A Novel Hybrid Metaheuristic Based on Augmented Grey Wolf Optimizer and Cuckoo Search for Global Optimization

Sharma, Surya Prakash; Kapoor, Ridhi; Dhiman, Sanjeev

doi:10.1109/icsccc51823.2021.9478142

Cited by 25 publications

(10 citation statements)

References 9 publications

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“…The gravitational search results were compared with the GA during fault conditions. In [11], the six PI controllers were tuned using the hybrid augmented grey wolf optimizer and cuckoo search (AGWO-CS) algorithm [24]. The results were compared with PI controllers optimized using COOT and PSO during different grid faults.…”

Section: Literature Overviewmentioning

confidence: 99%

Optimal Design of Fractional-Order PID Controllers for a Nonlinear AWS Wave Energy Converter Using Hybrid Jellyfish Search and Particle Swarm Optimization

Ali,

Ahmed,

Hasanien

et al. 2023

Fractal Fract

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In this study, a nonlinear Archimedes wave swing (AWS) energy conversion system was employed to enable the use of irregular sea waves to provide useful electricity. Instead of the conventional PI controllers used in prior research, this study employed fractional-order PID (FOPID) controllers to control the back-to-back configuration of AWS. The aim was to maximize the energy yield from waves and maintain the grid voltage and the capacitor DC link voltage at predetermined values. In this study, six FOPID controllers were used to accomplish the control goals, leading to an array of thirty parameters required to be fine-tuned. In this regard, a hybrid jellyfish search optimizer and particle swarm optimization (HJSPSO) algorithm was adopted to select the optimal control gains. Verification of the performance of the proposed FOPID control system was achieved by comparing the system results to two conventional PID controllers and one FOPID controller. The conventional PID controllers were tuned using a recently presented metaheuristic algorithm called the Coot optimization algorithm (COOT) and the classical particle swarm optimization algorithm (PSO). Moreover, the FOPID was also tuned using the well-known genetic algorithm (GA). The system investigated in this study was subjected to various unsymmetrical and symmetrical fault disturbances. When compared with the standard COOT-PID, PSO-PID, and GA-FOPID controllers, the HJSPSO-FOPID results show a significant improvement in terms of performance and preserving control goals during system instability

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Section: Literature Overviewmentioning

confidence: 99%

Optimal Design of Fractional-Order PID Controllers for a Nonlinear AWS Wave Energy Converter Using Hybrid Jellyfish Search and Particle Swarm Optimization

Ali,

Ahmed,

Hasanien

et al. 2023

Fractal Fract

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“…Based on GWO, Sharma et al. proposed Augmented Grey Wolf Optimizer and Cuckoo Search (AGWOCS), 27 which introduced the Levy flight and nest parasitism of the cuckoo into the GWO and significantly enhanced its search speed and global search capability. But AGWOCS still has areas for improvement.…”

Section: Introductionmentioning

confidence: 99%

“…proposed Augmented Grey Wolf Optimizer and Cuckoo Search (AGWOCS), 27 � A novel dual-manipulator collaborative robot system for NOTES was optimized to achieve optimal dexterity in the dualmanipulator collaborative space. Simulations and experiments demonstrate that the parameters characterizing the robot's dexterity are improved by 24.91%.…”

mentioning

confidence: 99%

Dexterity enhancement of continuum robot for natural orifice transluminal endoscopic surgery in the dual‐manipulator collaborative space

