A Landscape-Driven Particle Swarm Optimization: A Preliminary Study on Feature Selection

Mohammed El Amrani, Malek Sarhani (), Abtin Nourmohammadzadeh () and Jawad Abrache ()
Additional contact information
Mohammed El Amrani: Mohammed V University in Rabat
Malek Sarhani: Al Akhawayn University
Abtin Nourmohammadzadeh: University of Hamburg
Jawad Abrache: Al Akhawayn University

Chapter Chapter 70 in Operations Research Proceedings 2023, 2025, pp 551-557 from Springer

Abstract: Abstract Particle Swarm Optimization (PSO) is widely acknowledged as one of the most effective swarm intelligence approaches in the field of metaheuristics. This paper introduces an adaptive variant of PSO that leverages fitness landscape information, specifically computing the ruggedness factor. The proposed method aims to identify the optimal PSO strategy by adopting an adaptive rule to update PSO parameters based on the ruggedness factor. The effectiveness of this approach is demonstrated through its evaluation on the feature selection problem, showcasing promising outcomes.

Keywords: Particle swarm optimization; Fitness landscape analysis; Adaptive metaheuristics; Feature selection (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-031-58405-3_70

Ordering information: This item can be ordered from
http://www.springer.com/9783031584053

DOI: 10.1007/978-3-031-58405-3_70

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().