Multi-objective optimization for smart cities: a systematic review of algorithms, challenges, and future directions
With the growing complexity and interdependence of urban systems, multi-objective optimization (MOO) has become a critical tool for smart-city planning, sustainability, and real-time decision-making. This article presents a systematic literature review (SLR) of 117 peer-reviewed studies published be...
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| Format: | Article |
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PeerJ Inc.
2025-07-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-3042.pdf |
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| author | YiFan Chen Weng Howe Chan Eileen Lee Ming Su Qi Diao |
| author_facet | YiFan Chen Weng Howe Chan Eileen Lee Ming Su Qi Diao |
| author_sort | YiFan Chen |
| collection | DOAJ |
| description | With the growing complexity and interdependence of urban systems, multi-objective optimization (MOO) has become a critical tool for smart-city planning, sustainability, and real-time decision-making. This article presents a systematic literature review (SLR) of 117 peer-reviewed studies published between 2015 and 2025, assessing the evolution, classification, and performance of MOO techniques in smart-city contexts. Existing algorithms are organised into four families—bio-inspired, mathematical theory-driven, physics-inspired, and machine-learning-enhanced—and benchmarked for computational efficiency, scalability, and scenario suitability across six urban domains: infrastructure, energy, transportation, Internet of Things (IoT)/cloud systems, agriculture, and water management. While established methods such as Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multiobjective Evolutionary Algorithm based on Decomposition (MOED/D) remain prevalent, hybrid frameworks that couple deep learning with evolutionary search display superior adaptability in high-dimensional, dynamic environments. Persistent challenges include limited cross-domain generalisability, inadequate uncertainty handling, and low interpretability of artificial intelligence (AI)-assisted models. Twelve research gaps are synthesised—from privacy-preserving optimisation and sustainable trade-off resolution to integration with digital twins, large language models, and neuromorphic computing—and a roadmap towards scalable, interpretable, and resilient optimisation frameworks is outlined. Finally, a ready-to-use benchmarking toolkit and a deployment-oriented algorithm-selection matrix are provided to guide researchers, engineers, and policy-makers in real-world smart-city applications. This review targets interdisciplinary researchers, optimisation developers, and smart-city practitioners seeking to apply or advance MOO techniques in complex urban systems. |
| format | Article |
| id | doaj-art-3c0daf25deb34bb9a88964a2cf70c849 |
| institution | DOAJ |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | PeerJ Inc. |
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| series | PeerJ Computer Science |
| spelling | doaj-art-3c0daf25deb34bb9a88964a2cf70c8492025-08-20T02:52:11ZengPeerJ Inc.PeerJ Computer Science2376-59922025-07-0111e304210.7717/peerj-cs.3042Multi-objective optimization for smart cities: a systematic review of algorithms, challenges, and future directionsYiFan Chen0Weng Howe Chan1Eileen Lee Ming Su2Qi Diao3Jiaxing Key Laboratory of Industrial Intelligence and Digital Twin, Jiaxing Vocational and Technical College, Jiaxing, Zhejiang, ChinaFaculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Johor, MalaysiaFaculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Johor, MalaysiaFaculty of Artificial Intelligence, Zhejiang Dongfang Polytechnic, Wenzhou, ChinaWith the growing complexity and interdependence of urban systems, multi-objective optimization (MOO) has become a critical tool for smart-city planning, sustainability, and real-time decision-making. This article presents a systematic literature review (SLR) of 117 peer-reviewed studies published between 2015 and 2025, assessing the evolution, classification, and performance of MOO techniques in smart-city contexts. Existing algorithms are organised into four families—bio-inspired, mathematical theory-driven, physics-inspired, and machine-learning-enhanced—and benchmarked for computational efficiency, scalability, and scenario suitability across six urban domains: infrastructure, energy, transportation, Internet of Things (IoT)/cloud systems, agriculture, and water management. While established methods such as Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multiobjective Evolutionary Algorithm based on Decomposition (MOED/D) remain prevalent, hybrid frameworks that couple deep learning with evolutionary search display superior adaptability in high-dimensional, dynamic environments. Persistent challenges include limited cross-domain generalisability, inadequate uncertainty handling, and low interpretability of artificial intelligence (AI)-assisted models. Twelve research gaps are synthesised—from privacy-preserving optimisation and sustainable trade-off resolution to integration with digital twins, large language models, and neuromorphic computing—and a roadmap towards scalable, interpretable, and resilient optimisation frameworks is outlined. Finally, a ready-to-use benchmarking toolkit and a deployment-oriented algorithm-selection matrix are provided to guide researchers, engineers, and policy-makers in real-world smart-city applications. This review targets interdisciplinary researchers, optimisation developers, and smart-city practitioners seeking to apply or advance MOO techniques in complex urban systems.https://peerj.com/articles/cs-3042.pdfSmart citiesMulti-objective optimizationSystematic literature reviewUrban optimizationSustainable urban developmentSustainability trade-offs |
| spellingShingle | YiFan Chen Weng Howe Chan Eileen Lee Ming Su Qi Diao Multi-objective optimization for smart cities: a systematic review of algorithms, challenges, and future directions PeerJ Computer Science Smart cities Multi-objective optimization Systematic literature review Urban optimization Sustainable urban development Sustainability trade-offs |
| title | Multi-objective optimization for smart cities: a systematic review of algorithms, challenges, and future directions |
| title_full | Multi-objective optimization for smart cities: a systematic review of algorithms, challenges, and future directions |
| title_fullStr | Multi-objective optimization for smart cities: a systematic review of algorithms, challenges, and future directions |
| title_full_unstemmed | Multi-objective optimization for smart cities: a systematic review of algorithms, challenges, and future directions |
| title_short | Multi-objective optimization for smart cities: a systematic review of algorithms, challenges, and future directions |
| title_sort | multi objective optimization for smart cities a systematic review of algorithms challenges and future directions |
| topic | Smart cities Multi-objective optimization Systematic literature review Urban optimization Sustainable urban development Sustainability trade-offs |
| url | https://peerj.com/articles/cs-3042.pdf |
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