Multi-constraint Optimization Research Articles

Path Planning of an Unmanned Aerial Vehicle Based on a Multi-Strategy Improved Pelican Optimization Algorithm.

The UAV path planning algorithm has many applications in urban environments, where an effective algorithm can enhance the efficiency of UAV tasks. The main concept of UAV path planning is to find the optimal flight path while avoiding collisions. This paper transforms the path planning problem into a multi-constraint optimization problem by considering three costs: path length, turning angle, and collision avoidance. A multi-strategy improved POA algorithm (IPOA) is proposed to address this. Specifically, by incorporating the iterative chaotic mapping method with refracted reverse learning strategy, nonlinear inertia weight factors, the Levy flight mechanism, and adaptive t-distribution variation, the convergence accuracy and speed of the POA algorithm are enhanced. In the CEC2022 test functions, IPOA outperformed other algorithms in 69.4% of cases. In the real map simulation experiment, compared to POA, the path length, turning angle, distance to obstacles, and flight time improved by 8.44%, 5.82%, 4.07%, and 9.36%, respectively. Similarly, compared to MPOA, the improvements were 4.09%, 0.76%, 1.85%, and 4.21%, respectively.

Design of Differential Loudspeaker Line Array for Steerable Frequency-Invariant Beamforming

Differential beamforming has attracted much research since it can utilize an array with a small aperture size to form frequency-invariant beampatterns and achieve high directional gains. It has recently been applied to the loudspeaker line array to produce a broadside frequency-invariant radiation pattern. However, designing steerable frequency-invariant beampatterns for the loudspeaker line array has yet to be explored. This paper proposes a method to design a steerable differential beamformer with a loudspeaker line array. We first determine the target differential beampatterns according to the desired direction, the main lobe width, and the beampattern order. Then, we transform the target beampattern into the modal domain for representation. The Jacobi-Anger expansion is subsequently used to design the beamformer so that the resulting beampattern matches the target differential beampattern. Furthermore, based on the criterion of minimizing the mean square error between the synthesized beampattern and the ideal one, a multi-constraint optimization problem, which compromises between the robustness and the mean square error, is formulated to calculate the optimal desired weighting vector. Simulations and experimental results show that the proposed method can achieve steerable frequency-invariant beamforming from 300 Hz–4 kHz.

High-Resolution Dipole Remote Detection Logging Based on Optimal Nonlinear Frequency Modulation Excitation.

In order to improve the detection range and imaging resolution of dipole remote detection logging, an optimal nonlinear frequency modulation (ONLFM) excitation method is proposed in this paper. In this method, the optimal waveform model of the sound source is designed with objective functions of SNR and resolution to obtain the highest resolution under the condition of the required SNR. This optimal model is a multi-constraint optimization problem, and the simulated annealing method has been used to solve it. Solving the optimal model can obtain the optimal spectrum, and the ONLFM waveform can be designed by using the stationary phase principle. Compared with the electric pulse sound source used in traditional technology, this ONLFM sound source can improve the energy and extend the frequency band range of the reflected wave, which can provide the higher resolution and SNR. The simulation results show that the ONLFM sound source can effectively improve the SNR and resolution of the reflected wave, and the detection range and imaging resolution of dipole shear wave remote detection will be improved.

Cyber-Physical Scheduling System for Multiobjective Scheduling Optimization of a Suspension Chain Workshop Using the Improved Non-Dominated Sorting Genetic Algorithm II

