Metaheuristic Algorithms in Optimal Design of Engineering Problems

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dc.contributor.authorKnypiński, Łukasz
dc.contributor.authorDevarapalli, Ramesh
dc.contributor.authorKaminski, Marcin
dc.date.accessioned2025-11-25T07:57:22Z
dc.date.available2025-11-25T07:57:22Z
dc.date.issued2025
dc.description238 p.
dc.description.abstractMetaheuristic 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.
dc.identifier.isbn9783725850761
dc.identifier.urihttps://oerrepository.ntt.edu.vn/handle/298300331/1229
dc.language.isoen
dc.publisherMDPI
dc.subjectOptimization
dc.subjectmulti-objective optimization
dc.subjectengineering problems
dc.subjectparticle swarm optimization
dc.subjectmetaheuristic optimization algorithms
dc.titleMetaheuristic Algorithms in Optimal Design of Engineering Problems
dc.typeBook
dcterms.licenseCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
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