Conventional Optimization Methods Research Articles

Crashworthiness analysis and shape design optimization of corrugated tubes for railway application

Thin-walled tubes have been widely used as energy-absorbing devices since they are light and have high energy absorption efficiency. However, the downside is that conventional thin-walled tubes usually exhibit an excessive peak crushing force (PCF) and large fluctuations in the force-displacement curve. Corrugated tubes are introduced to reduce the PCF and to increase the stability of energy-absorbing devices. Since the performance of corrugated tubes is highly influenced by their geometry, design optimization methods can be utilized to optimize the performance of corrugated tubes. It is difficult to apply conventional gradient-based optimization methods in crashworthiness analysis because of the strong nonlinearity and large computational time. In this paper, we utilize shape design optimization based on an adaptive surrogate model. The EA is maximized in transition crushing mode (T-mode), which is the boundary between progressive crushing mode (P-mode) and simultaneous crushing mode (S-mode), while simultaneously optimizing the contact force efficiency (CFE).

Multi-Agent Schedule Optimization Method for Regional Energy Internet Considering the Improved Tiered Reward and Punishment Carbon Trading Model

Regional energy internet (REI) contains massive market agents, whose interests and objectives vary from each other. In consequence, it is challenging to stimulate the energy conservation and emissions reduction participation of each agent by the conventional schedule optimization method. This paper proposes a multi-agent schedule optimization method for REI considering the improved tiered reward and punishment carbon trading model. Firstly, the energy flow constraints and device constraints of REI are established. Secondly, to tighten restrictions on carbon emissions, the relative carbon emission is used as the criterion to establish the improved tied reward and punishment carbon trading model. Next, to analyze the real multi-agent game situation in the market, different agents are classified, and the objective functions are defined based on their revenue. Finally, a two-layer algorithm is used to solve the above multi-agent model. Simulation results verify that the proposed method can effectively reduce carbon emissions and significantly enhance the revenue of the region.

Capacity configuration of distributed photovoltaic and battery system for office buildings considering uncertainties

In practice, the actual operation conditions are generally different from those assumed in the design stage, which causes uncertainties of actual building load and photovoltaic (PV) power generation. This study conducted an optimization design for PV-battery (PVB) system with consideration of these uncertainties. Three groups of factors with uncertainty, relating to outdoor conditions, building construction and indoor conditions, were discussed. An uncertainty-based multi-objective optimization design method was proposed for the PVB system based on the random simulations and weight sum method. Compared with conventional deterministic optimization method, the uncertainty-based optimization method is more likely to achieve the optimal configuration of PV and batteries in actual conditions. When the weight factors are the same, the impacts of self-consumption rate (SCR) on the PVB system configuration are larger than that of the self-sufficiency rate (SSR). The performance of the PVB system is much more sensitive to PV capacity than to battery capacity. In practice, on the premise of achieving the required SCR, the PV capacity should be maximized while the battery capacity be minimized. Representative values of technical indicators (SCR and SSR), of which a relative deviation of 5% covers more than 84% of all the results, were determined. Based on the representative values, the configuration indices of PV and battery capacities for office buildings in the hot-summer and cold-winter, and the IV solar resource zone of China, were proposed, and summarized in the form of table. The proposed configuration indices can be used directly as guide for PVB system design.

Velocity calibration by incremental pseudomaster events for single-well microseismic monitoring

The velocity model plays a critical role in accurately determining microseismic event locations during hydraulic fracturing. Conventional velocity optimization methods, such as updating the velocity model using perforation shots, have the drawback of weak ray coverage during single-well monitoring. The microseismic events distributed in different areas can broaden the ray coverage and help improve the accuracy of the velocity model, whereas only a few perforation shots are available for a given stage. We have developed an incremental pseudomaster (IPM) method to reduce the velocity errors. The master event location technique is used to determine the positions of representative events in each iteration of the IPM method. These events then act as new pseudomasters to further optimize the velocity model using Bayesian inference. With progressive iterations, the number of pseudomaster events increases and their distribution range expands. The accuracy of the velocity model continuously improves, which means that the final IPM-based velocity model, generated based on the pseudomasters distributed throughout the study area, more accurately locates all microseismic events than the perforation shot alone method. One synthetic example and one field test have been conducted, and the results demonstrate that the IPM method could overcome the drawbacks of weak ray coverage and improve the accuracy of the inverted velocity model and event locations.

