Title: Adaptive constraint multi-objective evolutionary computation industrial economic optimisation in smart city

Authors: Yao Lv; Zimeng Guo

Addresses: Shenyang University, Shenyang, Liaoning, China ' Shenyang University, Shenyang, Liaoning, China

Abstract: This paper introduces an Adaptive Constraint Multi-objective Evolutionary Algorithm for Smart City Industrial Economics (ACMEA-SCIE). ACMEA-SCIE employs a dual reproduction strategy, evolving two complementary populations: a main population for exploring diverse industrial configurations and an archive population for preserving high-quality solutions. Additionally, a dynamic fitness allocation function adaptively balances objective optimisation and constraint handling, while an innovative archive update mechanism maintains solution diversity. The algorithm's performance was evaluated on three benchmark sets: smart city resource allocation, industrial ecosystem optimisation and dynamic urban industrial planning. Experimental results demonstrate ACMEA-SCIE's superior performance compared to state-of-the-art algorithms, achieving significant improvements in both inverted generational distance and hypervolume metrics. Additional analyses, including convergence performance and solution distribution, further validate ACMEA-SCIE's effectiveness. The proposed algorithm shows remarkable adaptability across various problem types, enhanced constraint handling and improved multi-objective balancing.

Keywords: small city; industrial economic; evolutionary computation; multi-objective optimisation.

DOI: 10.1504/IJCAT.2025.150324

International Journal of Computer Applications in Technology, 2025 Vol.77 No.3/4, pp.185 - 197

Received: 15 Oct 2024
Accepted: 24 May 2025
Published online: 09 Dec 2025 *

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