A New Design of Metaheuristic Search Called Improved Monkey Algorithm Based on Random Perturbation for Optimization Problems

Abstract

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.

Improved Hypercube Optimisation Search Algorithm for Optimisation of High Dimensional Functions

“…ese algorithms are: bat algorithm (BAT) [3], cuckoo search (CS) [4], ant lion optimizer (ALO) [5,6], elephant herding optimisation (EHO) [7,8], mothame optimisation (MFO) [9], krill herd (KH) [10], moth search algorithm (MSA) [11], monarch butter y optimisation (MBO) [12,13], mussels wandering optimisation (MWO) [14], and whale optimisation algorithm (WOA) [15]. Other meta-heuristic optimisation algorithms such as di erential evolution (DE) [16], biogeography-based optimisation (BBO) [17,18], harmony search (HS) [19], evolution strategies (ES) [20], sine cosine algorithm (SCA) [21], gravitational search algorithm (GSA) [22], monkey algorithm [23,24], dragon y algorithm (DA), and hybrid ABC/ DA (HAD) [25,26] are also e ciently used for solving many optimisation problems.…”