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In power systems, optimal power flow (OPF) is a complex and constrained optimization problem in which quite often multiple and conflicting objectives are required to be optimized. The traditional way of dealing with multi-objective OPF (MOOPF) is the weighted sum method which converts the multi-objective OPF into a single-objective problem and provides a single solution from the set of Pareto solutions. This paper presents MOOPF study applying multi-objective evolutionary algorithm based on decomposition (MOEA/D) where a set of non-dominated solutions ( Pareto solutions) can be obtained in a single run of the algorithm. OPF is formulated with two or more objectives among fuel (generation) cost, emission, power loss and voltage deviation. The other important aspect in OPF problem is about satisfying power system constraints. As the search process adopted by evolutionary algorithms is unconstrained, for a constrained optimization problem like OPF, static penalty function approach has been extensively employed to discard infeasible solutions. This approach requires selection of a suitable penalty coefficient, largely done by trial-and-error, and an improper selection may often lead to violation of system constraints. In this paper, an effective constraint handling method, superiority of feasible solutions (SF), is used in conjunction with MOEA/D to handle network constraints in MOOPF study. The algorithm MOEA/D-SF is applied to standard IEEE 30-bus and IEEE 57-bus test systems. Simulation results are analyzed, especially for constraint violation and compared with recently reported results on OPF.

Autonomous navigation for Unmanned Aerial Vehicles (UAVs) has emerged as a critical enabler in various industries, from agriculture, delivery services, and surveillance to search and rescue operations. However, navigating UAVs in dynamic and unknown environments remains a formidable challenge. This paper explores the application of the D* algorithm, a prominent path-planning method rooted in artificial intelligence and widely used in robotics, alongside comparisons with other algorithms, such as A* and RRT*, to augment autonomous navigation capabilities in UAVs’ implication for sustainability development. The core problem addressed herein revolves around enhancing UAV navigation efficiency, safety, and adaptability in dynamic environments. The research methodology involves the integration of the D* algorithm into the UAV navigation system, enabling real-time adjustments and path planning that account for dynamic obstacles and evolving terrain conditions. The experimentation phase unfolds in simulated environments designed to mimic real-world scenarios and challenges. Comprehensive data collection, rigorous analysis, and performance evaluations paint a vivid picture of the D* algorithm’s efficacy in comparison to other navigation methods, such as A* and RRT*. Key findings indicate that the D* algorithm offers a compelling solution, providing UAVs with efficient, safe, and adaptable navigation capabilities. The results demonstrate a path planning efficiency improvement of 92%, a 5% reduction in collision rates, and an increase in safety margins by 2.3 m. This article addresses certain challenges and contributes by demonstrating the practical effectiveness of the D* algorithm, alongside comparisons with A* and RRT*, in enhancing autonomous UAV navigation and advancing aerial systems. Specifically, this study provides insights into the strengths and limitations of each algorithm, offering valuable guidance for researchers and practitioners in selecting the most suitable path-planning approach for their UAV applications. The implications of this research extend far and wide, with potential applications in industries such as agriculture, surveillance, disaster response, and more for sustainability.

In today’s era of rapid development of educational informatization, improving teaching quality has become the key to the reform and development of the educational field. However, traditional teaching quality evaluation methods often have problems, such as solid subjectivity and insufficient real-time, which are challenging to meet the needs of modern education development. Therefore, this study proposed a real-time evaluation and optimization model of teaching quality, which combines improved D * and RRT algorithms. Based on big data analysis technology, this model combines the dynamic education quality planning ability of the D * algorithm with the fast search characteristics of the RRT algorithm, aiming at realizing real-time monitoring, evaluation, and optimization of teaching quality. The model can provide real-time feedback on the teaching effect and provide a scientific basis for educational decision-makers through a comprehensive analysis of several indices in the teaching process. In the experimental part, we selected the actual teaching data of a university as the research object and compared and analyzed the performance of traditional evaluation methods and the model proposed in this study in terms of evaluation accuracy, real-time, and guidance. The results show that the evaluation accuracy of this model is 15.6% higher than that of traditional methods. The real-time performance is 21.8% higher, and it shows significant advantages in the guidance of teaching optimization suggestions. This research result provides a more accurate and efficient teaching quality evaluation tool for educational administrators. It has important practical significance and application value for promoting education and teaching reform and improving teaching quality.

Multi-objective evolutionary algorithm based on Decomposition (MOEA/D) decomposes a multi-objective problem into a number of scalar optimization problems using uniformly distributed weight vectors. However, uniformly distributed weight vectors do not guarantee uniformity of solutions on approximated Pareto-Front. This study proposes an adaptive strategy to modify these scalarizing weights after regular intervals by assessing the crowdedness of solutions using crowding distance operator. Experiments carried out over several benchmark problems with complex Pareto-Fronts show that such a strategy helps in improving the convergence and diversity of solutions on approximated Pareto-Front. Proposed algorithm also shows better performance when compared with other state-of-the-art multi-objective algorithms over most of the benchmark problems.

