An analysis of the search mechanisms of the bees algorithm

Baronti, Luca; Castellani, Marco

doi:10.1016/j.swevo.2020.100746

Cited by 39 publications

(14 citation statements)

References 43 publications

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“…According to this gure, (1) shows the start time of the simulation and the wing is waiting folded position. (2) shows the position of the wing after 40 ms, and at that moment the wing has turned 43 degrees. (3) shows the position of the wing after 71 ms. And the last picture (4) shows the end of turning and the opening position of the wing.…”

Section: Dynamic Analysis Of the Wing Mechanism And Experimental Resultsmentioning

confidence: 99%

Optimization of Spring Parameters by Using the Bees Algorithm for the Foldable Wing Mechanism

Şahin¹,

Kulunk²

2022

Preprint

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In this study, the design of the torsion and compression springs of the foldable wing mechanism used in the missile is discussed as an optimization problem. The objective functions discussed in this study are to maximize the energies of the springs and to minimize the opening time of the wing. In the optimization studies, the material type of the springs was kept constant and the wire diameter, coiling diameter, coiling number, and deflection were determined as variables. While there are geometric constraints for the variables due to the dimensions of the mechanism, there are also safety factor constraints due to the loads to which the springs are exposed. The Bees Algorithm was used to solve this optimization problem. The energy values obtained with BA were better than the values obtained with the DOE study before. The springs and mechanism designed with the parameters obtained as a result of the optimization were first analysed in the ADAMS program. Afterward, the springs produced were integrated into the real mechanism, and experimental tests were carried out. As a result of the tests, it has been seen that the wing opens in about 90 ms. This value is well below the project target of 200 ms. In addition, there is only a 15 ms difference between the analysis results and the experimental results.

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Section: Dynamic Analysis Of the Wing Mechanism And Experimental Resultsmentioning

confidence: 99%

Optimization of Spring Parameters by Using the Bees Algorithm for the Foldable Wing Mechanism

Şahin¹,

Kulunk²

2022

Preprint

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“…Basically, it does some kind of neighbor region search along with random search and can be used for both integrated and functional optimization. Detailed explanations about the algorithm were provided in references [29,30]. The algorithm starts with the determination of some parameters, such as number of scout bees (n), number of the best site (m) for local search, number of the elite site (e) for local search, number of recruited bees in the elite site (nep), number of recruited bees in the best site (nsp), the size of the neighborhood for each patch (ngh), number of iterations (I).…”

Section: The Traveling Salesman Problem and The Bees Algorithmmentioning

confidence: 99%

Improvement of the Bees Algorithm for Solving the Traveling Salesman Problems

Şahin¹

2022

Bilişim Teknolojileri Dergisi

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The traveling salesman problem (TSP) has been a popular problem studied in the optimization field for a long time. The most successful methods used in solving these difficult problems are metaheuristic algorithms. In this study, an improved version of the bees algorithm is used in the solution of TSPs. In addition to the classical bees algorithm, two different city selection and relocation functions have been developed. With these functions, it is possible to change the location of multiple and variable numbers of cities. These new functions have been added to the continuation of the classical bees algorithm and are used only on the elite site, ensuring that the elite site becomes more elite. Thus, better results could be obtained with less number of iterations and the number of the total evaluation compared to the existing bees algorithm.

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“…The algorithm mimics the nectar source search behavior of honey bees. Basically, it does some kind of neighbor region search along with random search and can be used for both integrated and functional optimization [26]. Detailed explanations about the algorithm were provided in Ref.s [27][28][29].…”

Section: Calculating Weights With Multi Objective Bees Algorithm (Moba)mentioning

confidence: 99%

Optimization of Model Predictive Control Weights for Control of Permanent Magnet Synchronous Motor by Using the Multi Objective Bees Algorithm

Şahin¹

2022

Model-Based Control Engineering - Recent Design and Implementations for Varied Applications

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In this study, the model predictive control (MPC) method was used within the scope of the control of the permanent magnet synchronous motor (PMSM). The strongest aspect of the MPC, the ability to control multiple components with a single function, is also one of the most difficult parts of its design. The fact that each component of the function has different effects requires assigning different weight coefficients to these components. In this study, the Bees Algorithm (BA) is used to determine the weights. Using the multi-objective function in BA, it has been tried to determine the weights that reduce the current values together with the speed error. Three different PI controllers have been designed to compare the MPC method. The coefficients of one of these are tuned with BA. Good Gain Method and Tyreus-Luyben Method were used in the other two. As a result of experimental studies, it has been observed that MPC can control PMSM more smoothly and accurately than PI controllers, with weights optimized with BA. With MPC, PMSM has been controlled with 15% settling time than other controllers and also with no overshoot.

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An analysis of the search mechanisms of the bees algorithm

Cited by 39 publications

References 43 publications

Optimization of Spring Parameters by Using the Bees Algorithm for the Foldable Wing Mechanism

Optimization of Spring Parameters by Using the Bees Algorithm for the Foldable Wing Mechanism

Improvement of the Bees Algorithm for Solving the Traveling Salesman Problems

Optimization of Model Predictive Control Weights for Control of Permanent Magnet Synchronous Motor by Using the Multi Objective Bees Algorithm

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