Multi-condition multi-objective optimization using deep reinforcement learning

A novel multi-condition multi-objective optimization method that can find Pareto front over a defined condition space is developed using deep reinforcement learning. Unlike the conventional methods which perform optimization at a single condition, the present method learns correlations between conditions and optimal solutions. The exclusive capability of the developed method is examined in solutions of a modified Kursawe benchmark problem and an airfoil shape optimization problem. The solutions include nonlinear characteristics which are difficult to be resolved using conventional optimization methods. Pareto front with high resolution over a condition space is successfully determined in both problems. Compared with multiple operations of a single-condition optimization method for multiple conditions, the present multi-condition optimization method shows a greatly accelerated search of Pareto front by reducing the required number of function evaluations. An analysis of aerodynamic performance of optimally designed airfoils confirms that multi-condition optimization is indispensable to avoid significant degradation of target performance for varying flow conditions.

Offline tuning mechanism of joint angular controller for lower-limb exoskeleton with adaptive biogeographical-based optimization

Designing an accurate controller to overcome the nonlinearity of dynamic systems is a technical matter in control engineering, particularly for tuning the parameters of the controller precisely. In this paper, a tuning mechanism for a proportional-integral-derivative (PID) controller of lower limb exoskeleton (LLE) joints by adaptive biogeographical based-optimization (ABBO) is presented. The tuning of the controller is defined as an optimization problem and solved by ABBO, which is an iterative algorithm inspired by a blending crossover operator (BLX-?). The parameters of the migration change proportionally to the growth of iteration that conveys the error to rapid convergence by narrowing the searching space. The Lyapunov stability theory is proven for LLE nonlinear dynamic systems. ABBO algorithm is compared with other conventional optimization methods in step response, which guaranteed it was not trapped in local optima and demonstrated the lowest average error and the fastest convergence rate. The tuned controller is applied in a closed-loop system to verify its performance in the prototype. The experimental results of ABBO with PID controller ascertained that the proposed tuning mechanism is applicable in the LLE gait training.

Genetic algorithm-powered non-sequential dwell time optimization for large optics fabrication.

Computer Controlled Optical Surfacing (CCOS) is widely applied for fabricating large aspheric optical surfaces. For large optics fabrication, various sizes of polishing tools are used sequentially. This raises the importance of efficient and globally optimized dwell time map of each tool. In this study, we propose a GEnetic Algorithm-powered Non-Sequential (GEANS) optimization technique to improve the feasibility of the conventional non-sequential optimization technique. GEANS consists of two interdependent parts: i) compose an influence matrix by imposing constraints on adjacent dwell points and ii) induce the desired dwell time map through the genetic algorithm. CCOS simulation results show that GEANS generates a preferable dwell time map that provides high figuring efficiency and structural similarity with the shape of target removal map, while improving computational efficiency more than 1000 times over the conventional non-sequential optimization method. The practicability of GEANS is demonstrated through error analyses. Random tool positioning error and tool influence function errors are imposed on dwell time maps. Compared to the conventional non-sequential optimization method, the power spectral density values of residual surface error from GEANS remain stable. GEANS also shows superior applicability when the maximum acceleration of a tool is applied.