When the multi-receiver synthetic aperture sonar (SAS) works with a wide-bandwidth signal, the performance of the range-Doppler (R-D) algorithm is seriously affected by two approximation errors, i.e., point target reference spectrum (PTRS) error and residual quadratic coupling error. The former is generated by approximating the PTRS with the second-order term in terms of the instantaneous frequency. The latter is caused by neglecting the cross-track variance of secondary range compression (SRC). In order to improve the imaging performance in the case of wide-bandwidth signals, an improved R-D algorithm is proposed in this paper. With our method, the multi-receiver SAS data is first preprocessed based on the phase center approximation (PCA) method, and the monostatic equivalent data are obtained. Then several sub-blocks are generated in the cross-track dimension. Within each sub-block, the PTRS error and residual quadratic coupling error based on the center range of each sub-block are compensated. After this operation, all sub-blocks are coerced into a new signal, which is free of both approximation errors. Consequently, this new data is used as the input of the traditional R-D algorithm. The processing results of simulated data and real data show that the traditional R-D algorithm is just suitable for an SAS system with a narrow-bandwidth signal. The imaging performance would be seriously distorted when it is applied to an SAS system with a wide-bandwidth signal. Based on the presented method, the SAS data in both cases can be well processed. The imaging performance of the presented method is nearly identical to that of the back-projection (BP) algorithm.

Considering of the dynamic cooperative path planning problem of multiple UAVs in complex environments, this paper further considers the flight constraints, space coordination, and fast re-planning of UAVs after detecting sudden obstacles on the basis of conflict-based search algorithm (CBS). A sparse CBS-D* algorithm is proposed as a cooperative dynamic path planning algorithm for UAVs in sudden threats. The algorithm adopts the two-layer planning idea. At the low layer, a sparse D* algorithm, which can realize the 3D dynamic path planning of UAVs, is proposed by combining the dynamic constraints of UAVs with the D* algorithm. At the high layer, heuristic information is introduced into the cost function to improve the search efficiency, and a dynamic response mechanism is designed to realize rapid re-planning in the face of sudden threats. The simulation results show that the proposed algorithm can deal with the UAV cooperative dynamic path planning problem in a complex environment more quickly and effectively.

With the increasing proportion of wind turbines in power grids, they are required to have capabilities of active and efficient virtual inertial response to maintain grid frequency stability. However, the virtual inertial control methods currently used in doubly-fed induction generator (DFIG) units suffer from a secondary frequency drop (SFD) problem. Although the SFD can be inhibited by reducing the active power support strength of the DFIG units during inertia response, it will undoubtedly weaken the virtual inertia of the units. Therefore, how to eliminate the SFD while increasing the virtual inertia of the units is a worthy issue for studying. To solve this issue, a wind-storage combined virtual inertial control system based on quantization and regulation decoupling of active power increments is proposed in this paper. First, by setting the parameters of a proportional–differential (P-D) algorithm, the total active power increments required for virtual inertial response are quantified at the DFIG level. Secondly, a curve-shifting method based on the rate of change of frequency is adopted to adjust the active power output of the DFIG units. Finally, a battery energy storage system (BESS) is used to compensate for the power shortages of the units according to the quantized value of the active power increments. Simulations show that the control method can not only eliminate SFD but also effectively increase the system’s virtual inertia.

The extraction of terrain feature lines is an important yet challenging problem in the processing and usage of contour lines. Most research has focused on identifying terrain feature points by using methods such as the Douglas–Peucker (D–P) algorithm and on determining contour adjacent relations for closed contour lines by using methods such as Delaunay triangulation and its dual Voronoi diagram, with few attempts made to address open contour lines and identify the direction of contour lines. Here, we propose a novel method for terrain feature line extraction based on adjacent relation trees. First, a contour adjacent relation tree is constructed. The tree is used to determine the directions of closed and open contour lines and thereby form a “directed adjacent relation tree” (DART). Terrain feature points are then extracted using the classic D–P algorithm, followed by the identification and connection of valley and ridge points via the aforementioned DART. Finally, integrity compensation for terrain feature lines is performed based on a slope extension algorithm. The effectiveness and accuracy of this method is verified by experimental results obtained from 1:10,000 topographic map data.

With the rapid development of the times, new tools are constantly appearing in visual communication design, such as the use of image packaging in visual communication. In order to speed up the transmission speed of visual images, ensure the integrity of visual images, and solve the transmission effect during visual communication. In this paper, based on the modern information fusion context, the object visualization image virtual packaging for model construction, the introduction of MOEA/D algorithm, the decomposition technique to decompose the MOP problem into a series of subproblems to solve, the use of weight vectors to obtain the neighbors of each subproblem, followed by the calculation of neighbor subproblems, the division of individuals into segments to obtain the child individuals. Finally, the fitness of each offspring individual was calculated and cut to give the final level of each factor. The final calculation of the proportion of images in different media communication from the MOEA/D algorithm leads to the strategy of using image virtual packaging in visual communication design. The experimental results showed that by means of multiple control groups, the experimental group achieved a 30% correct rate for Q3 and Q9 quiz questions, and the experimental group had a significantly greater correct rate than the control group. Therefore, more design concepts and design thinking can be explored through the study and analysis of image virtualization to help the use of image virtual packaging in visual communication design work.

To address the limitations of the Deep Deterministic Policy Gradient (DDPG) in robot path planning, we propose an improved DDPG method that integrates kinematic analysis and D* algorithm, termed D*-KDDPG. Firstly, the current work promotes the reward function of DDPG to account for the robot’s kinematic characteristics and environment perception ability. Secondly, informed by the global path information provided by the D* algorithm, DDPG successfully avoids getting trapped in local optima within complex environments. Finally, a comprehensive set of simulation experiments is carried out to investigate the effectiveness of D*-KDDPG within various environments. Simulation results indicate that D*-KDDPG completes strategy learning within only 26.7% of the training steps required by the original DDPG, retrieving enhanced navigation performance and promoting safety. D*-KDDPG outperforms D*-DWA with better obstacle avoidance performance in dynamic environments. Despite a 1.8% longer path, D*-KDDPG reduces navigation time by 16.2%, increases safety distance by 72.1%, and produces smoother paths.