TY  - JOUR
N2  - 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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
SN  - 19961073
Y1  - 2021///
IS  - 4
KW  - Learning algorithms
KW  -  Benchmark functions; Convergence rates; Gravitational search algorithm (GSA); Multimodal optimization problems; Niching techniques; Search Algorithms; Test functions; Well placement optimization
KW  -  Particle swarm optimization (PSO)
TI  - A modified niching crow search approach to well placement optimization
A1  - Islam, J.
A1  - Rahaman, M.S.A.
A1  - Vasant, P.M.
A1  - Negash, B.M.
A1  - Hoqe, A.
A1  - Alhitmi, H.K.
A1  - Watada, J.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106398980&doi=10.3390%2fen14040857&partnerID=40&md5=fdf288e490d86ff03b4695c1174870e6
N1  - cited By 5
ID  - scholars15197
PB  - MDPI AG
JF  - Energies
AV  - none
VL  - 14
ER  -