Cyber-Physical Systems (CPSs) offer significant potential to address the evolving demands of industrial development. In the Industry 4.0 era, a framework integrating sensing, data exchange, numerical analysis, and real-time feedback is essential for meeting modern industrial needs. However, implementing this integration presents challenges across multiple domains, particularly in digital analysis, information sensing, and data exchange during corporate transformation. Companies yet to undergo transformation face distinct challenges, including the risks and trial-and-error costs of adopting new technologies. This study focuses on a heavy-duty workpiece processing factory, with a specific emphasis on the painting process. The complexity of this process frequently results in congestion, which is approached as a multi-objective, multi-constraint optimization problem. This paper proposes the Improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) to address the requirements of multi-objective optimization. The proposed approach uses multi-chromosome structures, listeners, and recursive backtracking initialization to optimize the search for solutions under constraints. This enables the factory to automatically streamline production lines based on workpiece processing sequences, leading to increased efficiency. Additionally, a CPS framework focused on simulation modeling has been designed. First, the INSGA-II algorithm processes order data to generate production schedules. Next, the data transmission formats and input-output interfaces are designed. Then, a simulation model is built using the algorithm’s results. These components collectively form the CPS framework for this study. The proposed method offers an automated digital solution through the algorithm, enabling verification of its feasibility via the simulation model. As a result, it significantly enhances decision-making speed, reliability, and equipment utilization.

Computational design of auxetic microstructures via stress-based topology optimization

Material design plays a pivotal role in industries dedicated to lightweight manufacturing. In response to the needs of such industries, topology optimization (TO) has evolved beyond structural optimization to include designing material microstructures for improved structural performance. While conventional approaches often prioritize stiffness-oriented designs as a foundational aspect of structural integrity, it is equally imperative to emphasize strength-based design principles to ensure robustness against localized stress concentrations and potential failure modes. This paper explores the application of stress-constrained topology optimization (SCTO) for designing auxetic microstructures. The study commences with a comprehensive parametric analysis to examine the influence of the P-norm parameter, variations in volume fraction, and initial design adjustments on the resulting microstructures. Several optimized structures, accompanied by stress contours, are generated to showcase how the optimization process responds to these parameters. The approach employs the density-based solid isotropic material with penalization material interpolation scheme, and it utilizes the stress penalization technique to mitigate the singularity phenomenon. The multi-constraint optimization employs the method of moving asymptotes algorithm to update design variables. The effectiveness of the proposed approach to utilize actual strain fields, derived from macroscopic finite element analysis under service loads, for TO of an auxetic microstructure according to strength requirements is demonstrated. The application of the designed microstructure is explored through its integration into an auxetic cushion pad. This analysis confirms the microstructure’s effectiveness in practical applications, demonstrating its potential for broader usage in similar applications.

Improved Snake Optimizer Using Sobol Sequential Nonlinear Factors and Different Learning Strategies and Its Applications

The Snake Optimizer (SO) is an advanced metaheuristic algorithm for solving complicated real-world optimization problems. However, despite its advantages, the SO faces certain challenges, such as susceptibility to local optima and suboptimal convergence performance in cases involving discretized, high-dimensional, and multi-constraint problems. To address these problems, this paper presents an improved version of the SO, known as the Snake Optimizer using Sobol sequential nonlinear factors and different learning strategies (SNDSO). Firstly, using Sobol sequences to generate better distributed initial populations helps to locate the global optimum solution faster. Secondly, the use of nonlinear factors based on the inverse tangent function to control the exploration and exploitation phases effectively improves the exploitation capability of the algorithm. Finally, introducing learning strategies improves the population diversity and reduces the probability of the algorithm falling into the local optimum trap. The effectiveness of the proposed SNDSO in solving discretized, high-dimensional, and multi-constraint problems is validated through a series of experiments. The performance of the SNDSO in tackling high-dimensional numerical optimization problems is first confirmed by using the Congress on Evolutionary Computation (CEC) 2015 and CEC2017 test sets. Then, twelve feature selection problems are used to evaluate the effectiveness of the SNDSO in discretized scenarios. Finally, five real-world technical multi-constraint optimization problems are employed to evaluate the performance of the SNDSO in high-dimensional and multi-constraint domains. The experiments show that the SNDSO effectively overcomes the challenges of discretization, high dimensionality, and multi-constraint problems and outperforms superior algorithms.

