## 4 Proposed Solution
### Main Characteristics of the Multi-Agent Gradient Descent Algorithm (MAGD)
[CrossRef]
* [PERSON] and [PERSON] (2017) [PERSON]; [PERSON] SGDR: Stochastic Gradient Descent with Warm Restarts. _arXiv_**2017**, arXiv:1608.03983. [CrossRef]
* [PERSON] and [PERSON] (2019) [PERSON]; [PERSON] Adaptive restart of accelerated gradient methods under local quadratic growth condition. _IMA J. Numer. Anal._**2019**, _39_, 2069-2095. [CrossRef]
* [PERSON] and [PERSON] (2012) [PERSON]; [PERSON] A multistart gradient-based algorithm with surrogate model for global optimization. _Commun. Appl. Ind. Math._**2012**, \(3\), 863. [CrossRef]
* [PERSON] (2017) [PERSON]; [PERSON] Swarm Intelligence: A Review of Algorithms. In _Nature-Inspired Computing and Optimization_; Springer: Berlin/Heidelberg, Germany, 2017; pp. 475-494. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON] A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends. _IEEE/CAA J. Auton. Sin._**2021**, \(8\), 1627-1643. [CrossRef]
* [PERSON] and [PERSON] (1996) [PERSON]; [PERSON]; [PERSON] Ant system: Optimization by a colony of cooperating agents. _IEEE Trans. Syst. Man Cybern. Part B Cybern._**1996**, _26_, 29-41. [CrossRef] [PubMed]
* [PERSON] (2005) [PERSON] _An Idea Based on Honey Be Swarm for Numerical Optimization;_ Technical Report-tr06; Computer Engineering Department, Engineering Faculty, Ecriyes University: Kayseri, Turkey, 2005. * [PERSON] and [PERSON] (2009) [PERSON]; [PERSON] Cuckoo search via Levy flights. In Proceedings of the 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), Coimbatore, India, 9-11 December 2009; IEEE: New York, NY, USA, 2009; pp. 210-214. * [PERSON] et al. (2014) [PERSON]; [PERSON]; [PERSON] Grey Wolf Optimizer. _Adv. Eng. Softw._**2014**, _69_, 46-61. [CrossRef]
* [PERSON] (2010) [PERSON] A New Metaheuristic Bat-Inspired Algorithm. In _Nature Inspired Cooperative Strategies for Optimization (NICSO 2010)_; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]; Springer: Berlin/Heidelberg, Germany, 2010; pp. 65-74. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] An alternative approach to neural network training based on hybrid bio meta-heuristic algorithm. _J. Ambient Intell. Humaniz. Comput._**2019**, _10_, 3821-3830. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] Training multi-layer perceptron with artificial algae algorithm. _Eng. Sci. Technol. Int. J._**2020**, _23_, 1342-1350. [CrossRef]
* [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON] Artificial algae algorithm (AAA) for nonlinear global optimization. _Appl. Soft Comput._**2015**, _31_, 153-171. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON] Comparison of Swarm-based Metaheuristic and Gradient Descent-based Algorithms in Artificial Neural Network Training. _ADCAIJ_**2023**, _12_, e29969. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON] A Compact Cat Swarm Optimization Algorithm Based on Small Sample Probability Model. _Appl. Sci._**2022**, _12_, 8209. [CrossRef]
* Walsh and Occam's Razor (1979) [PERSON] Occam's Razor: A Principle of Intellectual Elegance. _Am. Philos. Q._**1979**, _16_, 241-244. * [PERSON] (2020) [PERSON] Become Competent within One Day in Generating Boxplots and Violin Plots for a Novice without Prior R Experience. _Methods Protoc._**2020**, \(3\), 64. [CrossRef] [PubMed]* [PERSON] (2004) [PERSON] Who was student and why do we care so much about his \(t\)-test?1. _J. Surg. Res._**2004**, _118_, 58-65. [CrossRef] [PubMed]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Survey of Uncertainty Quantification in Machine Learning for Space Weather Prediction. _Geosciences_**2022**, _12_, 27. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] Bad Global Minima Exist and SGD Can Reach Them. _Mach. Learn._**2020**, _33_, 8543-8552. * [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON] Target Localization and Grasping of Parallel Robots with Multi-Vision Based on Improved RANSAC Algorithm. _Appl.