The aim of this paper is to present a design of a metaheuristic search called improved monkey algorithm (MA+) that provides a suitable solution for the optimization problems. The proposed algorithm has been renewed by a new method using random perturbation (RP) into two control parameters (p1 and p2) to solve a wide variety of optimization problems. A novel RP is defined to improve the control parameters and is constructed off the proposed algorithm. The main advantage of the control parameters is that they more generally prevented the proposed algorithm from getting stuck in optimal solutions. Many optimization problems at the maximum allowable number of iterations can sometimes lead to an inferior local optimum. However, the search strategy in the proposed algorithm has proven to have a successful global optimal solution, convergence optimal solution, and much better performance on many optimization problems for the lowest number of iterations against the original monkey algorithm. All details in the improved monkey algorithm have been represented in this study. The performance of the proposed algorithm was first evaluated using 12 benchmark functions on different dimensions. These different dimensions can be classified into three different types: low-dimensional (30), medium-dimensional (60), and high-dimensional (90). In addition, the performance of the proposed algorithm was compared with the performance of several metaheuristic algorithms using these benchmark functions on many different types of dimensions. Experimental results show that the improved monkey algorithm is clearly superior to the original monkey algorithm, as well as to other well-known metaheuristic algorithms, in terms of obtaining the best optimal value and accelerating convergence solution.

中文翻译：

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一种基于随机扰动的元启发式搜索改进猴子算法的新设计

本文的目的是提出一种称为改进的猴子算法（MA +）的元启发式搜索设计，该设计可为优化问题提供合适的解决方案。通过使用随机扰动（RP）到两个控制参数（p 1和p2）解决各种各样的优化问题。定义了一种新颖的RP来改善控制参数，并从所提出的算法中构造了该RP。控制参数的主要优点是，它们更普遍地防止了所提出的算法陷入最佳解决方案中。在最大允许迭代次数下的许多优化问题有时会导致次优的局部最优。然而，已证明该算法中的搜索策略具有成功的全局最优解，收敛最优解，并且在针对原始猴子算法的最小迭代次数的许多优化问题上具有更好的性能。改进的猴子算法中的所有细节都在这项研究中得到了体现。首先使用12个基准函数在不同维度上评估了所提出算法的性能。这些不同的维度可以分为三种不同的类型：低维度（30），中维度（60）和高维度（90）。此外，在许多不同类型的维度上，使用这些基准函数，将所提出算法的性能与几种元启发式算法的性能进行了比较。实验结果表明，改进后的猴子算法在获得最佳最优值和加速收敛解方面，明显优于原始猴子算法以及其他著名的元启发式算法。中维度（60）和高维度（90）。此外，在许多不同类型的维度上，使用这些基准函数，将所提出算法的性能与几种元启发式算法的性能进行了比较。实验结果表明，改进后的猴子算法在获得最佳最优值和加速收敛解方面，明显优于原始猴子算法以及其他著名的元启发式算法。中维度（60）和高维度（90）。此外，在许多不同类型的维度上，使用这些基准函数，将所提出算法的性能与几种元启发式算法的性能进行了比较。实验结果表明，改进后的猴子算法在获得最佳最优值和加速收敛解方面，明显优于原始猴子算法以及其他著名的元启发式算法。