Browsing by Author "Yildizdan, G."

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Citation - WoS: 2
Citation - Scopus: 6
A New Binary Arithmetic Optimization Algorithm for Uncapacitated Facility Location Problem
(Springer Science and Business Media Deutschland GmbH, 2023) Baş, Emine; Yildizdan, G.
Arithmetic Optimization Algorithm (AOA) is a heuristic method developed in recent years. The original version was developed for continuous optimization problems. Its success in binary optimization problems has not yet been sufficiently tested. In this paper, the binary form of AOA (BinAOA) has been proposed. In addition, the candidate solution production scene of BinAOA is developed with the xor logic gate and the BinAOAX method was proposed. Both methods have been tested for success on well-known uncapacitated facility location problems (UFLPs) in the literature. The UFL problem is a binary optimization problem whose optimum results are known. In this study, the success of BinAOA and BinAOAX on UFLP was demonstrated for the first time. The results of BinAOA and BinAOAX methods were compared and discussed according to best, worst, mean, standard deviation, and gap values. The results of BinAOA and BinAOAX on UFLP are compared with binary heuristic methods used in the literature (TSA, JayaX, ISS, BinSSA, etc.). As a second application, the performances of BinAOA and BinAOAX algorithms are also tested on classical benchmark functions. The binary forms of AOA, AOAX, Jaya, Tree Seed Algorithm (TSA), and Gray Wolf Optimization (GWO) algorithms were compared in different candidate generation scenarios. The results showed that the binary form of AOA is successful and can be preferred as an alternative binary heuristic method. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Citation - WoS: 13
Citation - Scopus: 16
A New Binary Coati Optimization Algorithm for Binary Optimization Problems
(Springer Science and Business Media Deutschland GmbH, 2023) Yildizdan, G.; Baş, Emine
The coati optimization algorithm (COA) is a recently proposed heuristic algorithm. The COA algorithm, which solved the continuous optimization problems in its original paper, has been converted to a binary optimization solution by using transfer functions in this paper. Thus, binary COA (BinCOA) is proposed for the first time in this study. In this study, twenty transfer functions are used (four S-shaped, four V-shaped, four Z-shaped, four U-shaped, and four taper-shaped transfer functions). Thus, twenty variations of BinCOA are obtained, and the effect of each transfer function on BinCOA is examined in detail. The knapsack problem (KP) and uncapacitated facility location problem (UFLP), which are popular binary optimization problems in the literature, are chosen to test the success of BinCOA. In this study, small-, middle-, and large-scale KP and UFLP datasets are selected. Real-world problems are not always low-dimensional. Although a binary algorithm sometimes shows superior success in low dimensions, it cannot maintain the same success in large dimensions. Therefore, the success of BinCOA has been tested and demonstrated not only in low-dimensional binary optimization problems, but also in large-scale optimization problems. The most successful transfer function is T3 for KPs and T20 for UFLPs. This showed that S-shaped and taper-shaped transfer functions obtained better results than others. After determining the most successful transfer function for each problem, the enhanced BinCOA (EBinCOA) is proposed to increase the success of BinCOA. Two methods are used in the development of BinCOA. These are the repair method and the XOR gate method. The repair method repairs unsuitable solutions in the population in a way that competes with other solutions. The XOR gate is one of the most preferred methods in the literature when producing binary solutions and supports diversity. In tests, EBinCOA has achieved better results than BinCOA. The added methods have proven successful on BinCOA. In recent years, the newly proposed evolutionary mating algorithm, fire hawk optimizer, honey badger algorithm, mountain gazelle optimizer, and aquila optimizer have been converted to binary using the most successful transfer function selected for KP and UFLP. BinCOA and EBinCOA have been compared with these binary heuristic algorithms and literature. In this way, their success has been demonstrated. According to the results, it has been seen that EBinCOA is a successful and preferable algorithm for binary optimization problems. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.