A novel two-step strategy of non-probabilistic multi-objective optimization for load-dependent sensor placement with interval uncertainties

Conventional optimal sensor placement methods use structural dynamic characteristics such as mode shapes to determine sampling positions for extracting information. However, these approaches have not considered actual load cases or structural responses. Relevant mode shapes in the responses are important for sensor configurations and damage detection. Because the sensor layouts under specific load cases and free vibrations are completely different from them in free vibration, using current sensor placement theories may lead to errors or even failure. In studies that consider both the dynamic characteristics and load cases, a novel uncertain load-dependent sensor placement method is developed using the non-probabilistic theory for response reconstruction, which has a two-step strategy. Based on the unbiased estimate of modal coordinates with a reduced and full model in the deterministic case, this study treats uncertainties as interval numbers, and the propagation of uncertain modal coordinates is proposed based on non-probabilistic theory. Provided uncertain but bounded responses are obtained, the uncertain modal coordinates are calculated easily without requiring the complex process of uncertainty quantification using statistical methods. Furthermore, a two-step strategy is used to select the optimum displacement sensor configurations. The non-probabilistic multi-objective optimization consists of deterministic and uncertain parts that can be solved using NSGA-II (Non-dominated Sorting Genetic Algorithm II) to obtain the preliminary Pareto front in the first step. For conveniently determining the final sensor configuration from a large number of candidate solutions located at the Pareto front, a novel interval time series model is constituted based on the ratio of reduced and full intervals. Therefore, the obtained solutions can be applied to reconstruct responses of the full structures from the deterministic and uncertain parts simultaneously. Two engineering examples are applied to verify the effectiveness and accuracy of the proposed method, accompanied by comprehensive discussions.

Finite Element Analysis and Support Vector Regression-Based Optimal Design to Minimize Deformation of Indoor Bicycle Handle Frame Equipped with Monitor

Exercise has been gaining importance, as well as people’s interest. Nowadays, exercise bikes and similar home training devices usually feature a monitor to provide visual information and increase people’s convenience. With the increasing size of the mounted monitor frame, it is imperative to consider the monitor weight while designing the handle for an indoor exercise bike equipped with a monitor. In this study, optimal design based on finite element (FE) analysis was applied to increase the safety and robustness of the handle of an indoor bicycle equipped with a monitor. Considering the load that may be imposed on the handle, and its location, four FE analysis cases were performed. Loading conditions that contributed the largest von Mises stress (out of the four cases) were applied. Five design variables were chosen to minimize the effective stress. Moreover, the factor arrangement method using five factors and two levels required the run of 25 = 32 design cases. The resulting Pareto chart and sensitivity analysis confirmed the relationship between the effective stress and the chosen design variables. To obtain a predictive model using support vector regression (SVR), we subsequently increased the data range to five factors and three levels. The SVR prediction model was trained using a polynomial kernel to find the kernel parameters that provided the highest accuracy. Based on the coefficient of determination, the accuracy of the SVR prediction model was found to be superior to the conventional regression-based optimal design method. Additionally, the value of the design variable with the minimum effective stress was obtained using the SVR prediction model. Furthermore, upon redesigning the handle of the bicycle with the optimized design variable values, we found that the maximum effective stress was reduced by 80% compared with the initial model. Finally, the effective stress predicted by the SVR model was similar to that from the FE analysis, which confirmed the reliability of the predictive model.

An improved posture evaluation method for cylindrical intersecting holes on large aerospace components based on monocular vision

Cylindrical intersecting holes (CIHs) are common connection and location reference features in the assembly of large aerospace structures such as missiles and rocket cabins. The posture accuracy of assembly holes significantly impacts the relative position accuracy of joined parts and the fatigue strength of the finished product. At present, monocular vision measurement is widely used in the automatic drilling of assembly holes due to its integration simplicity and lower cost, but in most research, only the front face edge of the hole is used in the measurement model, and the hole end surface is usually assumed to be plane, which inevitably leads to precision loss. In this research, a novel posture measurement method for CIHs is proposed. Firstly, by introducing an ambiguity removal strategy, a coarse posture estimation method based on the plane hypothesis of the two end surfaces of CIHs is suggested. Secondly, considering that there is no simple explicit expression for a CIH’s edge, it is difficult to adopt the conventional model projection-based pose optimization method. In view of this, the 3D points corresponding to the edge pixels of the CIHs’ image are derived, and the pose optimization model is established by minimizing the deviations between the distance from the points to the CIHs’ axis and the hole radius. Moreover, to better control the direction parameters of CIHs during the global optimization process, the approximately perpendicular and intersection constraints between the CIHs’ axis and cylindrical component axis are involved in the solution. The effectiveness of the posture measurement method is verified by comparative experiments with current methods and coordinate measuring machine, which demonstrates improvements in both measurement accuracy and robustness.

