Orthogonal Design Method for Optimizing Roughly Designed Antenna

Orthogonal design method (ODM) is widely used in real world application while it is not used for antenna design yet. It is employed to optimize roughly designed antenna in this paper. The geometrical factors of the antenna are relaxed within specific region and each factor is divided into some levels, and the performance of the antenna is constructed as objective. Then the ODM samples small number of antennas over the relaxed space and finds a prospective antenna. In an experiment of designing ST5 satellite miniantenna, we first get a roughly evolved antenna. The reason why we evolve roughly is because the evolving is time consuming even if numerical electromagnetics code 2 (NEC2) is employed (NEC2 source code is openly available and is fast in wire antenna simulation but not much feasible). Then the ODM method is employed to locally optimize the antenna with HFSS (HFSS is a commercial and feasible electromagnetics simulation software). The result shows the ODM optimizes successfully the roughly evolved antenna

Prediction method of truck travel time in open pit mines

Aiming at the prediction of truck travel time in open pit mines, we established a prediction model based on long short-term memory(LSTM). This model fully accounts for 11 factors, including the nature of trucks, weather, road conditions, and driver’s behaviors, as well as the influence of neighbor road segments in the route on the current predicted road segment. The experiment shows that the error of the LSTM prediction model is significantly reduced compared with SVR and BP models. In addition, the maximum absolute mean error under different conditions is less than 12 seconds

Handling constrained many-objective optimization problems via problem transformation

The file attached to this record is the author's final peer reviewed version.Objectives optimization and constraints satisfaction are two equally important goals to solve constrained many-objective optimization problems (CMaOPs). However, most existing studies for CMaOPs can be classified as feasibility-driven constrained many-objective evolutionary algorithms (C-MaOEAs), they always give priority to satisfy constraints, while ignoring the maintenance of the population diversity for dealing with conflicting objectives. Consequently, the population may be pushed towards some locally feasible optimal or locally infeasible areas in the high-dimensional objective space. To alleviate this issue, this paper presents a problem transformation technique, which transforms a CMaOP into a dynamic CMaOP (DCMaOP) for handling constraints and optimizing objectives simultaneously, to help the population cross the large and discrete infeasible regions. The well-known reference-point-based NSGA-III is tailored under the problem transformation model to solve CMaOPs, namely DCNSGA-III. In this paper, ε -feasible solutions play an important role in the proposed algorithm. To this end, in DCNSGA-III, a mating selection mechanism and an environmental selection operator are designed to generate and choose high-quality ε-feasible offspring solutions, respectively. The proposed algorithm is evaluated on a series of benchmark CMaOPs with 3, 5, 8, 10, and 15 objectives and compared against six state-of-the-art CMaOEAs. The experimental results indicate that the proposed algorithm is highly competitive for solving CMaOPs

An adaptive evolutionary algorithm for bi-level multi-objective VRPs with real-time traffic conditions

The file attached to this record is the author's final peer reviewed version.The research of vehicle routing problem (VRP) is significant for people traveling and logistics distribution. Recently, in order to alleviate global warming, the VRP based on electric vehicles has attracted much attention from researchers. In this paper, a bi-level routing problem model based on electric vehicles is presented, which can simulate the actual logistics distribution process. The classic backpropagation neural network is used to predict the road conditions for applying the method in real life. We also propose a local search algorithm based on a dynamic constrained multi-objective optimization framework. In this algorithm, 26 local search operators are designed and selected adaptively to optimize initial solutions. We also make a comparison between our algorithm and 3 modified algorithms. Experimental results indicate that our algorithm can attain an excellent solution that can satisfy the constraints of the VRP with real-time traffic conditions and be more competitive than the other 3 modified algorithms

A Robust Technique without Additional Computational Cost in Evolutionary Antenna Optimization

A robustness-enhancing technique without additional computational cost in antenna optimization design is presented. The robustness is implemented by minimizing the variances of the gains, axial ratios and VSWRs over the required frequency band. It is demonstrated that the new technique has two obvious advantages. One is that it can ensure the antenna robustness without the extra computational overhead. The other one is that it is possible to broaden the bandwidth of the antenna. We apply this technique to design a microstrip antenna at 2.4GHz. Experimental results show that, by adopting this new technique, the evolved antenna is more robust than by using two other techniques

An adaptive multi-population evolutionary algorithm for contamination source identification in water distribution systems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Real-time monitoring of drinking water in a water distribution system (WDS) can effectively warn and reduce safety risks. One of the challenges is to identify the contamination source through these observed data due to the real-time, non-uniqueness, and large scale characteristics. To address the real-time and non-uniqueness challenges, we propose an adaptive multi-population evolutionary optimization algorithm to determine the real-time characteristics of contamination sources, where each population aims to locate and track a different global optimum. The algorithm adaptively adjusts the number of populations using a feed-back learning mechanism. To effectively locate an optimal solution for a population, a co-evolutionary strategy is used to identify the location and the injection profile separately. Experimental results on three WDS networks show that the proposed algorithm is competitive in comparison with three other state-of-the-art evolutionary algorithms

An efficient multi-objective evolutionary algorithm: OMOEA-II

Abstract- A new algorithm is proposed to solve constrained multi-objective problems in this paper. The constraints of the MOPs are taken account of in determining Pareto dominance. As a result, the feasibility of solutions is not an issue. At the same time, it takes advantage of both the orthogonal design method to search evenly, and the statistical optimal method to speed up the computation. The output of the technique is a large set of solutions with high precision and even distribution. Notably, for an engineering problem WATER, it finds the Pareto-optimal set, which was previously unknown

Radar Complex Intermediate Frequency Signal Denosing Based on Convolutional Auto-Encoder Network

In radar systems, target state features are commonly extracted from intermediate frequency signals. However, these signals often have a low signal-to-noise ratio due to noisy environments and limitations of the radar hardware. This can lead to a significant loss in performance during target state feature extraction. Therefore, improving the signal-to-noise ratio of intermediate frequency signals is crucial for the effective operation of radar systems. To solve this problem, we developed a deep learning-based method for denoising intermediate frequency signals in this paper. Our approach involves using an auto-encoder network to remove unstructured noise and recover the original signal. During the signal preprocessing stage, it is important to ensure that the phase of the complex signal remains undistorted and that differences in signal amplitudes do not negatively affect the denoising performance. To achieve this, the real and imaginary parts of the complex signal are separated and subjected to 0–1 normalization. The loss function of the denoising network is then established based on signal correlation. The numerical results demonstrate that the proposed method outperforms other denoising techniques in terms of mean square error and denoising performance