Metaheuristic Algorithms in Optimal Design of Engineering Problems
Metaheuristic algorithms are advanced optimization methods widely used to solve complex engineering design problems. They operate by iteratively searching the solution space, employing strategies inspired by natural or physical processes to balance exploration and exploitation. Common examples inclu...
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| বিন্যাস: | Online |
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| ভাষা: | ইংরেজি |
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MDPI - Multidisciplinary Digital Publishing Institute
2026
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | ONIX_20260416T142754_9783725850754_36 |
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| _version_ | 1869524397539196928 |
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| collection | Directory of Open Access Books |
| description | Metaheuristic algorithms are advanced optimization methods widely used to solve complex engineering design problems. They operate by iteratively searching the solution space, employing strategies inspired by natural or physical processes to balance exploration and exploitation. Common examples include genetic algorithms, particle swarm optimization, simulated annealing, and ant colony optimization. These algorithms are effective for large-scale, nonlinear, and non-convex problems, making them valuable in fields such as mechanical, civil, electrical, and aerospace engineering. Metaheuristics can efficiently find near-optimal solutions where traditional methods may fail or become computationally expensive. Their performance depends on factors like initial solution quality, algorithm selection, and parameter tuning. By integrating metaheuristics with domain-specific knowledge, engineers can optimize system designs to meet performance, cost, and operational constraints. As research progresses, metaheuristic algorithms continue to expand their applicability and effectiveness in solving real-world engineering challenges. |
| format | Online |
| id | doab-20.500.12854ir-174881 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1748812026-04-16T17:09:09Z Metaheuristic Algorithms in Optimal Design of Engineering Problems Knypiński, Łukasz Devarapalli, Ramesh Kaminski, Marcin Optimization Multi-objective optimization Engineering problems Particle swarm optimization Metaheuristic optimization algorithms thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilities Metaheuristic algorithms are advanced optimization methods widely used to solve complex engineering design problems. They operate by iteratively searching the solution space, employing strategies inspired by natural or physical processes to balance exploration and exploitation. Common examples include genetic algorithms, particle swarm optimization, simulated annealing, and ant colony optimization. These algorithms are effective for large-scale, nonlinear, and non-convex problems, making them valuable in fields such as mechanical, civil, electrical, and aerospace engineering. Metaheuristics can efficiently find near-optimal solutions where traditional methods may fail or become computationally expensive. Their performance depends on factors like initial solution quality, algorithm selection, and parameter tuning. By integrating metaheuristics with domain-specific knowledge, engineers can optimize system designs to meet performance, cost, and operational constraints. As research progresses, metaheuristic algorithms continue to expand their applicability and effectiveness in solving real-world engineering challenges. 2026-04-16T17:09:02Z 2026-04-16T17:09:02Z 2025 book ONIX_20260416T142754_9783725850754_36 9783725850754 9783725850761 https://directory.doabooks.org/handle/20.500.12854/174881 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/ https://mdpi.com/books/pdfview/book/11762 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-5076-1 10.3390/books978-3-7258-5076-1 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725850754 9783725850761 238 CH open access |
| spellingShingle | Optimization Multi-objective optimization Engineering problems Particle swarm optimization Metaheuristic optimization algorithms thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilities Metaheuristic Algorithms in Optimal Design of Engineering Problems |
| title | Metaheuristic Algorithms in Optimal Design of Engineering Problems |
| title_full | Metaheuristic Algorithms in Optimal Design of Engineering Problems |
| title_fullStr | Metaheuristic Algorithms in Optimal Design of Engineering Problems |
| title_full_unstemmed | Metaheuristic Algorithms in Optimal Design of Engineering Problems |
| title_short | Metaheuristic Algorithms in Optimal Design of Engineering Problems |
| title_sort | metaheuristic algorithms in optimal design of engineering problems |
| topic | Optimization Multi-objective optimization Engineering problems Particle swarm optimization Metaheuristic optimization algorithms thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilities |
| topic_facet | Optimization Multi-objective optimization Engineering problems Particle swarm optimization Metaheuristic optimization algorithms thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilities |
| url | ONIX_20260416T142754_9783725850754_36 |