Improved FPA for aircraft conceptual design

Aircraft weight design is a key technology in aircraft conceptual design. The design of aircraft weight is directly related to many design parameters of the aircraft, and there are complex constraint relationships between these parameters. It is difficult to determine the design parameters in the process of aircraft conceptual design with an accurate mathematical analytical model, it is necessary to use an optimization algorithm. Firstly, to build the optimization object function, the aircraft weight function and some constrain functions are given out, thus, the aircraft weight design problem is transformed into a single objective multi-constraint optimization problem, aiming at the problem of parameter coupling, Aitken acceleration algorithm is proposed to solve the problem through iterative, thus, and a feasible solution of aircraft parameters are obtained; Secondly, considering the advantages of flower pollination algorithms in solving unconstrained problems, the flower pollination algorithm (FPA) combined with the Aitken acceleration algorithm is proposed for automatic global optimization, to avoid the FPA falling into the local optimal solution or missing optimal solution, propose improvements to the FPA algorithm from three aspects: adaptive conversion probability, adaptive step size adjustment, and adaptive reproduction probability generation. To use FPA, the multi-constraint optimization problem is transformed into a multi-parameter unconstrained optimization problem with a penalty term based on the penalty function method. Finally, a design example is given to verify the effectiveness of the algorithm, and the superiority of the improved algorithm is verified by comparing it with PSO and the fixed transition probability FPA.

Surge-Elimination Strategy for Aero-Engine Transient Control

Abstract The fundamental principle of the transient state control method for turbofan engines, which is based on the acceleration ratio of high-pressure rotational speed (N-dot), involves sacrificing a portion of the safety margin to obtain satisfactory acceleration performance. However, it could induce surge in the engine's compressor. To prevent the destructive damage caused by surge to both the engine and its components, a surge-elimination control strategy for the engine based on an N-dot controller is proposed. First, the engine mathematical model, which incorporates the effects of engine volumetric dynamics, stall zone characteristics, and combustion chamber flameout characteristics, is established to simulate surge mechanism. Subsequently, the acceleration schedule of the N-dot is calculated by employing sequential quadratic programming (SQP) algorithm to solve the multiconstraint optimization problem, while designing the transition state controller of N-dot based on a high-order filter. Finally, the surge detection logic and surge-elimination strategy based on the μ-correction method are proposed and designed to realize active control of surge elimination. The simulation results demonstrate that the N-dot control method offers significant advantages in mitigating the steady-state errors resulting from inevitable engine degradations. The surge state is effectively suppressed by the proposed surge-elimination control method, and the surge duration is significantly shorten during the acceleration phases. Furthermore, compared to the one without any surge-elimination control, the proposed method decreases the acceleration time by 5.53%.

Distributed short-term predictive control for AUV clusters in underwater cooperative hunting tasks

A distributed short-term predictive control method is proposed to be applied to autonomous underwater vehicle (AUV) clusters for underwater cooperative hunting tasks. To support AUV clusters in unknown underwater dynamic environments, the constraints of information acquisition, AUV motion constraints, collision avoidance, and smoothness requirements of motion paths in the tasks are considered. First, a distributed multi-constraint optimization model including target attraction, interactions between AUVs, continuous obstacle avoidance, and control effort is proposed. Effective information between AUVs is extended under limited information conditions. Second, each AUV solves the distributed optimization problem and determines the desired trajectory in real time using information from the prediction horizon. In the simulation experiments, the proposed method is demonstrated to provide at least 12.19%, 11.08%, and 79.89% improvement in terms of task completion time, energy consumption, and path smoothing, respectively, when compared to other methods under the same information conditions. The robustness of the proposed method under different noise conditions is verified by positioning noise experiments, and the task can still be accomplished even if the noise level reaches 75% of the safe distance. The adaptability of the proposed method is demonstrated by simulations in different environments.

Epigenetik Algoritma ile Konferans Çizelgeleme

The most important of the activities where the presentations of scientific studies take place are academic conferences. The days, halls, and sessions are determined in advance to organize multidisciplinary conferences and this process is called conference scheduling. In multidisciplinary conferences, in the scheduling of presentations, the coexistence of studies belonging to the same fields in the same sessions is very important for the conference listener and the conference speaker. In this context, the multidisciplinary conference scheduling problem is considered a multi-constraint optimization problem. Multi-constraint optimization problems are solved with heuristic optimization techniques, not traditional optimization methods. In this study, the problem of conference scheduling is addressed using multidisciplinary conference data. The solution to the conference scheduling problem was realized with Genetic Algorithm (GA) and Epigenetic Algorithm (EGA) using C# programming language. In the study, experimental results obtained with GA and EGA were examined. As a result of this examination, it was seen that EGA achieved better results in fewer iterations compared to classical GA.

