analytics

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