Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Please note that all times are shown in the time zone of the conference. The current conference time is: 13th May 2024, 12:23:02pm JST
A DATA-DRIVEN MULTI-OBJECTIVE OPTIMIZATION ALGORITHM FOR COMPUTATIONALLY EXPENSIVE ELECTROMAGNETIC DESIGNS
Yiying LI, Shiyou YANG
Zhejiang University, China, People's Republic of
This paper proposes a surrogate-assisted evolutionary algorithm based on an artificial neural network (ANN) for computationally expensive multi-objective optimization problems of electromagnetic devices. The training data of ANN are selected from the current population according to the Pareto rank and the crowding distances of individuals to learn cumulative knowledge from the searched solutions in the evolution. Reproduction of the offspring is implemented by the trained ANN instead of the traditional genetic operators. Finally, the performance of the proposed algorithm is tested on the team workshop problem 35.
3:40pm - 4:00pm ID: 158 / OA-A3: 2 Regular_Abstract (short paper) Submission Topics: Optimization and Inverse Problems (OIP) Keywords: 3D Inductor, Monte Carlo tree search, optimal design, saturated current.
INTEGRATED DESIGN OF INDUCTORS USING MONTE CARLO TREE SEARCH BASED ON THREE-DIMENSIONAL MODEL
Shuli YIN, Hajime IGARASHI
Hokkaido University, Japan
This study extends the application of Monte Carlo tree search (MCTS) to the comprehensive design of three-dimensional (3D) inductors with nonlinear characteristics. Specifically, configuration settings of inductors are determined by MCTS, and the finite element method is employed for the simulation. This research demonstrates the effectiveness of MCTS in achieving desired performance of inductors, such as attaining a target value of inductance, and meeting the requirement of minimum saturated current.
STRUCTURAL DYNAMICS MODEL UPDATING METHOD BASED ON A NOVEL PARTICLE SWARM OPTIMIZATION ALGORITHM
Zhenyu WANG1, Junhan AN1, Cheng HE2, Huan HE1
1State Key Laboratory of Mechanic and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, 210016, Nanjing, China; 2Research Institute of Pilotless Aircraft, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
A structural dynamics model updating method based on a novel particle swarm optimization algorithm with Lévy flight and orthogonal learning (LOPSO) is proposed and successfully applied to the updating of the finite element (FE) model of a typical complex multi-component structure. The LOPSO is designed to overcome premature convergence and better solve inverse problems such as parameter identification and model updating. The LOPSO method is used to update the equivalent stiffness and damping parameters of shafting bearings. It is proved that the new algorithm has higher accuracy. After updating, the errors of the parameters were reduced to less than 1%. The accuracy of the FE model was significantly improved, verifying the effectiveness of the proposed method and its applicability to engineering problems.
ON THE IDENTIFICATION OF UNKNOWN TARGETS VIA KIRCHHOFF MIGRATION
Won-Kwang PARK
Kookmin University, Korea, Republic of (South Korea)
For a proper application of the Kirchhoff migration technique for imaging small anomaly, complete element of the scattering matrix must be collected. However, it is very hard to measure the diagonal element of scattering matrix in real-world microwave imaging. Here, we set the diagonal elements as a constant, apply the KM for anomaly imaging, and show that zero constant choice guarantees good imaging results.
