Boosting quantum rotation gate embedded slime mould algorithm

Yu, Caiyang; Heidari, Ali Asghar; Xue, Xiao; Zhang, Lejun; Chen, Huiling; Chen, Weibin

doi:10.1016/j.eswa.2021.115082

Cited by 53 publications

(16 citation statements)

References 120 publications

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“…Similar to many other recently proposed optimization algorithms, including Harris hawks optimization (HHO) [28], the Runge Kutta optimizer (RUN) [29], the colony predation algorithm (CPA) [30], and hunger games search (HGS) [31], SMA is a novel and high-performing swarm intelligence optimization algorithm that was developed by Li et al [11], who were motivated by the slime mould's foraging behavior. Since its introduction, SMA has been applied to many problems such as image segmentation [32,33], engineering design [34], parameter identification in photovoltaic models [35], medical decision-making [36], and multi-objective problems [37]. In this section, some mathematical models related to the mechanisms and characteristics of SMA are presented.…”

Section: Slime Mould Algorithmmentioning

confidence: 99%

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

2022

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In this study, the authors aimed to study an effective intelligent method for employment stability prediction in order to provide a reasonable reference for postgraduate employment decision and for policy formulation in related departments. First, this paper introduces an enhanced slime mould algorithm (MSMA) with a multi-population strategy. Moreover, this paper proposes a prediction model based on the modified algorithm and the support vector machine (SVM) algorithm called MSMA-SVM. Among them, the multi-population strategy balances the exploitation and exploration ability of the algorithm and improves the solution accuracy of the algorithm. Additionally, the proposed model enhances the ability to optimize the support vector machine for parameter tuning and for identifying compact feature subsets to obtain more appropriate parameters and feature subsets. Then, the proposed modified slime mould algorithm is compared against various other famous algorithms in experiments on the 30 IEEE CEC2017 benchmark functions. The experimental results indicate that the established modified slime mould algorithm has an observably better performance compared to the algorithms on most functions. Meanwhile, a comparison between the optimal support vector machine model and other several machine learning methods on their ability to predict employment stability was conducted, and the results showed that the suggested the optimal support vector machine model has better classification ability and more stable performance. Therefore, it is possible to infer that the optimal support vector machine model is likely to be an effective tool that can be used to predict employment stability.

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Section: Slime Mould Algorithmmentioning

confidence: 99%

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

2022

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“… 36 developed multi-objective SMA (MOSMA) for solving complicated multi-objective engineering design problems in the real world; Yu et al . 37 proposed an improved SMA (WQSMA) that enhanced the original SMA's robustness by using a quantum rotation gate (QRG) and a water cycle operator. Houssein et al .…”

Section: Introductionmentioning

confidence: 99%

An equilibrium optimizer slime mould algorithm for inverse kinematics of the 7-DOF robotic manipulator

Yin

Luo

Zhou

et al. 2022

Sci Rep

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In order to solve the inverse kinematics (IK) of complex manipulators efficiently, a hybrid equilibrium optimizer slime mould algorithm (EOSMA) is proposed. Firstly, the concentration update operator of the equilibrium optimizer is used to guide the anisotropic search of the slime mould algorithm to improve the search efficiency. Then, the greedy strategy is used to update the individual and global historical optimal to accelerate the algorithm’s convergence. Finally, the random difference mutation operator is added to EOSMA to increase the probability of escaping from the local optimum. On this basis, a multi-objective EOSMA (MOEOSMA) is proposed. Then, EOSMA and MOEOSMA are applied to the IK of the 7 degrees of freedom manipulator in two scenarios and compared with 15 single-objective and 9 multi-objective algorithms. The results show that EOSMA has higher accuracy and shorter computation time than previous studies. In two scenarios, the average convergence accuracy of EOSMA is 10e−17 and 10e−18, and the average solution time is 0.05 s and 0.36 s, respectively.

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“…Liu et al [22], Mostafa et al [23] and Yousri et al [24] used hybrid and improved SMA to estimate parameters of solar photovoltaic cells, respectively; Agarwal and Bharti [25] applied improved SMA to the collision-free shortest time path planning of mobile robots; Rizk-Allah et al [26] proposed a chaos-opposition-enhanced SMA (CO-SMA) to minimize the energy costs of wind turbines at highaltitude sites; Hassan et al [27] applied improved SMA (ISMA) to efficiently solve economic and emission dispatch (EED) problem with single and dual objectives; Abdollahzadeh et al [28] proposed a binary SMA to solve the 0-1 knapsack problem; Zubaidi et al [29] combined SMA and artificial neural network (ANN) for urban water demand prediction; Chen and Liu [30] combined Kmeans clustering and chaotic SMA with support vector regression to obtain higher prediction accuracy; Ekinci et al [31] applied SMA to the power system stabilizer design (PSSD); Wazery et al [32] combined SMA and K-nearest neighbor for disease classification and diagnosis system; Wei et al [33] developed a simpler SMA for the problem of wireless sensor network coverage; Wei et al [34] proposed an enhanced SMA in power systems for optimal reactive power dispatch; Premkumar et al [35] and Houssein et al [36] developed multi-objective SMA (MOSMA) for solving complicated multi-objective engineering design problems in the real world; Yu et al [37] proposed an improved SMA (WQSMA) that enhanced the original SMA's robustness by using a quantum rotation gate (QRG) and a water cycle operator. Houssein et al [38] proposed a hybrid SMA and adaptive guided differential evolution (AGDE) algorithm, which makes a good combination of SMA's exploitation ability and AGDE's exploration ability.…”

Section: Introductionmentioning

confidence: 99%

An Equilibrium Optimizer Slime Mould Algorithm For Inverse Kinematics Of The 7-DOF Robotic Manipulator

Yin

Luo

Zhou

et al. 2022

Preprint

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In order to solve the inverse kinematics (IK) of complex manipulators efficiently, a hybrid equilibrium optimizer slime mould algorithm (EOSMA) is proposed. Firstly, the concentration update operator of the equilibrium optimizer is used to guide the anisotropic search of the slime mould algorithm to improve the search efficiency. Then, the greedy strategy is used to update the individual and global historical optimal to accelerate the algorithm’s convergence. Finally, the random difference mutation operator is added to EOSMA to increase the probability of escaping from the local optimum. On this basis, a multi-objective version of EOSMA is proposed. Then, EOSMA and MOEOSMA are applied to the IK of the 7-DOF manipulator in two scenarios and compared with 15 single-objective and 3 multi-objective algorithms. The results show that EOSMA has higher accuracy and shorter computation time than previous studies. In two scenarios, the average convergence accuracy of EOSMA is 10e-17 and 10e-18, and the average solution time is 0.05s and 0.36s, respectively.

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Boosting quantum rotation gate embedded slime mould algorithm

Cited by 53 publications

References 120 publications

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

An equilibrium optimizer slime mould algorithm for inverse kinematics of the 7-DOF robotic manipulator

An Equilibrium Optimizer Slime Mould Algorithm For Inverse Kinematics Of The 7-DOF Robotic Manipulator

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