A Hybrid Multi-Swarm Particle Swarm Optimization Algorithm for Solving Agent-Based Epidemiological Model

Akopov, Andranik S.

doi:10.2478/cait-2025-0033

Abstract

This paper presents a new agent-based epidemiological model, which is solved using the proposed Hybrid Multi-Swarm Particle Swarm Optimization Algorithm (HMSPSO Algorithm). The HMSPSO is based on a combination of a parallel multi-swarm particle swarm optimization algorithm and real-coded genetic operators, including crossover and mutation. Unlike other well-known particle swarm optimization algorithms, this method uses alternating real-coded heuristic operators applied to parent solutions selected from sub-swarms obtained through agglomerative clustering. The performance of the HMSPSO Algorithm was compared to that of other established single-objective evolutionary algorithms, and the results show that the HMSPSO achieves the best performance in terms of both time efficiency and accuracy. HMSPSO was combined with the developed agent-based epidemiological model. As a result, optimal strategies for anti-epidemic measures such as vaccination intensity, self-quarantine intensity, and other parameters were calculated to maximize the share of surviving individuals.

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A Hybrid Multi-Swarm Particle Swarm Optimization Algorithm for Solving Agent-Based Epidemiological Model

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