Abstract
Over the last decade, metaheuristic algorithms have emerged as a powerful paradigm for global optimization of multimodal functions formulated by nonlinear problems arising from various engineering subjects. However, numerical analyses of many complex engineering design problems may be performed using finite element method (FEM) or computational fluid dynamics (CFD), by which function evaluations of population-based algorithms are repetitively computed to seek a global optimum. It is noted that these simulations become computationally prohibitive for design optimization of complex structures. To efficiently and effectively address this class of problems, an adaptively integrated swarm intelligence-metamodelling (ASIM) technique enabling multi-level search and model management for the optimal solution is proposed in this paper. The developed technique comprises two steps: in the first step, a global-level exploration for near optimal solution is performed by adaptive swarm-intelligence algorithm, and in the second step, a local-level exploitation for the fine optimal solution is studied on adaptive metamodels, which are constructed by the multipoint approximation method (MAM). To demonstrate the superiority of the proposed technique over other methods, such as conventional MAM, particle swarm optimization, hybrid cuckoo search, and water cycle algorithm in terms of computational expense associated with solving complex optimization problems, one benchmark mathematical example and two real-world complex design problems are examined. In particular, the key factors responsible for the balance between exploration and exploitation are discussed as well.
Highlights
With tremendous advances in computational sciences, information technology, and artificial intelligence, design optimization becomes increasingly popular in many engineering subjects, such as mechanical, civil, structural, aerospace, automotive, and energy engineering
Based on a response surface methodology, multipoint approximation method (MAM) aims to construct midrange approximations and is suitable to solve complex optimization problems owing to (1) producing better-quality approximations that are sufficiently accurate in a current trust region and (2) affordability in terms of computational costs required for their building
Each particle’s current position in adaptively integrated swarm intelligence-metamodelling (ASIM) gains local refinement by optimization of metamodel building around their neighborhood and tends to move towards the global best position according to swarm intelligence
Summary
With tremendous advances in computational sciences, information technology, and artificial intelligence, design optimization becomes increasingly popular in many engineering subjects, such as mechanical, civil, structural, aerospace, automotive, and energy engineering. Based on a response surface methodology, multipoint approximation method (MAM) aims to construct midrange approximations and is suitable to solve complex optimization problems owing to (1) producing better-quality approximations that are sufficiently accurate in a current trust region and (2) affordability in terms of computational costs required for their building These approximation functions have a relatively small number (N + 1 where N is number of design variables) of regression coefficients to be determinedm and the corresponding least squares problem can be solved [25]. The proposed ASIM framework takes the advantage of PSO in global searching and reduces the burden on computation by introducing the metamodel building technique, model management, and trust region strategy
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