Work

AI-driven multi-objective optimization of municipal production systems using genetic algorithms

Year: 2026

Type: conference-paper

Abstract: The present study proposes an evolutionary model based on multi-objective optimization of the municipal production systems with genetic algorithms; the Non-dominated Sorted Genetic Algorithm II (NSGA II) generates a set of Pareto-optimal strategies for investment, which maximize outputs, minimize fiscal risks, and create structural balance. Results from simulations of this process indicate a 17% increase in the efficiency of producing goods, thus demonstrating that this methodology provides an effective decision support system for municipalities.

Source: International Conference on Cybersecurity, Digital Forensics, and AI Applications (ICCSDFAI)

DOI: 10.1109/iccsdfai70505.2026.11647916

Authors: Vagif Gasimov, Nizami Jafarov, Jabir Mammadov, Sevinj Aliyeva, Ilhami Suleymanov

Institution: Baku Engineering University

Language: English

Topic: Advanced Multi-Objective Optimization Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: hybrid

OpenAlex ID: W7203603359

FWCI:

Cites: 17

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Related to: 10