LMRAOA: An improved arithmetic optimization algorithm with multi-leader and high-speed jumping based on opposition-based learning solving engineering and numerical problems

Zhang, Yu-Jun; Wang, Yufei; Yan, Yu-Xin; Zhao, Juan; Gao, Zheng-Ming

doi:10.1016/j.aej.2022.06.017

Cited by 24 publications

(4 citation statements)

References 71 publications

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“…The AOA algorithm is widely used for solving several optimization problems because of the simplicity, robustness, and effectiveness of the results in terms of solving optimization problems [32]. However, AOA would also easily fall in the local optima for optimizing some complex issues, and the exploration and exploitation capabilities are less significant [33].…”

Section: Refmentioning

confidence: 99%

Accelerated Arithmetic Optimization Algorithm by Cuckoo Search for Solving Engineering Design Problems

Hijjawi,

Alshinwan,

Khashan

et al. 2023

Processes

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Several metaheuristic algorithms have been implemented to solve global optimization issues. Nevertheless, these approaches require more enhancement to strike a suitable harmony between exploration and exploitation. Consequently, this paper proposes improving the arithmetic optimization algorithm (AOA) to solve engineering optimization issues based on the cuckoo search algorithm called AOACS. The developed approach uses cuckoo search algorithm operators to improve the ability of the exploitation operations of AOA. AOACS enhances the convergence ratio of the presented technique to find the optimum solution. The performance of the AOACS is examined using 23 benchmark functions and CEC-2019 functions to show the ability of the proposed work to solve different numerical optimization problems. The proposed AOACS is evaluated using four engineering design problems: the welded beam, the three-bar truss, the stepped cantilever beam, and the speed reducer design. Finally, the results of the proposed approach are compared with state-of-the-art approaches to prove the performance of the proposed AOACS approach. The results illustrated an outperformance of AOACS compared to other methods of performance measurement.

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Section: Refmentioning

confidence: 99%

Accelerated Arithmetic Optimization Algorithm by Cuckoo Search for Solving Engineering Design Problems

Hijjawi,

Alshinwan,

Khashan

et al. 2023

Processes

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“…The chaotic mappings are generated by iterations of the deterministic nonlinear difference equation. The motion orbit is disordered, but its internal evolution is regular and can traverse the state space ( Zhang et al, 2022 ).…”

Section: Related Workmentioning

confidence: 99%

Strip thickness prediction method based on improved border collie optimizing LSTM

Sun

Lin²,

Zhou

et al. 2022

PeerJ Computer Science

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Background The thickness accuracy of strip is an important indicator to measure the quality of strip, and the control of the thickness accuracy of strip is the key for the high-quality strip products in the rolling industry. Methods A thickness prediction method of strip based on Long Short-Term Memory (LSTM) optimized by improved border collie optimization (IBCO) algorithm is proposed. First, chaotic mapping and dynamic weighting strategy are introduced into IBCO to overcome the shortcomings of uneven initial population distribution and inaccurate optimization states of some individuals in Border Collie Optimization (BCO). Second, Long Short-Term Memory (LSTM) which can effectively deal with time series data and alleviate long-term dependencies is adopted. What’s more, IBCO is utilized to optimize parameters to mitigate the influence of hyperparameters such as the number of hidden neurons and learning rate on the prediction accuracy of LSTM, so IBCO-LSTM is established. Results The experiments are carried out on the measured strip data, which proves the excellent prediction performance of IBCO-LSTM. The experiments are carried out on the actual strip data, which prove that IBCO-LSTM has excellent capability of prediction.

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“…In this section, 10 functions are selected from the CEC 2020 used in [9] to explore the performance of CDOWDE. Under identical conditions, CDOWDE is compared with four methods including WDE, OMLDE, CSO, and PSO.…”

Section: Experiments Setupmentioning

confidence: 99%

An Improved Weighted Differential Evolution Algorithm Based on the Chaotic Mapping and Dynamic Reverse Learning Strategy

Zhang

et al. 2022

J. Phys.: Conf. Ser.

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The weighted differential evolution algorithm has the disadvantage of slipping into local optima and low convergence. To address these problems, an extended weighted differential evolution algorithm based on the chaotic mapping and optimal-worst dynamic opposite learning strategy is proposed. Firstly, the chaotic sequence generated by improved one-dimensional Logistic-Chebyshev mapping is employed to modify the starting population generated uniformly within the original algorithm, which aids in broadening the richness of the population. Secondly, to augment the probability of obtaining global optimum, an optimal-worst dynamic reverse learning strategy is used. Finally, a new evolutionary step size generation method is incorporated into the algorithm, which helps to acquire better solutions. The experimental outcomes obtained by testing 10 benchmark functions in CEC 2020 indicate that the proposed method has been greatly improved in accuracy.

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LMRAOA: An improved arithmetic optimization algorithm with multi-leader and high-speed jumping based on opposition-based learning solving engineering and numerical problems

Cited by 24 publications

References 71 publications

Accelerated Arithmetic Optimization Algorithm by Cuckoo Search for Solving Engineering Design Problems

Accelerated Arithmetic Optimization Algorithm by Cuckoo Search for Solving Engineering Design Problems

Strip thickness prediction method based on improved border collie optimizing LSTM

An Improved Weighted Differential Evolution Algorithm Based on the Chaotic Mapping and Dynamic Reverse Learning Strategy

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