Abstract
The optimisation of complex engineering design problems is highly challenging due to the consideration of various design variables. To obtain acceptable near-optimal solutions within reasonable computation time, metaheuristics can be employed for such problems. However, a plethora of novel metaheuristic algorithms are developed and constantly improved and hence it is important to evaluate the applicability of the novel optimisation strategies and compare their performance using real-world engineering design problems. Therefore, in this paper, eight recent population-based metaheuristic optimisation algorithms—African Vultures Optimisation Algorithm (AVOA), Crystal Structure Algorithm (CryStAl), Human-Behaviour Based Optimisation (HBBO), Gradient-Based Optimiser (GBO), Gorilla Troops Optimiser (GTO), Runge–Kutta optimiser (RUN), Social Network Search (SNS) and Sparrow Search Algorithm (SSA)—are applied to five different mechanical component design problems and their performance on such problems are compared. The results show that the SNS algorithm is consistent, robust and provides better quality solutions at a relatively fast computation time for the considered design problems. GTO and GBO also show comparable performance across the considered problems and AVOA is the most efficient in terms of computation time.
Highlights
Published: 8 December 2021An engineering design problem comprises of designing products that meet the functional requirements by considering a multitude of values for decision variables [1]
It is possible to use the class of optimisation algorithms that fall under the category of metaheuristics such that the design space is explored with a guiding heuristic technique [4]
The performance of the Runge–Kutta optimiser (RUN) algorithm was qualitatively assessed by three metrics: (i) search history, (ii) trajectory graph, and (iii) convergence curve and the algorithm was compared with benchmark problems to assess its ability to explore and exploit the design space efficiently
Summary
An engineering design problem comprises of designing products that meet the functional requirements by considering a multitude of values for decision variables [1]. The algorithms perform global exploration and local exploitation and are able to provide acceptable good solutions for complex problems within reasonable computation time. A comparison of these newgeneration metaheuristic optimisation algorithms on their applicability and performance on various engineering design problems has not been evaluated. In this paper, the above-mentioned eight new-generation population-based metaheuristic optimisation algorithms are compared and evaluated on five different classical mechanical machinery component design problems such as the tension/compression spring design, crane hook design, reduction gear design, pressure vessel design and hydrostatic thrust bearing design. To the best of the authors’ knowledge, this is the first benchmark research study that quantitatively and qualitatively evaluates the selected eight new-generation metaheuristic optimisation algorithms by applying them in various mechanical machinery component design problems.
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