MOMPA: Multi-objective marine predator algorithm

Zhong, Keyu; Zhou, Guo; Deng, Wu; Zhou, Yongquan; Luo, Qifang

doi:10.1016/j.cma.2021.114029

Cited by 86 publications

(35 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…  uuuu r uuur uu r (19) where max uuuu r and min uuur are two vectors that store the maximum and minimum fitness of each objective, respectively, and Archivesize is the archive size [54].…”

Section: Max Min Dr Archivesizementioning

confidence: 99%

“…To verify the efficiency of EOSMA in solving the IK problem of complex manipulator, it is compared with slime mould algorithm (SMA) [14], equilibrium optimizer (EO) [39], manta ray foraging optimization (MRFO) [40], marine predators algorithm (MPA) [41], pathfinder algorithm (PFA) [42], flower pollination algorithm (FPA) [43], differential evolution (DE) [44], gradient-based optimizer (GBO) [45], teaching-learning-based optimization (TLBO) [46], Harris hawks optimization (HHO) [47], improved grey wolf optimizer (IGWO) [48], hybrid PSO and gravitational search algorithm (PSOGSA) [49], centroid opposition-based differential evolution (CODE) [50], multi-trial vectorbased differential evolution (MTDE) [51], self-adaptive spherical search algorithm (SASS) [52] and the results of previous studies. Then, a multi-objective EOSMA (MOEOSMA) is proposed and compared with MOSMA [35], multi-objective PSO (MOPSO) [53], and multi-objective MPA (MOMPA) [54] on the IK problem of a 7 degrees of freedom (DOF) manipulator. This paper's primary contributions are as follows:…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Yin

Luo

Zhou

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Max Min Dr Archivesizementioning

confidence: 99%

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…A chaotic ant colony optimization (CACO) algorithm is a combination of chaos search and the ant colony optimization algorithm. After the ant colony search is completed, chaos is used to improve the search accuracy and avoid falling into the local optimum [28][29][30][31][32][33][34][35][36][37][38][39][40]. Therefore, the CACO algorithm takes on a global optimization ability.…”

Section: Chaotic Ant Colony Optimization Algorithmmentioning

confidence: 99%

Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

Cui

Guan

2021

Applied Sciences

Self Cite

View full text Add to dashboard Cite

Aiming at the problems of poor decomposition quality and the extraction effect of a weak signal with strong noise by empirical mode decomposition (EMD), a novel fault diagnosis method based on cascaded adaptive second-order tristable stochastic resonance (CASTSR) and EMD is proposed in this paper. In the proposed method, low-frequency interference components are filtered by using high-pass filtering, and the restriction conditions of stochastic resonance theory are solved by using an ordinary variable-scale method. Then, a chaotic ant colony optimization algorithm with a global optimization ability is employed to adaptively adjust the parameters of the second-order tristable stochastic resonance system to obtain the optimal stochastic resonance, and noise reduction pretreatment technology based on CASTSR is developed to enhance the weak signal characteristics of low frequency. Next, the EMD is employed to decompose the denoising signal and extract the characteristic frequency from the intrinsic mode function (IMF), so as to realize the fault diagnosis of rolling bearings. Finally, the numerical simulation signal and actual bearing fault data are selected to prove the validity of the proposed method. The experiment results indicate that the proposed fault diagnosis method can enhance the decomposition quality of the EMD, effectively extract features of weak signals, and improve the accuracy of fault diagnosis. Therefore, the proposed fault diagnosis method is an effective fault diagnosis method for rotating machinery.

show abstract

“…Gao et al [13] proposed a multi-independent population genetic algorithm (MGA) combined with a subdomain model to improve the magnetic field distribution, cost, and efficiency of SPMSM, which proves the advantages of the MGA optimization method. In addition, some new algorithms also have been proposed in recent years [14][15][16][17][18][19][20][21][22][23]. As a new algorithm of bionic swarm intelligence, the sparrow search algorithm was proposed by Xue and Shen [24] in 2020.…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation

Yang

Liu

et al. 2021

Applied Sciences

Self Cite

View full text Add to dashboard Cite

Aiming at the problems of the basic sparrow search algorithm (SSA) in terms of slow convergence speed and the ease of falling into the local optimum, the chaotic mapping strategy, adaptive weighting strategy and t-distribution mutation strategy are introduced to develop a novel adaptive sparrow search algorithm, namely the CWTSSA in this paper. In the proposed CWTSSA, the chaotic mapping strategy is employed to initialize the population in order to enhance the population diversity. The adaptive weighting strategy is applied to balance the capabilities of local mining and global exploration, and improve the convergence speed. An adaptive t-distribution mutation operator is designed, which uses the iteration number t as the degree of freedom parameter of the t-distribution to improve the characteristic of global exploration and local exploration abilities, so as to avoid falling into the local optimum. In order to prove the effectiveness of the CWTSSA, 15 standard test functions and other improved SSAs, differential evolution (DE), particle swarm optimization (PSO), gray wolf optimization (GWO) are selected here. The compared experiment results indicate that the proposed CWTSSA can obtain higher convergence accuracy, faster convergence speed, better diversity and exploration abilities. It provides a new optimization algorithm for solving complex optimization problems.

show abstract

MOMPA: Multi-objective marine predator algorithm

Cited by 86 publications

References 87 publications

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

Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation

Contact Info

Product

Resources

About