Introduction to Optimum Design

Zhao, Zhiye

doi:10.1016/s0266-8920(08)80002-1

Cited by 3 publications

(3 citation statements)

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“…The objective of this test problem is to minimize the weight of the tension/compression spring. Figure 3 shows the spring and its parameters [39,40]. The optimum design must satisfy constraints on shear stress, surge frequency, and deflection.…”

Section: Tension/compression Spring Designmentioning

confidence: 99%

“…The objective of this test problem is to minimize the weight of the tension/compression spring. Figure 3 shows the spring and its parameters [39,40] Consider There are several solutions for this problem found in the literature. This test case was solved using either mathematical techniques (constraints correction at constant cost [41] and penalty functions [40]) or meta-heuristics, such as GSA [42], PSO [43], evolution strategy (ES) [44], GA [45], and improved harmony search (HS) [46].…”

Section: Tension/compression Spring Designmentioning

confidence: 99%

“…The objective of this test problem is to minimize the weight of the tension/compression spring. Figure 3 shows the spring and its parameters [39,40] This problem was solved by GWO [49], GSA [42], Richardson's random method, simplex method, Davidon-Fletcher-Powell, and Griffith and Stewart's successive linear approximation [45]. The optimization results obtained by the proposed LAFBA for this problem were evaluated by comparing it with 15 other optimization algorithms that were previously reported, as shown in Table 6.…”

Section: Tension/compression Spring Designmentioning

confidence: 99%

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An Improved Bat Algorithm Based on Lévy Flights and Adjustment Factors

Liu

et al. 2019

Symmetry

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This paper proposed an improved bat algorithm based on Lévy flights and adjustment factors (LAFBA). Dynamically decreasing inertia weight is added to the velocity update, which effectively balances the global and local search of the algorithm; the search strategy of Lévy flight is added to the position update, so that the algorithm maintains a good population diversity and the global search ability is improved; and the speed adjustment factor is added, which effectively improves the speed and accuracy of the algorithm. The proposed algorithm was then tested using 10 benchmark functions and 2 classical engineering design optimizations. The simulation results show that the LAFBA has stronger optimization performance and higher optimization efficiency than basic bat algorithm and other bio-inspired algorithms. Furthermore, the results of the real-world engineering problems demonstrate the superiority of LAFBA in solving challenging problems with constrained and unknown search spaces.

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Section: Tension/compression Spring Designmentioning

confidence: 99%