Optimized cuckoo search algorithm using tournament selection function for robot path planning

Sharma, Kaushlendra; Singh, Shikha; Doriya, Rajesh

doi:10.1177/1729881421996136

Cited by 13 publications

(4 citation statements)

References 38 publications

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“…In the same year, ref. [132] presented an improved CS algorithm based on a competition selection function. Instead of using the random selection idea included in the standard CS, this algorithm replaced it with a competition selection function that calculated the best route for robots from their initial position to their final position.…”

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

Research Progress of Nature-Inspired Metaheuristic Algorithms in Mobile Robot Path Planning

Xu,

Li,

et al. 2023

Electronics

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The research of mobile robot path planning has shifted from the static environment to the dynamic environment, from the two-dimensional environment to the high-dimensional environment, and from the single-robot system to the multi-robot system. As the core technology for mobile robots to realize autonomous positioning and navigation, path-planning technology should plan collision-free and smooth paths for mobile robots in obstructed environments, which requires path-planning algorithms with a certain degree of intelligence. Metaheuristic algorithms are widely used in various optimization problems due to their algorithmic intelligence, and they have become the most effective algorithm to solve complex optimization problems in the field of mobile robot path planning. Based on a comprehensive analysis of existing path-planning algorithms, this paper proposes a new algorithm classification. Based on this classification, we focus on the firefly algorithm (FA) and the cuckoo search algorithm (CS), complemented by the dragonfly algorithm (DA), the whale optimization algorithm (WOA), and the sparrow search algorithm (SSA). During the analysis of the above algorithms, this paper summarizes the current research results of mobile robot path planning and proposes the future development trend of mobile robot path planning.

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Section: Cuckoo Search Algorithmmentioning

confidence: 99%

Research Progress of Nature-Inspired Metaheuristic Algorithms in Mobile Robot Path Planning

Xu,

Li,

et al. 2023

Electronics

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“…At the same time, the cuckoo algorithm and its improved algorithm also have a very broad application, such as PID controller design [ 27 ], grayscale image enhancement [ 28 ], cryptanalysis of Vigenere ciphers [ 29 ], data privacy protection [ 30 ], image segmentation [ 31 ], spam detection [ 32 ], the design of multidocument summarization extractor [ 33 ], and robot path planning [ 34 , 35 ].…”

Section: Related Workmentioning

confidence: 99%

Cuckoo Algorithm Based on Global Feedback

Liu

Lai

et al. 2023

Computational Intelligence and Neuroscience

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This article proposes a cuckoo algorithm (GFCS) based on the global feedback strategy and innovatively introduces a “re-fly” mechanism. In GFCS, the process of the algorithm is adjusted and controlled by a dynamic global variable, and the dynamic global parameter also serves as an indicator of whether the algorithm has fallen into a local optimum. According to the change of the global optimum value of the algorithm in each round, the dynamic global variable value is adjusted to optimize the algorithm. In addition, we set new formulas for the other main parameters, which are also adjusted by the dynamic global variable as the algorithm progresses. When the algorithm converges prematurely and falls into a local optimum, the current optimum is retained, and the algorithm is initialized and re-executed to find a better value. We define the previous process as “re-fly.” To verify the effectiveness of GFCS, we conducted extensive experiments on the CEC2013 test suite. The experimental results show that the GFCS algorithm has better performance compared to other algorithms when considering the quality of the obtained solution.

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“…Similar to the DE algorithm, the CS has also been widely used in robotics for such applications as trajectory tracking [43], path planning [44], and so forth. Sharm et al [45] used a tournament selection function, which considered both path time and length, to optimize the CS algorithm for robot path planning. Compared to the PSO and the traditional CS, the performance of Sharm's improved CS algorithm was better in terms of path length and time optimization.…”

Section: Advantagesmentioning

confidence: 99%

Inverse Kinematics of Robot Manipulator Based on BODE-CS Algorithm

Luo

Qiao

2023

Machines

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Differential evolution is a popular algorithm for solving global optimization problems. When tested, it has reportedly outperformed both robotic problems and benchmarks. However, it may have issues with local optima or premature convergence. In this paper, we present a novel BODE-CS (Bidirectional Opposite Differential Evolution–Cuckoo Search) algorithm to solve the inverse kinematics problem of a six-DOF EOD (Explosive Ordnance Disposal) robot manipulator. The hybrid algorithm was based on the differential evolution algorithm and Cuckoo Search algorithm. To avoid any local optimum and accelerate the convergence of the swarm, various strategies were introduced. Firstly, a forward-kinematics model was established, and the objective function was formulated according to the structural characteristics of the robot manipulator. Secondly, a Halton sequence and an opposite search strategy were used to initialize the individuals in the swarm. Thirdly, the optimization algorithms applied to the swarm were dynamically allocated to the Differential Evolution algorithm or the Cuckoo algorithm. Fourthly, a composite differential algorithm, which consisted of a dynamically opposite differential strategy, a bidirectional search strategy, and two other typically used differential strategies were introduced to maintain the diversity of the swarm. Finally, two adaptive parameters were introduced to optimize the amplification factor F and cross-over probability Cr. To verify the performance of the BODE-CS algorithm, two different tasks were tested. The experimental results of the simulation showed that the BODE-CS algorithm had high accuracy and a fast convergence rate, which met the requirements of an inverse solution for the manipulator.

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Optimized cuckoo search algorithm using tournament selection function for robot path planning

Cited by 13 publications

References 38 publications

Research Progress of Nature-Inspired Metaheuristic Algorithms in Mobile Robot Path Planning

Research Progress of Nature-Inspired Metaheuristic Algorithms in Mobile Robot Path Planning

Cuckoo Algorithm Based on Global Feedback

Inverse Kinematics of Robot Manipulator Based on BODE-CS Algorithm

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