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A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer

Metaheuristic algorithms have received much attention recently for solving different optimization and engineering problems. Most of these methods were inspired by nature or the behavior of certain swarms, such as birds, ants, bees, or even bats, while others were inspired by a specific social behavi...

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Main Authors:	Alsewari, Abdul Rahman Ahmed, Sinan, Q. Salih
Format:	Article
Language:	English
Published:	Springer 2019
Subjects:	Not Available
Online Access:	http://umpir.ump.edu.my/id/eprint/29726/1/Salih-Alsewari2020_Article_ANewAlgorithmForNormalAndLarge.pdf http://umpir.ump.edu.my/id/eprint/29726/ https://link.springer.com/article/10.1007/s00521-019-04575-1 https://doi.org/10.1007/s00521-019-04575-1(0123456789().,-volV)(0123456789(). ,- volV)
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id	my.ump.umpir.29726
record_format	eprints
spelling	my.ump.umpir.297262020-10-26T09:02:51Z http://umpir.ump.edu.my/id/eprint/29726/ A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer Alsewari, Abdul Rahman Ahmed Sinan, Q. Salih Not Available Metaheuristic algorithms have received much attention recently for solving different optimization and engineering problems. Most of these methods were inspired by nature or the behavior of certain swarms, such as birds, ants, bees, or even bats, while others were inspired by a specific social behavior such as colonies, or political ideologies. These algorithms faced an important issue, which is the balancing between the global search (exploration) and local search (exploitation) capabilities. In this research, a novel swarm-based metaheuristic algorithm which depends on the behavior of nomadic people was developed, it is called ‘‘Nomadic People Optimizer (NPO)’’. The proposed algorithm simulates the nature of these people in their movement and searches for sources of life (such as water or grass for grazing), and how they have lived hundreds of years, continuously migrating to the most comfortable and suitable places to live. The algorithm was primarily designed based on the multi-swarm approach, consisting of several clans and each clan looking for the best place, in other words, for the best solution depending on the position of their leader. The algorithm is validated based on 36 unconstrained benchmark functions. For the comparison purpose, six well-established nature-inspired algorithms are performed for evaluating the robustness of NPO algorithm. The proposed and the benchmark algorithms are tested for large-scale optimization problems which are associated with high-dimensional variability. The attained results demonstrated a remarkable solution for the NPO algorithm. In addition, the achieved results evidenced the potential high convergence, lower iterations, and less time-consuming required for finding the current best solution. Springer 2019-10-28 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/29726/1/Salih-Alsewari2020_Article_ANewAlgorithmForNormalAndLarge.pdf Alsewari, Abdul Rahman Ahmed and Sinan, Q. Salih (2019) A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer. Neural Computing and Applications, 32. pp. 10359-10386. ISSN 0941-0643 https://link.springer.com/article/10.1007/s00521-019-04575-1 https://doi.org/10.1007/s00521-019-04575-1(0123456789().,-volV)(0123456789(). ,- volV)
institution	Universiti Malaysia Pahang
building	UMP Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang
content_source	UMP Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English
topic	Not Available
spellingShingle	Not Available Alsewari, Abdul Rahman Ahmed Sinan, Q. Salih A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer
description	Metaheuristic algorithms have received much attention recently for solving different optimization and engineering problems. Most of these methods were inspired by nature or the behavior of certain swarms, such as birds, ants, bees, or even bats, while others were inspired by a specific social behavior such as colonies, or political ideologies. These algorithms faced an important issue, which is the balancing between the global search (exploration) and local search (exploitation) capabilities. In this research, a novel swarm-based metaheuristic algorithm which depends on the behavior of nomadic people was developed, it is called ‘‘Nomadic People Optimizer (NPO)’’. The proposed algorithm simulates the nature of these people in their movement and searches for sources of life (such as water or grass for grazing), and how they have lived hundreds of years, continuously migrating to the most comfortable and suitable places to live. The algorithm was primarily designed based on the multi-swarm approach, consisting of several clans and each clan looking for the best place, in other words, for the best solution depending on the position of their leader. The algorithm is validated based on 36 unconstrained benchmark functions. For the comparison purpose, six well-established nature-inspired algorithms are performed for evaluating the robustness of NPO algorithm. The proposed and the benchmark algorithms are tested for large-scale optimization problems which are associated with high-dimensional variability. The attained results demonstrated a remarkable solution for the NPO algorithm. In addition, the achieved results evidenced the potential high convergence, lower iterations, and less time-consuming required for finding the current best solution.
format	Article
author	Alsewari, Abdul Rahman Ahmed Sinan, Q. Salih
author_facet	Alsewari, Abdul Rahman Ahmed Sinan, Q. Salih
author_sort	Alsewari, Abdul Rahman Ahmed
title	A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer
title_short	A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer
title_full	A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer
title_fullStr	A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer
title_full_unstemmed	A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer
title_sort	new algorithm for normal and large-scale optimization problems: nomadic people optimizer
publisher	Springer
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/29726/1/Salih-Alsewari2020_Article_ANewAlgorithmForNormalAndLarge.pdf http://umpir.ump.edu.my/id/eprint/29726/ https://link.springer.com/article/10.1007/s00521-019-04575-1 https://doi.org/10.1007/s00521-019-04575-1(0123456789().,-volV)(0123456789(). ,- volV)
_version_	1683230923275894784
score	13.211869

A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer

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