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Article · Engineered Science · January 1, 2026

Hybrid Method for Optimizing Artificial Intelligence-Driven Group-Buying Strategies and Marketing in the Agro-Industrial Complex using the Non-Dominated Sorting Genetic Algorithm-II and Machine Learning

Zhansaya Abildaeva, Zhainagan Jalbirova, Gulzhanat Beketova, Sabit Ibadulla, Nurbek Konyrbaev, Bakhytbek Nassyradyn

Gulzhanat Beketova
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Abstract

The article considers the problem of multi-criteria optimization of marketing strategies in the agro-industrial complex (AIC) of Kazakhstan using the Non-dominated Sorting Genetic Algorithm (NSGA-II) and machine learning (ML) methods. A combined approach was used, combining optimization on a set of Pareto-optimal solutions, and K-means clustering was used to analyze the results, which made it possible to identify dominant budget allocation strategies between digital, television, radio, print channels and marketing events, such as exhibitions and product presentations. The method was validated both on synthetic data and on real data from Kazakhstan's agro-industrial complex enterprises with an assessment of the quality of solutions through the Hypervolume Indicator, as well as visualization of the 3D Pareto front. The results obtained during the study demonstrate nonlinear relationships between the criteria and identify optimal configurations for various market segments. The practical significance of the study is confirmed by the adaptation of solutions for Kazakhstan's agro-industrial complex enterprises, taking into account the adopted regional restrictions, in particular on the budget for digital marketing and advertising.

Advanced Multi-Objective Optimization AlgorithmsOptimization and Mathematical ProgrammingMulti-Criteria Decision Makingmarketing strategy optimizationagro-industrial complexNSGA-II

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