A Relaxed and Bound Algorithm Based on Auxiliary Variables for Quadratically Constrained Quadratic Programming Problem

Quadratically constrained quadratic programs (QCQP), which often appear in engineering practice and management science, and other fields, are investigated in this paper. By introducing appropriate auxiliary variables, QCQP can be transformed into its equivalent problem (EP) with non-linear equality constraints. After these equality constraints are relaxed, a series of linear relaxation subproblems with auxiliary variables and bound constraints are generated, which can determine the effective lower bound of the global optimal value of QCQP. To enhance the compactness of sub-rectangles and improve the ability to remove sub-rectangles, two rectangle-reduction strategies are employed. Besides, two ϵ-subproblem deletion rules are introduced to improve the convergence speed of the algorithm. Therefore, a relaxation and bound algorithm based on auxiliary variables are proposed to solve QCQP. Numerical experiments show that this algorithm is effective and feasible.

Downlink User Power Allocation and Joint Rate Maximization in UAV-Relayed Assisted SWIPT-NOMA System

In this paper, in order to improve the performance of 5G wireless communication system and save power consumption to achieve the optimal power distribution and maximize the total user rate in a multi-user cluster of non-orthogonal multiple access (NOMA) system downlink. a SWIPT-NOMA system assisted by dynamic unmanned aerial vehicle (UAV) relay is constructed. The UAV relay dynamic programming under two-hop communication was firstly studied, and the global optimal power allocation strategy for downlink users of SWIPT-NOMA system is found. Finally, the optimal relay selection algorithm was used to maximize the total user rate, which was verified by Monto Carlo simulation. The simulation results show that the performance of the proposed system model is better than that of the traditional FDMA system scheme in terms of outage probability, energy consumption and total user rate under different distribution scenarios for the multi-user cluster deployed with dynamic UAV relay system.

Robust Estimation of Deformation from Observation Differences Using Some Evolutionary Optimisation Algorithms

In this paper, an original modification of the generalised robust estimation of deformation from observation differences (GREDOD) method is presented with the application of two evolutionary optimisation algorithms, the genetic algorithm (GA) and generalised particle swarm optimisation (GPSO), in the procedure of robust estimation of the displacement vector. The iterative reweighted least-squares (IRLS) method is traditionally used to perform robust estimation of the displacement vector, i.e., to determine the optimal datum solution of the displacement vector. In order to overcome the main flaw of the IRLS method, namely, the inability to determine the global optimal datum solution of the displacement vector if displaced points appear in the set of datum network points, the application of the GA and GPSO algorithms, which are powerful global optimisation techniques, is proposed for the robust estimation of the displacement vector. A thorough and comprehensive experimental analysis of the proposed modification of the GREDOD method was conducted based on Monte Carlo simulations with the application of the mean success rate (MSR). A comparative analysis of the traditional approach using IRLS, the proposed modification based on the GA and GPSO algorithms and one recent modification of the iterative weighted similarity transformation (IWST) method based on evolutionary optimisation techniques is also presented. The obtained results confirmed the quality and practical usefulness of the presented modification of the GREDOD method, since it increased the overall efficiency by about 18% and can provide more reliable results for projects dealing with the deformation analysis of engineering facilities and parts of the Earth’s crust surface.

Application of Marine Predator Algorithm in Solving the Problem of Directional Overcurrent Relay in Electrical Power System

In electrical power systems, directional overcurrent relay (DOCR) coordination is assumed to be an essential component of the system for protection purposes. To diminish and reduce power losses, the coordination between these relays ought to be kept at an ideal value to minimalize the overall operating time of all primary-relay shortcoming situations. The coordination of DOCR is a complex and profoundly compelling nonlinear problem. The objective function is to minimalize the overall total operating time of all essential relays to minimize inordinate breakdown and interference. Coordination is performed using the marine predator algorithm (MPA), inspired by a widespread foraging strategy, namely Lévy and Brownian movements, to search for global optimal solutions in order to resolve the DOCRs coordination issue. The results acquired from MPA are equated with other state-of-the-art algorithms, and it was observed that the proposed algorithm outperforms other algorithms.

