A multi-verse Optimizer Approach Based on a Salp Swarm for Image Feature Selection to Observe Changes in Urban Lands

Authors

Nagham Tharwat Saeed College of Education for Pure Science, University of Mosul, Department of Computer Science, Mosul, Iraq

DOI:

https://doi.org/10.47577/technium.v25i.8929

Keywords:

Salp swarm algorithm, Image feature, Urban lands, Driving force model, Fitness function

Abstract

Many changes have occurred in most urban areas, and it is usually governed by many geographical and social factors, the result will be either the expansion or contraction of these cities. In this paper, an optimizer method called salp swarm optimization (SSO) is used to build a driving force model for urban lands according to the changes in the driving force mechanism. The principle of (SSO) is extracting the driving force classification rules corresponding to different types of land use change samples by imitating the behavior of salp. The classification rules are constructed in the form of "IF ... THEN", and three different fitness functions are selected for simulation verification. The data set used in this search represents images from Global Positioning System (GPS) satellites and remote sensing data. The experimental results show that the overall accuracy evaluation of the salp swarm optimization model is superior to other algorithms, indicating that the SSA is feasible for land use change modeling.

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Published

2024-11-10

How to Cite

Saeed, N. T. (2024). A multi-verse Optimizer Approach Based on a Salp Swarm for Image Feature Selection to Observe Changes in Urban Lands. Technium: Romanian Journal of Applied Sciences and Technology, 25, 44–52. https://doi.org/10.47577/technium.v25i.8929