Robust trajectory and communication design for angle-constrained multi-UAV communications in the presence of jammers

This paper studies a multi-unmanned aerial vehicle (UAV) enabled wireless communication system, where multiple UAVs are employed to communicate with a group of ground terminals (GTs) in the presence of potential jammers. We aim to maximize the throughput overall GTs by jointly optimizing the UAVs' trajectory, the GTs' scheduling and power allocation. Unlike most prior studies, we consider the UAVs' turning and climbing angle constraints, the UAVs' three-dimensional (3D) trajectory constraints, minimum UAV-to-UAV (U2U) distance constraint, and the GTs' transmit power requirements. However, the formulated problem is a mixed-integer non-convex problem and is intractable to work it out with conventional optimization methods. To tackle this difficulty, we propose an efficient robust iterative algorithm to decompose the original problem be three sub-problems and acquire the suboptimal solution via utilizing the block coordinate descent (BCD) method, successive convex approximation (SCA) technique, and S-procedure. Extensive simulation results show that our proposed robust iterative algorithm offers a substantial gain in the system performance compared with the benchmark algorithms.

Machine Learning Algorithms for Temperature Management in the Anaerobic Digestion Process

Process optimization is no longer an option for processes, but an obligation to survive in the market in any industry. This argument also applies to anaerobic digestion in biogas plants. The contribution of biogas plants to renewable energy can be increased through more productive systems with less waste, which brings the common goal of minimizing costs and maximizing yields in processes. With the help of data science and predictive analytics, it is possible to take conventional process optimization and operational excellence methods, such as statistical process control and Six Sigma, to the next level. The more advanced the process optimization aspect, the more transparent and responsive the systems. In this study, seven different machine learning algorithms—linear regression, logistic regression, K-NN, decision trees, random forest, support vector machine (SVM) and XGBoost—were compared with laboratory results to define and predict the possible impacts of wide range temperature fluctuations on process stability. SVM provided the best accuracy with 0.93 according to the metric precision of the models calculated using the confusion matrix.

Multi-Condition Multi-Objective Optimization Using Deep Reinforcement Learning

A multi-condition multi-objective optimization method that can find Pareto front over a defined condition space is developed for the first time using deep reinforcement learning. Unlike the conventional methods which perform optimization at a single condition, the present method learns the correlations between conditions and optimal solutions. The exclusive capability of the developed method is examined in the solutions of a novel modified Kursawe benchmark problem and an airfoil shape optimization problem which include nonlinear characteristics which are difficult to resolve using conventional optimization methods. Pareto front with high resolution over a defined condition space is successfully determined in each problem. Compared with multiple operations of a single-condition optimization method for multiple conditions, the present multi-condition optimization method based on deep reinforcement learning shows a greatly accelerated search of Pareto front by reducing the number of required function evaluations. An analysis of aerodynamics performance of airfoils with optimally designed shapes confirms that multi-condition optimization is indispensable to avoid significant degradation of target performance for varying flow conditions.

Bio-Inspired Ant Lion Optimizer for a Constrained Petroleum Product Scheduling

Real-world optimization problems demand sophisticated algorithms. Over the years bio-inspired approach, a subset of computational intelligence has demonstrated remarkable success in real-world use cases, especially where exact or deterministic algorithms are ineffective. Petroleum product scheduling is a complex optimization task belonging to the combinatorial problem category. The problem size and the constraints compound the complexity of the petroleum product scheduling problem. However, conventional optimization methods such as the exact or deterministic algorithm produced a poor solution quality to the petroleum products scheduling problem. Therefore, this study leverages the potency of a bio-inspired approach, Ant Lion Optimizer (ALO) in its basic state to enhance the solution quality. This is in line with She-Shin Yang’s proposition, father of bio-inspired algorithms who advocated for the application of existing bio-inspired algorithms to tackle real-world problems rather than developing new algorithms. Bio-inspired is a computational paradigm that models the characteristics of natural biological entities to solve complex problems. We also used the Chaotic Particle Swarm Optimization (CPSO) algorithm for the same problem to unveil the efficacy of the roulette wheel function in ALO. The results show a 24.8% and 23.9% reduction in the original cost of distribution on ALO and CPSO respectively. Also, 99.5% of the constraints are met. Thus, problems of scarcity, minimum allocation and product availability are solved using the penalty constraint handling method. The exact algorithm showed a 14% reduction in the original cost. However, despite the effectiveness, further work on constraint handling methods and other bio-inspired computation approaches such as Genetic algorithms and their variants could be possible in the future scope. Moreover, other real-world problem domains such as power distribution in the power, sector could be a possible application of the ALO.