Heat Exchanger Network Optimization Based on the Participatory Evolution Strategy for Streams

The non-structural model of a heat exchanger network randomly selects a position of a node on hot and cold streams to generate a heat exchanger and an existing heat exchanger to participate in the evolution. Despite the model being more random and flexible, this selection method cannot easily find a good solution. In addition, the heat exchangers participating in the evolution might not be involved in all streams in each evolutionary process. A stream that does not participate in the evolution will have no significance to the current iteration. Therefore, many iterations are required to make each stream participate in the evolution, which limits the evolution efficiency of the optimization algorithm. In view of this shortcoming, this study proposes a participatory evolutionary strategy for streams based on hot streams. The proposed strategy reorders the existing heat exchangers on hot and cold streams and takes the corresponding measures to ensure that a heat exchanger is selected for each stream to participate in the evolution in every cycle. The proposed participatory evolutionary strategy for streams improves the global optimal solution for designs based on non-structural models. The effectiveness of the proposed strategy is demonstrated in two cases.

Global Simultaneous Optimization of Oil, Hysteretic and Inertial Dampers Using Real-Valued Genetic Algorithm and Local Search

A method for global simultaneous optimization of oil, hysteretic and inertial dampers is proposed for building structures using a real-valued genetic algorithm and local search. Oil dampers has the property that they can reduce both displacement and acceleration without significant change of natural frequencies and hysteretic dampers possess the characteristic that they can absorb energy efficiently and reduce displacement effectively in compensation for the increase of acceleration. On the other hand, inertial dampers can change (prolong) the natural periods with negative stiffness and reduce the effective input and the maximum acceleration in compensation for the increase of deformation. By using the proposed simultaneous optimization method, structural designers can select the best choice of these three dampers from the viewpoints of cost and performance indices (displacement, acceleration). For attaining the global optimal solution which cannot be attained by the conventional sensitivity-based approach, a method including a real-valued genetic algorithm and local search is devised. In the first stage, a real-valued genetic algorithm is used for searching an approximate global optimal solution. Then a local search procedure is activated for enhancing the optimal character of the solutions by reducing the total quantity of three types of dampers. It is demonstrated that a better design from the viewpoint of global optimality can be obtained by the proposed method and the preference of damper selection strongly depends on the design target (displacement, acceleration). Finally, a multi-objective optimization for the minimum deformation and acceleration is investigated.

Path Planning of Mobile Robots in Warehouse Logistics Relying on Computer Multimedia 3D Reconstruction Technology

Because traditional methods are difficult to solve the problems related to the path planning of logistics robots, this study proposes a method of using computer multimedia 3D reconstruction technology to realize the path planning of warehouse logistics robots. Without fully considering the accurate movement path between points, according to the warehouse logistics robot, it is judged whether the starting point is at the exit. The planning problem of the movement path is converted into a TSP problem and a TS-TSP problem. Finally, the analysis of experimental results shows that the method proposed in this study converges faster than traditional algorithms and can quickly obtain the global optimal solution. At the same time, the warehousing logistics robot requires less path planning time and has strong practical application.

Powerful and Novel Tumour Detection in Brain MRI Images Employing Hybrid Computational Techniques

Brain tumour detection is an evergreen topic to attract attention in the examination field of Information Technology innovation with biomedical designing, in view of the gigantic need of proficient and viable strategy for assessment of enormous measure of information. Image segmentation is considered as one of the most vital systems for visualizing tissues in an individual. To robotize image segmentation, we have proposed a calculation to get global optimal thresholding esteem for a specific brain MRI image, utilizing OTSU+Sauvola binarization strategy. The fundamental reason for feature collection is to diminish the quantity of structures utilized in classification while keeping up satisfactory classification exactness. One of the most extra-customary procedures applied for feature extraction is Discrete Wavelet Transform (DWT). Adequately it anticipates the estimation space on a plane to such an extent that the fluctuation of the information is ideally protected. We propose a justifiable model for brain tumours discovery and classification i.e., to classify whether the tumour is benign or malignant, utilizing SVM classification. SVM utilized here deals with basic hazard minimization to group the images for the tumour extraction, and a Graphical User Interface is created for the tumour classification operation, using the MATLAB platform.