Adaptive mesh refinement using piecewise-linear shape functions based on the blending function method

Schramm, Uwe; Möller, Peter; Reissmann, C

doi:10.1007/bf01299394

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…As a result of this, the algorithm lacks information when searching for and updating location, resulting in inaccurate location update and slow convergence speed. Some academics use adaptive weight into other optimization algorithms in order to boost the algorithm's capacity to optimize [34][35][36][37][38]. To increase the algorithm's local mining capacity, it is important to re-update the neighborhood of the search prey position, improve the original algorithm's solution accuracy in the local neighborhood, and locate the best solution in the search space.…”

Section: Improvement Of Adaptive Weight Strategymentioning

confidence: 99%

An Improved Bald Eagle Search Algorithm with Cauchy Mutation and Adaptive Weight Factor for Engineering Optimization

Wang¹,

Tian²,

Chau³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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The Bald Eagle Search algorithm (BES) is an emerging meta-heuristic algorithm. The algorithm simulates the hunting behavior of eagles, and obtains an optimal solution through three stages, namely selection stage, search stage and swooping stage. However, BES tends to drop-in local optimization and the maximum value of search space needs to be improved. To fill this research gap, we propose an improved bald eagle algorithm (CABES) that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima. Firstly, CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage, to select a better search range. Secondly, in the search stage, CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES. To verify the performance of CABES, the benchmark function of CEC2017 is used to simulate the algorithm. The findings of the tests are compared to those of the Particle Swarm Optimization algorithm (PSO), Whale Optimization Algorithm (WOA) and Archimedes Algorithm (AOA). The experimental results show that CABES can provide good exploration and development capabilities, and it has strong competitiveness in testing algorithms. Finally, CABES is applied to four constrained engineering problems and a groundwater engineering model, which further verifies the effectiveness and efficiency of CABES in practical engineering problems.

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