A Deep Learning-Based Transmission Scheme Using Reduced Feedback for D2D Networks

In this study, we investigate frequency division duplex (FDD)-based overlay device-to-device (D2D) communication networks. In overlay D2D networks, D2D communication uses a dedicated radio resource to eliminate the cross-interference with cellular communication and multiple D2D devices share the dedicated radio resource to resolve the scarcity of radio spectrum, thereby causing co-channel interference, one of the challenging problems in D2D communication networks. Various radio resource management problems for D2D communication networks can’t be solved by conventional optimization methods because they are modelled by non-convex optimization. Recently, various studies have relied on deep reinforcement learning (DRL) as an alternative method to maximize the performance of D2D communication networks overcoming co-channel interference. These studies showed that DRL-based radio resource management schemes can achieve almost optimal performance, and even outperform the state-of-art schemes based on non-convex optimization. Most of DRL-based transmission schemes inevitably require feedback information from D2D receivers to build input states, especially in FDD networks where the channel reciprocity between uplink and downlink is not valid. However, the effect of feedback overhead has not been well investigated in previous studies using DRL, and none of the studies reported on reducing the feedback overhead of DRL-based transmission schemes for FDD-based D2D networks. In this study, we propose a DRL-based transmission scheme for FDD-based D2D networks where input states are built by using reduced feedback information, thereby reducing feedback overhead. The proposed DRL-based transmission scheme using reduced feedback information achieves the same average sum-rates as that using full feedback, while reducing the feedback overhead significantly.

Joint Trajectory and Passive Beamforming Design for Intelligent Reflecting Surface-Aided UAV Communications: A Deep Reinforcement Learning Approach

In this paper, the intelligent reflecting surface (IRS)-aided unmanned aerial vehicle (UAV) communication system is studied, where the UAV is deployed to serve the user equipment (UE) with the assistance of multiple IRSs mounted on several buildings to enhance the communication quality between UAV and UE. We aim to maximize the energy efficiency of the system, including the data rate of UE and the energy consumption of UAV via jointly optimizing the UAV's trajectory and the phase shifts of reflecting elements of IRS, when the UE moves and the selection of IRSs is considered for the energy saving purpose. Since the system is complex and the environment is dynamic, it is challenging to derive low-complexity algorithms by using conventional optimization methods. To address this issue, we first propose a deep Q-network (DQN)-based algorithm by discretizing the trajectory, which has the advantage of training time. Furthermore, we propose a deep deterministic policy gradient (DDPG)-based algorithm to tackle the case with continuous trajectory for achieving better performance. The experimental results show that the proposed algorithms achieve considerable performance compared to other traditional solutions.

Intelligent Drone-Base-Station Placement for Improved Revenue in B5G/6G Systems Under Uncertain Fluctuated Demands

In this paper, the drone base-station (DBS) dispatching problem in a multi-cell B5G/6G network is investigated. The main objective is to achieve the highest system profit by serving the largest possible number of users with the least possible cost while considering the uncertain time-dependent fluctuated user’s (service) demand in the different cells, the cost of dispatched drones, and the possible profit loss due to un-served users. The problem is formulated as a profit-maximization discount return problem. Due to the uncertainty in the demand (users) in each cell, the problem cannot be solved using conventional optimization methods. Hence, the problem is reformulated as a Markov decision problem (MDP). Due to the exponential complexity of finding the solution and the unavailability of statistical knowledge about user availability (demand) in the considered regions for such optimization, we adopt a reinforcement learning (RL) approach based on the state-action-reward-state-action (SARSA) algorithm to efficiently solve the MDP. Simulation results reveal that our RL-based approach significantly increases the overall operator profit by continuously adapting its DBS dispatching strategy based on the learned users’ behavior in the network, which enables serving a larger number of users (highest revenue) with least number of DBSs (least cost).

