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Nature-Inspired Heuristic Frameworks Trends in Solving Multi-objective Engineering Optimization Problems

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Abstract

Nowadays, nature-inspired artificial intelligent metaheuristic optimization algorithms (MHOAs) have gained many attentions from researchers all over the world due to their capabilities in solving various decision-making problems. These algorithms are inspired and modelled based on the searching behaviour of animals in real life. This review paper provides in-depth discussions on various challenges and breakthroughs in numerous state-of-the-art nature-inspired artificial intelligence (AI) algorithms in solving multi-objective optimization engineering problems with emphasis on the mathematical modelling and algorithm developments. From conventional analysis such as speeds and accuracies to relatively advanced benchmarks such as complexities and convergence patterns, the comparison criteria of population-based and nature-inspired search mechanisms have evolved in the effort to further enhance the overall performance and reachability of these heuristic algorithms. This paper provides a platform for young readers and new researches who are about to indulge in the realm of various AI optimization techniques. Comprehensive analysis and discussions are presented on various state-of-the-art methods, with possible fields of applications proposed. Suitability of search mechanisms to specific optimization problem categories has also been investigated and presented, with combined or hybrid methods under scrutiny.

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Acknowledgements

The authors would like to extend heartfelt gratitude to Xiamen University Malaysia for supporting this study under XMUM Research Fund: XMUMRF/2021-C7/IECE/0020.

Funding

This study was funded by XMUM Research Fund (XMUMRF/2021-C7/IECE/0020).

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Authors and Affiliations

  1. School of Energy and Chemical Engineering, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900, Sepang, Selangor, Malaysia

    Clifford Choe Wei Chang

  2. School of Electrical Engineering and Artificial Intelligence, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900, Sepang, Selangor, Malaysia

    Tan Jian Ding, Wang Han, Mohammad Arif Sobhan Bhuiyan & Goh Sim Kuan

  3. Graduate School of Engineering, Nagaoka University of Technology, Kamitomiokamachi, Nagaoka, Niigata, 940-2137, Japan

    Chloe Choe Wei Ee

  4. Institute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Jalan Ikram Uniten, 43000, Kajang, Selangor, Malaysia

    Johnny Koh Siaw Paw

  5. School of Computing and Data Science, Xiamen University Malaysia, 43900, Sepang, Selangor, Malaysia

    Iftekhar Salam

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  1. Clifford Choe Wei Chang
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Chang, C.C.W., Ding, T.J., Ee, C.C.W. et al. Nature-Inspired Heuristic Frameworks Trends in Solving Multi-objective Engineering Optimization Problems. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-024-10090-x

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