Cheng

Zhang

Sun³

et al. 2023

Robotics Computer Surgery

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Background: Suturing and knotting in Natural Orifice Transluminal Endoscopic Surgery (NOTES) requires the robot not only to be able to work with multiple manipulators but also to have a high degree of dexterity. However, little attention has been paid to the design and enhancement of dexterity in multi-manipulated robots. Methods:In this paper, the dexterity of a new dual-manipulator collaborative continuum robot in collaborative space is analyzed and enhanced. A kinematic model of the continuum robot was developed. The dexterity function of the robot is evaluated based on the concepts of the low-Degree-of-Freedom Jacobian matrix.Then an Adaptive Parameter Gray Wolf Coupled Cuckoo Optimization Algorithm with faster convergence and higher accuracy is innovatively proposed to optimize the objective function. Finally, experiments demonstrate that the dexterity of the optimized continuum robot is enhanced. Results:The optimization results show that the optimized dexterity is 24.91% better than the initial state. Conclusion:Through the work of this paper, the robot for NOTES can perform suturing and knot more dexterously, which has significant implications for the treatment of digestive tract diseases. K E Y W O R D Scontinuum robots, dexterity, low-DOF Jacobian matrix, robot for NOTES | INTRODUCTIONSuturing and knotting in traditional Natural Orifice Transluminal Endoscopic Surgery (NOTES) is mainly done by endoscopy and human hands. 1 In NOTES, suturing is usually made with purse strings, which are easy to operate, the surface after suturing is smooth and promotes healing. Currently, the purse-string method is implemented with titanium clips, which are easily operated and accurately positioned. However, they suffer from the problem of dislodging easily, causing wound dehiscence and rebleeding. Therefore, new types of

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“…By evaluating the success of newly developed meta-heuristic optimization algorithms and comparing them with previously published algorithms, new studies on improving existing optimization algorithms or developing new optimization algorithms based on successful algorithms are added to the literature on a daily basis. In this context, Artificial Water Drop Algorithm [28], Mexican Axolotl Optimization: a novel bio-inspired heuristic [3], Tunicate Swarm Algorithm [2], Tuna Swarm Optimization [1,8], Equilibrium Slime Mould Algorithm [8,26], Dingo Optimization Algorithm [29], Leader Harris hawks optimization [5], Differential Squirrel Search Algorithm [9,10], Leader Slime Mould Algorithm [31], Adaptive Opposition Slime Mould Algorithm [32], CLA-New Meta-Heuristic Algorithm [38], Hybrid Augmented Grey Wolf Optimizer and Cuckoo Search [33,34], Child Drawing Development Optimization Algorithm [34], Golden Eagle Optimizer [35], Bald eagle search Optimization algorithm [14], Chimp Optimization Algorithm [39], Lévy Flight Distribution [6] and Harris hawks optimization [36] are some of them. This paper compares the performances of sixteen meta-heuristic optimization algorithms presented in the literature between 2021 and 2022.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Recent Meta-Heuristic Optimization Algorithms Using Different Benchmark Functions

Dirik

2022

Journal of Mathematical Sciences and Modelling

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Metaheuristic optimization algorithms are used in many application areas to solve optimization problems. In recent decades, the popularity of metaheuristic optimization algorithms has increased compared to deterministic search algorithms in solving global optimization problems. But none of the algorithms can solve all optimization problems equally well. Therefore, researchers focus on either improving existing meta-heuristic optimization algorithms or introducing new algorithms. Many alternative metaheuristic algorithms inspired by nature have been developed to solve complex optimization problems. It is important to compare the performances of the developed algorithms through statistical analysis and determine the better algorithm. This paper compares the performances of sixteen proposed metaheuristic optimization algorithms (AWDA, MAO, TSA, TSO, ESMA, DOA, LHHO, DSSA, LSMA, AOSMA, AGWOCS, CDDO, GEO, BES, LFD, HHO) between 2021 and 2022. These algorithms have been tested with various test functions, including single-mode, multi-mode, and fixed-size multi-mode benchmark functions. Based on the obtained results, the success of the algorithms is visualized with the frequency histogram diagram.

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A Novel Hybrid Metaheuristic Based on Augmented Grey Wolf Optimizer and Cuckoo Search for Global Optimization

Cited by 25 publications

References 9 publications

Optimal Design of Fractional-Order PID Controllers for a Nonlinear AWS Wave Energy Converter Using Hybrid Jellyfish Search and Particle Swarm Optimization

Optimal Design of Fractional-Order PID Controllers for a Nonlinear AWS Wave Energy Converter Using Hybrid Jellyfish Search and Particle Swarm Optimization

Dexterity enhancement of continuum robot for natural orifice transluminal endoscopic surgery in the dual‐manipulator collaborative space

Comparison of Recent Meta-Heuristic Optimization Algorithms Using Different Benchmark Functions

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