Conflict-free optimal motion planning for parallel aerial 3D printing using multiple UAVs

This article introduces a novel collaborative optimal motion planning framework for parallel aerial 3D printing. The proposed novel framework is efficiently capable of handling conflicts between the utilized Unmanned Aerial Vehicles (UAVs), as they follow predefined paths, allowing for a seamless enhancement of aerial 3D printing capabilities by employing multiple UAVs to collaborate in a parallel printing process. The established approach ingeniously formulates UAVs’ motion planning as a multi-constraint optimization problem, ensuring minimal adjustments to their velocities within specified limits. This guarantees smooth and uninterrupted printing while preventing collisions and adhering to the requirements of aerial printing. To substantiate the effectiveness of our proposed motion planning algorithm, an extensive array of simulation studies have been undertaken, encompassing scenarios where multiple UAVs engage in the fabrication of diverse construction shapes. The overall novel concept is being extensively validated in simulations, while the obtained results promise for enhancing the viability and advancing the landscape of aerial additive manufacturing.

Research on Time Series-Based Pipeline Ground Penetrating Radar Calibration Angle Prediction Algorithm.

The pipeline ground-penetrating radar stands as an indispensable detection device for ensuring underground space security. A wheeled pipeline robot is deployed to traverse the interior of urban underground drainage pipelines along their central axis. It is subject to influences such as resistance, speed, and human factors, leading to deviations in its posture. A guiding wheel is employed to rectify its roll angle and ensure the precise spatial positioning of defects both inside and outside the pipeline, as detected by the radar antenna. By analyzing its deflection factors and correction trajectories, the intelligent correction control of the pipeline ground-penetrating radar falls into the realm of nonlinear multi-constraint optimization. Consequently, a time-series-based correction angle prediction algorithm is proposed. The application of the long short-term memory (LSTM) deep learning model facilitates the prediction of correction angles and torque for the guiding wheel. This study compares the performance of LSTM with an autoregressive integrated moving average model under identical dataset conditions. The subsequent findings reveal a reduction of 4.11° and 8.25 N·m in mean absolute error, and a decrease of 10.66% and 7.27% in mean squared error for the predicted correction angles and torques, respectively. These outcomes are achieved utilizing the three-channel drainage pipeline ground-penetrating radar device with top antenna operating at 1.2 GHz and left/right antennas at 750 MHz. The LSTM prediction model intelligently corrects its posture. Experimental results demonstrate an average correction speed of 5 s and an average angular error of ±1°. It is verified that the model can correct its attitude in real-time with small errors, thereby enhancing the accuracy of ground-penetrating radar antennas in locating pipeline defects.

A semi-supervised high-quality pseudo labels algorithm based on multi-constraint optimization for speech deception detection

Deep learning-based speech deception detection research relies on a large amount of labeled data. However, in the process of collecting speech deception detection data, the identification of truth and lies requires researchers to have a professional knowledge reserve, which greatly limits the number of annotated samples. Improving the accuracy of lie detection with insufficient annotation data is the focus of this study at this stage. In this paper, we propose a semi-supervised high-quality pseudo-label algorithm based on multi-constraint optimization (HQPL-MC) for speech deception detection. Firstly, the algorithm exploits the potential feature information of unlabeled data by using deep auto-encoder networks; secondly, it achieves entropy minimization with the help of the pseudo labeling technique to reduce the class overlap distribution of truth and deception data; finally, it improves the quality of pseudo labels by optimizing the unlabeled loss and reconstruction loss to further enhance the classification performance of the model when the labeled data is insufficient. We recorded an interview-style corpus by ourselves and used it in this paper for the experimental demonstration of the algorithm together with the Columbia/SRI/Colorado(CSC) corpus. The detection performance of the proposed algorithm is better than most state-of-the-art algorithms.

