Bird swarm algorithms with chaotic mapping

Altay, Elif Varol; Alataş, Bilal

doi:10.1007/s10462-019-09704-9

Cited by 115 publications

(29 citation statements)

References 41 publications

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“…The flow chart of the BSA is shown in Figure 3. More explanations regarding the BSA algorithm can be found in the literature Miramontes et al, 2018;Varol Altay and Alatas, 2019).…”

Section: Bird Swarm Algorithmmentioning

confidence: 99%

A Comparative Study of Six Hybrid Prediction Models for Uniaxial Compressive Strength of Rock Based on Swarm Intelligence Optimization Algorithms

Zhou

Luo

et al. 2022

Front. Earth Sci.

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Uniaxial compressive strength (UCS) is a significant parameter in mining engineering and rock engineering. The laboratory rock test is time-consuming and economically costly. Therefore, developing a reliable and accurate UCS prediction model through easily obtained rock parameters is a good way. In this paper, we set five input parameters and compare six hybrid models based on BP neural network and six swarm intelligence optimization algorithms–bird swarm algorithm (BSA), grey wolf optimization (GWO), whale optimization algorithm (WOA), seagull optimization algorithm (SOA), lion swarm optimization (LSO), firefly algorithm (FA) with the accuracy of two single models without optimization–BP neural network and random forest algorithm. Finally, the above eight models were evaluated and compared by root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and a10 index to obtain the most suitable prediction model. It is indicated that the best prediction model is the FA-BP model, with a RMSE value of 4.883, a MAPE value of 0.063, and a R2 of 0.985, and an a10 index of 0.967. Furthermore, the normalized mutual information sensitivity analysis shows that point load strength is the most effective parameters on the UCS, respectively.

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“…The flow chart of the BSA is shown in Figure 3. More explanations regarding the BSA algorithm can be found in the literature Miramontes et al, 2018;Varol Altay and Alatas, 2019).…”

Section: Bird Swarm Algorithmmentioning

confidence: 99%

A Comparative Study of Six Hybrid Prediction Models for Uniaxial Compressive Strength of Rock Based on Swarm Intelligence Optimization Algorithms

Zhou

Luo

et al. 2022

Front. Earth Sci.

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“…In the field of optimization, chaotic mapping is widely used in intelligent optimization algorithms because of its ergodicity, randomness and nonlinearity [17]. At present, Logistic chaotic map and Tent chaotic map are commonly used [18].…”

Section: ) Initialization Of Logistic-tent Chaotic Mapmentioning

confidence: 99%

Research on Multiaircrafts Cooperative Arraying to Jam Based on Multiobjective Moth-Flame Optimization Algorithm

Shi

et al. 2022

IEEE Access

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The problem of cooperative arraying to jam is an important part of EW mission planning. Aiming at the problem that multi-objective optimization algorithm is easy to fall into local optimum and converge in three-objective optimization, a multi-aircraft jamming and cooperative arraying method based on improved multi-objective Moth-flame optimization algorithm is proposed. Firstly, the simulation environment is established by using digital elevation map and radar detection model. Then, based on the multi-objective Moth-flame optimization algorithm, the population initialization is completed by using Logistic-Tent chaotic map, which increases the diversity and uniformity of the solution and improves the search ability of the algorithm; Then, the decision factor and Gaussian difference mutation are introduced, which makes the algorithm not only accept the current solution with a certain probability, but also jump out of the current solution and search again according to the disturbance, thus enhancing the search ability of the algorithm; Finally, by comparing with NSGA-II, MOEA/D, MOPSO and NSMFO algorithms on test functions of ZDT and DTLZ series, the performance of the algorithm is verified, and it is proved that multiobjective Moth-flame optimization algorithm is better than other algorithms in both convergence and diversity. In addition, compared with the NSMFO and MOEA/D algorithms in the arraying simulation experiment. The values of the interference power, the width of the route safety zone and the detection area of the radar obtained by the algorithm in this paper, are 117.9 kw, 46 km, and 1727 km 2 . Compared with the results of the other two algorithms, the effectiveness of interference is improved by 39.8%, 22.8% and 41.9% respectively.

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“…At present, there have been a large number of studies on the application of chaotic mapping to the optimization of swarm intelligence algorithms [18]. Circle map is an excellent mapping function with good order, existence and uniqueness [19].…”

Section: Adaptive Hybrid Sparrow Algorithm Based On the Construction Of ''Convergence And Divergence'' Similarity A Improved Circle Chaosmentioning

confidence: 99%

A Hybrid Sparrow Search Algorithm Based on Constructing Similarity

Liu

Wang

2021

IEEE Access

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Sparrow search algorithm (SSA) is easy to fall into local convergence and convergence stagnation. In order to solve these problems, this paper introduced Circle chaos map into the original SSA to improve its global search ability at the beginning of iteration. Meanwhile, it introduced T-distribution variation to affect the sparrow population position update rules in different iteration periods. Finally, we constructed the ''similarity function'' to measure the ''dispersion'' of the sparrow population, and formulated the search rules of the sparrow population under different ''dispersion''. In order to test the specific optimization performance of the proposed algorithm, the test results of 54 test functions are compared with those of 9 other algorithms which are widely used, and then the test results are analyzed using non-parametric tests in statistics. At the same time, this paper introduces this algorithm into three concrete engineering test problems for testing. The results of these tests all prove that the proposed algorithm has stronger global optimization ability and higher convergence precision compared with other algorithms.

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Bird swarm algorithms with chaotic mapping

Cited by 115 publications

References 41 publications

A Comparative Study of Six Hybrid Prediction Models for Uniaxial Compressive Strength of Rock Based on Swarm Intelligence Optimization Algorithms

A Comparative Study of Six Hybrid Prediction Models for Uniaxial Compressive Strength of Rock Based on Swarm Intelligence Optimization Algorithms

Research on Multiaircrafts Cooperative Arraying to Jam Based on Multiobjective Moth-Flame Optimization Algorithm

A Hybrid Sparrow Search Algorithm Based on Constructing Similarity

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