Low-Complexity PSO-Based Resource Allocation Scheme for Cooperative Non-Linear SWIPT-Enabled NOMA

Cooperative networks integrating non-orthogonal multiple access (NOMA) and simultaneous wireless information power transfer (SWIPT) are emerging technologies that have been investigated as potential techniques to support the proliferation of the Internet of Things (IoT) and to obtain greener communications. In this sense, the optimization of resource allocation schemes is crucial to improve the performance of future cooperative wireless networks. However, conventional optimization methods that attempt to find the optimal solution may entail high computational complexity. Therefore, we propose a low-complexity particle swarm optimization (PSO)-based scheme to solve the resource allocation problem in a cooperative non-linear SWIPT-enabled NOMA system with a non-linear energy harvesting (EH) user. Specifically, we consider two optimization problems. First, we minimize transmission power, and second, we maximize energy efficiency subject to meeting quality-of-service (QoS) constraints. The problems are non-convex and challenging to solve. Furthermore, we develop the optimal solution based on convex optimization and the exhaustive search (ES) method to validate the results of the proposed PSO-based framework. Afterward, we investigate the performance of five swarm intelligence-based baseline schemes and evaluate an additional low-complexity solution based on the cuckoo search (CS) technique. For comparison purposes, we use orthogonal multiple access (OMA), equal power splitting (EPS), and time fixed (TF) baseline schemes. To our satisfaction, the proposed SWIPT NOMA network outperforms the benchmark schemes, and the proposed PSO-based framework achieves the nearest performance to the optimal scheme with lower complexity than obtained by the comparative swarm intelligence techniques and from convex optimization with the ES method.

Noise Pollution Reduction through a Novel Optimization Procedure in Passive Control Methods

This paper proposes a novel optimization framework in passive control techniques to reduce noise pollution. The geometries of the structures are represented by Catmull-Clark subdivision surfaces, which are able to build gap-free Computer-Aided Design models and meanwhile tackle the extraordinary points that are commonly encountered in geometric modelling. The acoustic fields are simulated using the isogeometric boundary element method, and a density-based topology optimization is conducted to optimize distribution of sound-absorbing materials adhered to structural surfaces. The approach enables one to perform acoustic optimization from Computer-Aided Design models directly without needing meshing and volume parameterization, thereby avoiding the geometric errors and time-consuming preprocessing steps in conventional simulation and optimization methods. The effectiveness of the present method is demonstrated by three dimensional numerical examples.

Auto-Tuning Controller Using MLPSO With K-Means Clustering and Adaptive Learning Strategy for PMSM Drives

This paper proposes a new online auto-tuning method to improve the accuracy and reduce the tuning time of permanent magnet synchronous motor (PMSM) drives. Under varying loads, the ability to tune the controllers of PMSM drives using optimal tuning time is crucial. However, direct tuning of controller parameters using estimated parameters or conventional particle swarm optimization (PSO) methods do not satisfy the performance criteria. To solve this problem, the new method combining mechanical parameter estimation (MPE) and multi-layer particle swarm optimization (MLPSO) with K-means clustering (KMC) and an adaptive learning strategy (ALS) is proposed. First, the combination of an MPE method with a lookup table (LUT) for initial parameter selection is introduced to reduce the iteration time. Then, the MLPSO-KMCALS method is proposed as an improvement over the conventional PSO method by increasing the number of layers, grouping the swarm into several subswarms, and using the ALS for each particle to increase the population diversity and optimize the controller parameters within the shortest possible amount of time. Finally, a disturbance load torque observer is applied to compensate for the effect of external disturbances after tuning. The effectiveness of the proposed method is validated through experiments conducted under practical conditions.