A novel coverage-aware task allocation scheme in Cooperative Mobile Crowd Sensing

Task assignment is a key issue in Mobile Crowd Sensing, which affects the quality and cost of tasks. Most of the existing works mainly consider the scenario where workers can complete tasks independently, ignoring the requirement of cooperation with multiple workers for complex sensing tasks. The quality of the cooperation of workers’ teams needs to be evaluated, and the task coverage should be improved in Cooperative Mobile Crowd Sensing. Therefore, in this paper, we first introduce the team cohesion index by analyzing the impact of workers’ social networks on tasks and then propose a new measurement method to better evaluate the quality of workers’ teamwork. Then, to improve the task coverage, the task assignment is modeled as a multi-constraint optimization problem in view of several factors, including location, worker team cohesion, and skill coverage. The greedy approach and the heuristic genetic algorithm are combined to create the two-stage algorithm known as GGA. Finally, experiments are conducted based on real datasets, which verify the effectiveness of the team cohesion index measurement method and task allocation algorithm GGA.

Re-Scheduling IoT Services in Edge Networks

With the explosive growth of the Internet of Things (IoT) devices deployed in edge networks, the functionalities of IoT devices are typically encapsulated as IoT services, and user requests can be achieved through the composition of data and/or computation-intensive IoT services. Considering the prediction-uncertainty of forthcoming requests, certain IoT services may (i) not be hosted currently by appropriate IoT devices, or (ii) such an IoT service exists, but its non-functional properties may hardly be satisfied with respect to certain constraints prescribed by requests. To address this challenge, this paper proposes an efficiency-aware service Migration Scheduling (denoted eMS) mechanism in edge networks, in order to migrate IoT services on-demand, and thus, to optimally settle non-satisfiable constraints. Specifically, IoT services are re-scheduled, such that certain IoT services are migrated from their hosting IoT devices to neighboring ones, while minimizing the energy consumption and average delay caused by this service re-scheduling operation. We formulate this service re-scheduling as a multi-objective and multi-constraint optimization problem, which is solved through integrating the greedy algorithm into the fast non-dominated sorting and crowded-comparison operators as the hybrid genetic algorithm (G-NSGA-II). Based on real-life datasets provided by an oil pipeline monitoring project, extensive experiments are conducted, and evaluation results show that our eMS is promising in reducing the energy consumption and average delay of service re-scheduling in comparison with the state-of-art’s techniques.

Non-smooth/non-convex economic dispatch through modified particle swarm optimization

Economic Dispatch (ED) is the foremost typical task in the power system engineering. Its aim to minimize the total generation cost while satisfying system constraints. Considering some practical features like prohibited operating zones, transmission losses and ramp rate limits ED becomes a non-convex, non-smooth, non-linear, and multi-constraint optimization problem. To solve complex optimization problems, particle swarm optimization (PSO) have proved noticeable success. However, it may suffer to trap at local minima, slow convergence and poor solutions. Thus, to address these issues and solve ED problem, a modified particle swarm optimization (mPSO) is introduced. In mPSO, new factors for inertia, cognitive and social components (i.e. acceleration coefficients and inertia weight) are familiarized. As per the concerts these factors are dynamically changed and properly maintained the diversity of the PSO. The projected mPSO is implemented to solve three altered unit test structures (3, 6 and 15) of ED problem. The experimental and compared results shows that proposed mPSO produce more appropriate economic cost.

An Enhanced Multi-Constraint Optimization Algorithm for Efficient Network Topology Generation

In order to address a problem in the research related to the low stability and communication efficiency issues in the generation of optical communication constellation network topology, there is a critical component for sensing the interaction among satellites. This paper makes a novel contribution by proposing a multi-constraint optimization algorithm for optical communication constellation network topology generation. The proposed method significantly improves the existing systems by considering multiple attributes that influence the establishment of inter-satellite links and reducing the impact of subjective factors. This unique approach involves calculating the entropy weight of each attribute using the information entropy method based on the degree of change in each indicator. Subsequently, the weights of the indicators are corrected to obtain the objective weight of each attribute. The comprehensive weight of the link, computed based on the initial link attribute values and weights, serves as the decision basis for link selection, thereby forming the satellite network topology. Upon evaluation, the proposed method has shown remarkable superiority over the compared schemes in terms of communication efficiency and stability.

