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

Yin, Shihong; Luo, Qifang; Zhou, Guo; Zhou, Yongquan; Zhu, Binwen

doi:10.1038/s41598-022-13516-3

Cited by 24 publications

(17 citation statements)

References 89 publications

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“…The Inverse Kinematics (IK) of complex manipulators are successfully solved using a hybrid equilibrium optimizer SMA (EOSMA) [ 100 ]. The EO's concentration update operator directs the SMA's anisotropic search, improving its efficacy.…”

Section: Methods Of Smamentioning

confidence: 99%

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Gharehchopogh

Uçan

İbrikçi

et al. 2023

Arch Computat Methods Eng

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Meta-heuristic algorithms have a high position among academic researchers in various fields, such as science and engineering, in solving optimization problems. These algorithms can provide the most optimal solutions for optimization problems. This paper investigates a new meta-heuristic algorithm called Slime Mould algorithm (SMA) from different optimization aspects. The SMA algorithm was invented due to the fluctuating behavior of slime mold in nature. It has several new features with a unique mathematical model that uses adaptive weights to simulate the biological wave. It provides an optimal pathway for connecting food with high exploration and exploitation ability. As of 2020, many types of research based on SMA have been published in various scientific databases, including IEEE, Elsevier, Springer, Wiley, Tandfonline, MDPI, etc. In this paper, based on SMA, four areas of hybridization, progress, changes, and optimization are covered. The rate of using SMA in the mentioned areas is 15, 36, 7, and 42%, respectively. According to the findings, it can be claimed that SMA has been repeatedly used in solving optimization problems. As a result, it is anticipated that this paper will be beneficial for engineers, professionals, and academic scientists.

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Section: Methods Of Smamentioning

confidence: 99%

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Gharehchopogh

Uçan

İbrikçi

et al. 2023

Arch Computat Methods Eng

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“…Additionally, the experimental result clearly shows that the proposed EOSMA outperforms SMA, EO, MPA, IGWO, AGPSO, and MTDE employed for various cutting-edge metaheuristics for engineering design issues. A hybridized EO called the Equilibrium Optimizer Slime Mould Algorithm (EOSMA) and a Multi-Objective EOSMA (MOEOSMA) were proposed by Yin et al in the year 2022 [227]. These algorithms use the concentration update operator of the EO to direct the SMA search mechanism, a greedy strategy to speed up convergence and escape from local optima, and a random difference mutation operator.…”

Section: Hybridization With Slime Mould Algorithm (Sma)mentioning

confidence: 99%

Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

Rai

Dhal

2023

Arch Computat Methods Eng

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There have been many algorithms created and introduced in the literature inspired by various events observable in nature, such as evolutionary phenomena, the actions of social creatures or agents, broad principles based on physical processes, the nature of chemical reactions, human behavior, superiority, and intelligence, intelligent behavior of plants, numerical techniques and mathematics programming procedure and its orientation. Nature-inspired metaheuristic algorithms have dominated the scientific literature and have become a widely used computing paradigm over the past two decades. Equilibrium Optimizer, popularly known as EO, is a population-based, nature-inspired meta-heuristics that belongs to the class of Physics based optimization algorithms, enthused by dynamic source and sink models with a physics foundation that are used to make educated guesses about equilibrium states. EO has achieved massive recognition, and there are quite a few changes made to existing EOs. This article gives a thorough review of EO and its variations. We started with 175 research articles published by several major publishers. Additionally, we discuss the strengths and weaknesses of the algorithms to help researchers find the variant that best suits their needs. The core optimization problems from numerous application areas using EO are also covered in the study, including image classification, scheduling problems, and many others. Lastly, this work recommends a few potential areas for EO research in the future.

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“…The main objective of this research is to solve the inverse solution problem of redundant robots. For this purpose, we design a fitness function that describes the Euclidean distance between the coordinates of a given desired position * , * , * and the coordinates of the described current position , , [13], formula (1).…”

Section: Solving the Objective Functionmentioning

confidence: 99%

Inverse kinematics of 7-Dof robot based on pelican optimization algorithm

Nie,

2023

Third International Conference on Control and Intelligent Robotics (ICCIR 2023)

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

Cited by 24 publications

References 89 publications

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

Inverse kinematics of 7-Dof robot based on pelican optimization algorithm

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