A Modified Niching Crow Search Approach to Well Placement Optimization

Jahedul Islam,Md Shokor A Rahaman,Hitmi Khalifa Alhitmi,Berihun Mamo Negash,Ahshanul Hoqe,Junzo Watada,Pandian M Vasant

doi:10.3390/en14040857

Abstract

Well placement optimization is considered a non-convex and highly multimodal optimization problem. In this article, a modified crow search algorithm is proposed to tackle the well placement optimization problem. This article proposes modifications based on local search and niching techniques in the crow search algorithm (CSA). At first, the suggested approach is verified by experimenting with the benchmark functions. For test functions, the results of the proposed approach demonstrated a higher convergence rate and a better solution. Again, the performance of the proposed technique is evaluated with well placement optimization problem and compared with particle swarm optimization (PSO), the Gravitational Search Algorithm (GSA), and the Crow search algorithm (CSA). The outcomes of the study revealed that the niching crow search algorithm is the most efficient and effective compared to the other techniques.

Highlights

Optimization performs a vital function in scientific, manufacturing, and environmental processes in the modern world [1]
Efficiency indicates the pace with which the algorithm achieves at least 98 percent of the best solution found using a specific number of evaluations, on average between tests, or where L98 i is the number of unique function evaluations required to find solution q such that f (q) equals 98 percent of the best solution found for trial i and M is the total number of function evaluations per trial
Totimodal avoid premature convergence, such as those in particle swarm optimization (PSO) and gravitational search algorithm (GSA), theitsawareness optimization problems as niching crow search algorithm (NCSA) can automatically subdivide population probability parameter keeps NCSA switching between the equations based on the into subgroups since the niching technique is implemented

Summary

Introduction

Optimization performs a vital function in scientific, manufacturing, and environmental processes in the modern world [1]. To solve problems of this kind, researchers use several different methods to determine the right approach for a specific problem [2,3]. The conventional exact approach should be used for smaller problems where the problems are constant and distinct [4,5]. The conventional exact solution approach cannot escape local optimum in real-world problems, as real-world problems are not always differentiable [6]. The metaheuristic algorithm’s performance is problem specific [7]. Metaheuristic techniques are used in a vast range of studies [8,9]

Objectives

Methods

Findings

Conclusion