DTOSFS–CatBoost: A Hybrid Metaheuristic Framework for

Accurate and Interpretable Unemployment Forecasting

Ghassan AL-Thabhawee1 Hussein Alkattan2

1 Sciences of Mathematics, Computer Sciences, College of Health and Medical Techniques-Kufa, Al-Furat Al-Awsat Technical

University, Kufa, Iraq

2 Department of System Programming, South Ural State University, Chelyabinsk, Russia; Directorate of Environment in Najaf,

Ministry of Environment, Najaf, Iraq

Received: January 03, 2026 Revised: March 01, 2026 Accepted: May 02, 2026 ⋆ Corresponding author

ABSTRACT

The fact that educational, demographic, and macroeconomic variables interact nonlinearly has remained a thorn

in the flesh of socio-economic analytics to date, making it challenging to forecast unemployment with sufficient

precision. To address this, the current study presents a hybrid metaheuristic, Dipper Throated Optimization with

Stochastic Fractal Search (DTOSFS), coupled with the Category Boosting (CatBoost) algorithm to improve predictive

modelling. The suggested DTOSFS-CatBoost system combines the general exploratory search of DTO with SFS

refinement to stochastic local optimization of hyperparameters, and alleviates overfitting. Empirical experiments

have shown that whereas the original CatBoost gave results with a Mean Squared Error (MSE) of 0.0256 and Root

Mean Squared Error (RMSE) of 0.1601 with a correlation coefficient of 0.873, the CatBoost optimized by DTOSFS

had drastically better results with an MSE of 0.00033, RMSE of 0.00207, and a correlation coefficient of 0.930.

These results confirm an increased exploration-to-exploitation ratio in DTOSFS and yield small, powerful designs

that substantially enhance model stability, precision, and convergence speed. These results show that educational

attainment (at least tertiary and primary enrollment) and demographics (at least the birth rate) are influential factors

in unemployment variation. This addition to predictive performance is not the only one, and it provides a predictive

data-driven labor-market optimization paradigm that can be replicated and interpreted. The research observes that

hybrid metaheuristics and gradient boosting can be used to drive next-generation economic intelligence systems for

adaptive policy formulation and to enhance online, privacy-conscious, and cross-domain unemployment prediction.

Keywords: DTOSFS CatBoost Unemployment Forecasting Hybrid Metaheuristic Optimization Socio-Economic

Prediction

1. INTRODUCTION

Employment is one of the most acute problems that negatively

impacts the socioeconomic development of countries,

societies, and people in general [1]. The comparatively high

unemployment rates slow progress and development, aggravate

poverty, and reduce the quality of life of all people .

Moreover, unemployment negatively affects society through

its effects on consumer spending, the growth of dependence

on social programs, and psychological effects on both communities

and individuals. To that end, accurately predicting

unemployment rates could help policymakers and businesses

address these challenges [2]. The study of unemployment patterns,

therefore, helps governments design better approaches,