A hybrid Harris hawks-moth-flame optimization algorithm including fractional-order chaos maps and evolutionary population dynamics DOI
Mohamed Abd Elaziz, Dalia Yousri, Seyedali Mirjalili

et al.

Advances in Engineering Software, Journal Year: 2021, Volume and Issue: 154, P. 102973 - 102973

Published: Feb. 23, 2021

Language: Английский

Sizing optimization and design of an autonomous AC microgrid for commercial loads using Harris Hawks Optimization algorithm DOI
İpek Çetinbaş, Bünyamin Tamyürek, Mehmet Demirtaş

et al.

Energy Conversion and Management, Journal Year: 2021, Volume and Issue: 245, P. 114562 - 114562

Published: Aug. 5, 2021

Language: Английский

Citations

67
Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications DOI Open Access
Abdelazim G. Hussien, Laith Abualigah,

Raed Abu Zitar

et al.

Electronics, Journal Year: 2022, Volume and Issue: 11(12), P. 1919 - 1919

Published: June 20, 2022

The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based performs optimization procedure using novel way exploration and exploitation multiphases search. In this review research, we focused on applications developments well-established robust (HHO) as one most popular techniques 2020. Moreover, several experiments were carried out to prove powerfulness effectivness HHO compared with nine other state-of-art algorithms Congress Evolutionary Computation (CEC2005) CEC2017. literature paper includes deep insight about possible future directions ideas worth investigations regarding new variants its widespread applications.

Language: Английский

Citations

65
Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation DOI Creative Commons
Qingxin Liu, Ni Li, Heming Jia

et al.

Mathematics, Journal Year: 2022, Volume and Issue: 10(7), P. 1014 - 1014

Published: March 22, 2022

Image segmentation is a key stage in image processing because it simplifies the representation of and facilitates subsequent analysis. The multi-level thresholding technique considered one most popular methods efficient straightforward. Many relative works use meta-heuristic algorithms (MAs) to determine threshold values, but they have issues such as poor convergence accuracy stagnation into local optimal solutions. Therefore, alleviate these shortcomings, this paper, we present modified remora optimization algorithm (MROA) for global tasks. We used Brownian motion promote exploration ability ROA provide greater opportunity find solution. Second, lens opposition-based learning introduced enhance search agents jump out To substantiate performance MROA, first 23 benchmark functions evaluate performance. compared with seven well-known regarding accuracy, speed, significant difference. Subsequently, tested quality MORA on eight grayscale images cross-entropy objective function. experimental metrics include peak signal-to-noise ratio (PSNR), structure similarity (SSIM), feature (FSIM). A series results proved that MROA has advantages among algorithms. Consequently, proposed promising method problems segmentation.

Language: Английский

Citations

65
Improved Salp Swarm Algorithm with mutation schemes for solving global optimization and engineering problems DOI

Bhaskar Nautiyal,

Rıshı Prakash, Vrince Vimal

et al.

Engineering With Computers, Journal Year: 2021, Volume and Issue: 38(S5), P. 3927 - 3949

Published: Feb. 7, 2021

Language: Английский

Citations

59
A hybrid Harris hawks-moth-flame optimization algorithm including fractional-order chaos maps and evolutionary population dynamics DOI
Mohamed Abd Elaziz, Dalia Yousri, Seyedali Mirjalili

et al.

Advances in Engineering Software, Journal Year: 2021, Volume and Issue: 154, P. 102973 - 102973

Published: Feb. 23, 2021

Language: Английский

Citations

57