2022
DOI: 10.1007/s00521-021-06747-4
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Meta-heuristic optimization algorithms for solving real-world mechanical engineering design problems: a comprehensive survey, applications, comparative analysis, and results

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Cited by 94 publications
(24 citation statements)
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“…Nowadays, as the computational scale and complexity of various engineering application problems increase, the original traditional optimization algorithms and heuristics may no longer confront the current practical situation [3,4], e.g., image classification and simulation, building load-bearing structure optimization, solar energy parameter optimization, etc [5]. These problems are multi-dimensional, nonlinear, multi-fitting NP-hard problems [6], which have posed great challenges to the existing computing system.…”
Section: Introductionmentioning
confidence: 99%
“…Nowadays, as the computational scale and complexity of various engineering application problems increase, the original traditional optimization algorithms and heuristics may no longer confront the current practical situation [3,4], e.g., image classification and simulation, building load-bearing structure optimization, solar energy parameter optimization, etc [5]. These problems are multi-dimensional, nonlinear, multi-fitting NP-hard problems [6], which have posed great challenges to the existing computing system.…”
Section: Introductionmentioning
confidence: 99%
“…Several strategies have been developed to address various optimization challenges ( Meraihi et al, 2021 ). Each algorithm employs a particular acceptable technique for certain problems and efficiently solves them while being ineffective for others ( Abualigah et al, 2022 ). However, the vast majority of them fall into one of two groups.…”
Section: Introductionmentioning
confidence: 99%
“…These MH strategies are generally inspired by nature, physics principles, and human behavior. The primary classes of MH include natural phenomena-based, swarm-based, human-based, and evolutionary-based techniques ( Abualigah et al, 2022 ). Natural phenomena-based approaches imitate natural phenomena such as spirals, rain, wind, and light ( Ewees et al, 2021 ; Şahin, Dinler & Abualigah, 2021 ).…”
Section: Introductionmentioning
confidence: 99%
“…1a). ML tasks include recognising objects, understanding speech, responding to a conversation, solving problems, optimising solutions, greeting people, and driving a vehicle [18][19][20]. Rumelhart et al [21] initially proposed shallow learning that initiated ML applications (Fig.…”
Section: Introductionmentioning
confidence: 99%