A comprehensive survey of Crow Search Algorithm and its applications DOI
Yassine Meraihi, Asma Benmessaoud Gabis, Amar Ramdane-Chérif

et al.

Artificial Intelligence Review, Journal Year: 2020, Volume and Issue: 54(4), P. 2669 - 2716

Published: Sept. 28, 2020

Language: Английский

An improved grey wolf optimizer for solving engineering problems DOI
Mohammad H. Nadimi-Shahraki, Shokooh Taghian, Seyedali Mirjalili

et al.

Expert Systems with Applications, Journal Year: 2020, Volume and Issue: 166, P. 113917 - 113917

Published: Sept. 16, 2020

Language: Английский

Citations

820
Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization DOI
Hoda Zamani, Mohammad H. Nadimi-Shahraki, Amir H. Gandomi

et al.

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2022, Volume and Issue: 392, P. 114616 - 114616

Published: Feb. 12, 2022

Language: Английский

Citations

229
Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study DOI
Mohammad H. Nadimi-Shahraki, Hoda Zamani, Seyedali Mirjalili

et al.

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 148, P. 105858 - 105858

Published: July 16, 2022

Language: Английский

Citations

210
MTDE: An effective multi-trial vector-based differential evolution algorithm and its applications for engineering design problems DOI
Mohammad H. Nadimi-Shahraki, Shokooh Taghian, Seyedali Mirjalili

et al.

Applied Soft Computing, Journal Year: 2020, Volume and Issue: 97, P. 106761 - 106761

Published: Sept. 28, 2020

Language: Английский

Citations

203
QANA: Quantum-based avian navigation optimizer algorithm DOI
Hoda Zamani, Mohammad H. Nadimi-Shahraki, Amir H. Gandomi

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2021, Volume and Issue: 104, P. 104314 - 104314

Published: June 21, 2021

Language: Английский

Citations

187
Crow Search Algorithm: Theory, Recent Advances, and Applications DOI Creative Commons
Abdelazim G. Hussien, Mohammed A. Amin, Mingjing Wang

et al.

IEEE Access, Journal Year: 2020, Volume and Issue: 8, P. 173548 - 173565

Published: Jan. 1, 2020

In this article, a comprehensive overview of the Crow Search Algorithm (CSA) is introduced with detailed discussions, which intended to keep researchers interested in swarm intelligence algorithms and optimization problems. CSA new algorithm recently developed, simulates crow behavior storing excess food retrieving it when needed. theory, searcher, surrounding environment search space, randomly location feasible solution. Among all locations, where most stored considered be global optimal solution, objective function amount food. By simulating intelligent crows, tries find solutions various It has gained considerable interest worldwide since its advantages like simple implementation, few numbers parameters, flexibility, etc. This survey introduces variant CSA, including hybrid, modified, multi-objective versions. Furthermore, based on analyzed papers published literature by some publishers such as IEEE, Elsevier, Springer, application scenarios power, computer science, machine learning, civil engineering have also been reviewed. Finally, disadvantages discussed conducting comparative experiments other similar peers.

Language: Английский

Citations

182
Social Network Search for Solving Engineering Optimization Problems DOI Creative Commons
Hadi Bayzidi, Siamak Talatahari,

Meysam Saraee

et al.

Computational Intelligence and Neuroscience, Journal Year: 2021, Volume and Issue: 2021(1)

Published: Jan. 1, 2021

In this paper, a new metaheuristic optimization algorithm, called social network search (SNS), is employed for solving mixed continuous/discrete engineering problems. The SNS algorithm mimics the user’s efforts to gain more popularity by modeling decision moods in expressing their opinions. Four moods, including imitation, conversation, disputation, and innovation, are real‐world behaviors of users networks. These used as operators that model how affected motivated share views. was verified with 14 benchmark problems one real application field remote sensing. performance proposed method compared various algorithms show its effectiveness over other well‐known optimizers terms computational cost accuracy. most cases, optimal solutions achieved better than best solution obtained existing methods.

Language: Английский

Citations

172
Quantum-inspired metaheuristic algorithms: comprehensive survey and classification DOI
Farhad Soleimanian Gharehchopogh

Artificial Intelligence Review, Journal Year: 2022, Volume and Issue: 56(6), P. 5479 - 5543

Published: Nov. 2, 2022

Language: Английский

Citations

131
Newton-Raphson-based optimizer: A new population-based metaheuristic algorithm for continuous optimization problems DOI

R. Sowmya,

M. Premkumar, Pradeep Jangir

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 128, P. 107532 - 107532

Published: Dec. 12, 2023

Language: Английский

Citations

124
A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations DOI Open Access
Mohammad H. Nadimi-Shahraki, Hoda Zamani, Zahra Asghari Varzaneh

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(7), P. 4113 - 4159

Published: May 27, 2023

Language: Английский

Citations

115