Variable Landscape Search: A Novel Metaheuristic Paradigm for Unlocking Hidden Dimensions in Global Optimization

Rustam Mussabayev () and Ravil Mussabayev
Additional contact information
Rustam Mussabayev: Laboratory for Analysis and Modeling of Information Processes, Institute of Information and Computational Technologies, Almaty 050010, Kazakhstan
Ravil Mussabayev: AI Research Lab, Satbayev University, Almaty 050000, Kazakhstan

Mathematics, 2025, vol. 13, issue 22, 1-26

Abstract: This paper presents the Variable Landscape Search (VLS), a novel metaheuristic designed for global optimization of complex problems by dynamically altering the objective function landscape. Unlike traditional methods that operate within a static search space, VLS introduces an additional level of flexibility and diversity to the global optimization process. It does this by continuously and iteratively varying the objective function landscape through slight modifications to the problem formulation, the input data, or both. The innovation of the VLS metaheuristic stems from its unique capability to seamlessly fuse dynamic adaptations in problem formulation with modifications in input data. This dual-modality approach enables continuous exploration of interconnected and evolving search spaces, significantly enhancing the potential for discovering optimal solutions in complex, multi-faceted optimization scenarios, thereby making it adaptable across various domains. In this paper, one of the theoretical results is obtained as a generalization of three alternative metaheuristics, which have been reduced to special cases of VLS: Variable Formulation Search (VFS), Formulation Space Search (FSS), and Variable Search Space (VSS). As a practical application, the paper demonstrates the superior efficiency of a recent big data clustering algorithm through its conceptualization using the VLS paradigm.

Keywords: variable landscape; variable landscape search (VLS); variable formulation search (VFS); formulation space search (FSS); variable search space (VSS); landscape meta-space; metaheuristic; global optimization; local search; search space variation; dynamic search spaces; big data clustering (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/22/3667/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/22/3667/ (text/html)

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:gam:jmathe:v:13:y:2025:i:22:p:3667-:d:1795588

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().