Nature-Inspired Heuristic Frameworks Trends in Solving Multi-objective Engineering Optimization Problems
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 beha...
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| Format: | Review |
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Springer Science and Business Media B.V.
2025
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| author | Chang C.C.W. Ding T.J. Ee C.C.W. Han W. Paw J.K.S. Salam I. Bhuiyan M.A.S. Kuan G.S. |
| author2 | 57473577900 |
| author_facet | 57473577900 Chang C.C.W. Ding T.J. Ee C.C.W. Han W. Paw J.K.S. Salam I. Bhuiyan M.A.S. Kuan G.S. |
| author_sort | Chang C.C.W. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. ? The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2024. |
| format | Review |
| id | my.uniten.dspace-36463 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | Springer Science and Business Media B.V. |
| record_format | dspace |
| spelling | my.uniten.dspace-364632025-03-03T15:42:33Z Nature-Inspired Heuristic Frameworks Trends in Solving Multi-objective Engineering Optimization Problems Chang C.C.W. Ding T.J. Ee C.C.W. Han W. Paw J.K.S. Salam I. Bhuiyan M.A.S. Kuan G.S. 57473577900 38863172300 58953009700 56097111100 58168727000 36601445500 55433759000 58953500700 Benchmarking Biomimetics Decision making Multiobjective optimization Artificial intelligent Decision-making problem Engineering optimization problems Metaheuristic optimization Multi objective Optimization algorithms Review papers Search mechanism Searching behavior State of the art Heuristic algorithms 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. ? The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2024. Final 2025-03-03T07:42:33Z 2025-03-03T07:42:33Z 2024 Review 10.1007/s11831-024-10090-x 2-s2.0-85188509203 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188509203&doi=10.1007%2fs11831-024-10090-x&partnerID=40&md5=61906357271692d099defd9ba609f3cb https://irepository.uniten.edu.my/handle/123456789/36463 31 6 3551 3584 Springer Science and Business Media B.V. Scopus |
| spellingShingle | Benchmarking Biomimetics Decision making Multiobjective optimization Artificial intelligent Decision-making problem Engineering optimization problems Metaheuristic optimization Multi objective Optimization algorithms Review papers Search mechanism Searching behavior State of the art Heuristic algorithms Chang C.C.W. Ding T.J. Ee C.C.W. Han W. Paw J.K.S. Salam I. Bhuiyan M.A.S. Kuan G.S. Nature-Inspired Heuristic Frameworks Trends in Solving Multi-objective Engineering Optimization Problems |
| title | Nature-Inspired Heuristic Frameworks Trends in Solving Multi-objective Engineering Optimization Problems |
| title_full | Nature-Inspired Heuristic Frameworks Trends in Solving Multi-objective Engineering Optimization Problems |
| title_fullStr | Nature-Inspired Heuristic Frameworks Trends in Solving Multi-objective Engineering Optimization Problems |
| title_full_unstemmed | Nature-Inspired Heuristic Frameworks Trends in Solving Multi-objective Engineering Optimization Problems |
| title_short | Nature-Inspired Heuristic Frameworks Trends in Solving Multi-objective Engineering Optimization Problems |
| title_sort | nature-inspired heuristic frameworks trends in solving multi-objective engineering optimization problems |
| topic | Benchmarking Biomimetics Decision making Multiobjective optimization Artificial intelligent Decision-making problem Engineering optimization problems Metaheuristic optimization Multi objective Optimization algorithms Review papers Search mechanism Searching behavior State of the art Heuristic algorithms |
| url_provider | http://dspace.uniten.edu.my/ |