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KÜÇÜKOĞLU, İLKER

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    Publication
    Adaptive electromagnetic field optimization algorithm for the solar cell parameter identification problem
    (Hindawi, 2019-04-28) Küçükoğlu, İlker; KÜÇÜKOĞLU, İLKER; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü.; 0000-0002-5075-0876; D-8543-2015
    Solar cell parameter identification problem (SCPIP) is one of the most studied optimization problems in the field of renewable energy since accurate estimation of model parameters plays an important role to increase their efficiency. The SCPIP is aimed at optimizing the performance of solar cells by estimating the best parameter values of the solar cells that produce an accurate approximation between the current vs. voltage (I-V) measurements. To solve the SCPIP efficiently, this paper introduces an adaptive variant of the electromagnetic field optimization (EFO) algorithm, named adaptive EFO (AEFO). The EFO simulates the attraction-repulsion mechanism between particles of electromagnets having different polarities. The main idea behind the EFO is to guide electromagnetic particles towards global optimum by the attraction-repulsion forces and the golden ratio. Distinct from the EFO, the AEFO searches the solution space with an adaptive search procedure. In the adaptive search strategy, the selection probability of a better solution is increased adaptively whereas the selection probability of worse solutions is reduced throughout the search progress. By employing the adaptive strategy, the AEFO is able to maintain the balance between exploration and exploitation more efficiently. Further, new boundary control and randomization procedures for the candidate electromagnets are presented. To identify the performance of the proposed algorithm, two different benchmark problems are taken into account in the computational studies. First, the AEFO is performed on global optimization benchmark functions and compared to the EFO. The efficiency of the AEFO is identified by statistical significance tests. Then, the AEFO is implemented on a well-known SCPIP benchmark problem set formed as a result of real-life physical experiments based on single- and double-diode models. To validate the performance of the AEFO on the SCPIP, extensive experiments are carried out, where the AEFO is tested against the original EFO, AEFO variants, and novel metaheuristic algorithms. Results of the computational studies reveal that the AEFO exhibits superior performance and outperforms other competitor algorithms.
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    Publication
    A hybrid meta-heuristic algorithm for vehicle routing and packing problem with cross-docking
    (Springer, 2019-12-01) Küçükoğlu, İlker; Öztürk, Nursel; KÜÇÜKOĞLU, İLKER; ÖZTÜRK, NURSEL; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü; 0000-0002-5075-0876; 0000-0002-9835-0783; AAG-9336-2021; D-8543-2015
    Cross-docking is a relatively new logistics strategy that has a great potential to eliminate storage cost and speed up the product flows. This paper considers the vehicle routing and packing problem with cross-docking and presents a mixed integer linear mathematical model. In the model, a set of trucks are used to transport products from suppliers to customers through cross-docking centers. Each supplier and customer node can be visited only once and directly shipping is not allowed from suppliers to customers. Moreover, truck capacities are identified with physical dimensional limits on the contrary of weight or amount of load. The objective of the study is to determine the vehicle routes that minimize the total distance. Due to the complexity of the mathematical model, a hybrid meta-heuristic algorithm (HMA), which integrates tabu search (TS) algorithm within simulated annealing (SA) algorithm, is proposed to solve the problem. Proposed HMA is tested on a well-known benchmark problem data set and compared with the SA and TS solutions. Results show that proposed HMA can produce effective solutions and outperforms the SA and TS especially for the large-sized problems.