A Multi-Objective and Multi-Constraint Optimization Model for Cyber-Physical Power Systems Considering Renewable Energy and Electric Vehicles

Dear editor To tackle the global challenges of climate change and energy security, building low carbon energy systems has become a research hotspot. Cyber-physical power systems (CPPSs) is an important infrastructure to link both energy and transport systems, two major sectors that are difficult to decarbonize, and it is necessary to establish CPPSs model to consider the integration of both renewable energy and electric vehicle (EV).

AFLI-Calib: Robust LiDAR-IMU extrinsic self-calibration based on adaptive frame length LiDAR odometry

As an effective complement to common laser scanning systems, the portable laser scanning system can acquire point clouds flexibly and quickly. Calibration between Light detection and ranging (LiDAR) sensors and inertial measurement units (IMU) is the prerequisite for laser scanning systems to obtain high-quality point clouds. Related methods have been proposed in the last two decades, where the global navigation satellite system (GNSS) or high-precision calibration fields are commonly used. However, the extrinsic self-calibration of LiDAR-IMU is challenging, due to the large distortion in single-frame point cloud caused by rapid motion and the position errors of IMU integration which drift quickly. At the same time, the highly dynamic motion patterns of portable devices and the changes in the scanned scene structure are not well considered in existing LiDAR odometry methods. To take better advantage of the characteristics of non-repetitive scanning LiDAR sensor, this paper proposes AFLI-Calib, which utilizes adaptive frame length LiDAR odometry to perform the extrinsic self-calibration of LiDAR-IMU. Unlike LiDAR odometry methods with a fixed frame length, the LiDAR frame length is dynamically adjusted according to the motion state of sensors and the matching stability of scenes. The single-frame point cloud is registered to the map through a linear-based continuous-time model, eliminating the motion distortion correction in advance. For further optimization of trajectory and extrinsic parameters, IMU raw measurements and LiDAR observations are involved in the multi-constraint optimization, through tightly-coupled IMU pre-integration constraints, LiDAR point-to-plane constraints, and prior constraints. The method is fully validated using self-collected calibration data of indoor and outdoor scenes and different motion modes. Experiments show that on the test data, the translation parameter accuracy of the method is 0.041 m, which is 56.3% higher than the state-of-the-art method. The standard deviation is significantly reduced, with translation deviation (0.017 m, 0.024 m, 0.022 m) and rotation deviation (0.17°, 0.25°, 0.15°), which verifies the robustness of our method. The average RMSE of distances to the reference point cloud acquired by the terrestrial laser scanning system (TLS) is 0.042 m, showing a high accuracy calibration result. Comparative experiments with the fixed frame length LiDAR odometry method and classical “correction-then-registration” motion distortion model further verify the superiority and effectiveness of the proposed adaptive frame length LiDAR odometry.

Topology Optimization Design of Multi-Input-Multi-Output Compliant Mechanisms with Hinge-Free Characteristic and Totally Decoupled Kinematics

A new multi-constraint optimization model with the weighted objective function is proposed to design the multi-input-multi-output (MIMO) compliant mechanisms. The main feature of this work is that both the two notable problems related to the de facto hinge and the movement coupling are tackled simultaneously in the topological synthesis of MIMO compliant mechanisms. To be specific, the first problem is the severe stress concentration in the flexible hinge areas, and it is solved by the introduction of input and output compliances into the objective function, which could facilitate the optimization to strike a good balance between structural flexibility and stiffness. The second problem is the high degree of control complexity caused by the coupled outputs and inputs, and it is addressed by achieving the complete decoupling with two groups of extra constraints that are used to suppress the input coupling and the output coupling, respectively. As the most common and effective topology optimization method, the Solid Isotropic Material with Penalization (SIMP)-based density method is adopted here to obtain the optimized configurations. After the analytical sensitivity deduction related to the weighted objective function and constraints, two typical numerical examples are presented to demonstrate the validity of the proposed topology optimization framework in designing the hinge-free and completely decoupled MIMO compliant mechanisms.