Multi-objective optimization of TBM-induced building settlement control considering physical constraints

Generally, the goal of reliability allocation is to maximize product reliability under various physical or budgetary constraints, and to minimize cost subject to reliability or physical constraints. For products sold with warranty, the product reliability affects not only the manufacturing cost but also the warranty servicing cost. Thus the reliability allocation decision can be regarded as a multi-objective optimization problem and must consider these costs for products under warranty. This paper develops a general multi-objective model considering above factors. To obtain the optimal reliability allocation for engineering systems, the multi-objective evolutionary algorithm NSGA-II is adopted to obtain a Pareto optimal solution set. Then the final representative solutions for the reliability allocation problem can be chosen from the Pareto solution set. Finally, a numerical example is given to illustrate the proposed method.

Integration between constrained optimization and deep networks has garnered significant interest from both research and industrial laboratories. Optimization techniques can be employed to optimize the choice of network structure based not only on loss and accuracy but also on physical constraints. Additionally, constraints can be imposed during training to enhance the performance of networks in specific contexts. This study surveys the literature on the integration of constrained optimization with deep networks. Specifically, we examine the integration of hyper-parameter tuning with physical constraints, such as the number of FLOPS (FLoating point Operations Per Second), a measure of computational capacity, latency, and other factors. This study also considers the use of context-specific knowledge constraints to improve network performance. We discuss the integration of constraints in neural architecture search (NAS), considering the problem as both a multi-objective optimization (MOO) challenge and through the imposition of penalties in the loss function. Furthermore, we explore various approaches that integrate logic with deep neural networks (DNNs). In particular, we examine logic-neural integration through constrained optimization applied during the training of NNs and the use of semantic loss, which employs the probabilistic output of the networks to enforce constraints on the output.

Optimal TMD design for floating offshore wind turbines considering model uncertainties and physical constraints

Thrust allocation (TA) plays a critical role in the dynamic positioning system (DPS). The task of TA is to allocate the rotational speed and angle of each thruster to generate the generalized control forces. Most studies take TA as a single-objective optimization problem; however, TA is a multi-objective optimization problem (MOP), which needs to satisfy multiple conflicting allocation objectives simultaneously. This study proposes an improved multi-objective particle swarm optimization (IMOPSO) method to deal with the non-convex MOP of TA. The objective functions of reducing the allocation error, and minimizing the power consumption and the tear-and-wear of thrusters under physical constraints, are established and solved via MOPSO. To enhance the global seeking ability, the improved mutation strategy combined with the roulette wheel mechanism is adopted. It is shown through test data that IMOPSO converges better than multi-objective algorithms such as MOPSO and nondominated sorting genetic algorithm II (NSGA-II). Simulations are conducted for a DP ship with two propeller–rudder combinations. The simulation results with the single-objective PSO algorithm show that the proposed IMOPSO algorithm reduces thrust allocation errors in the three directions of surge, sway, and yaw by 48.48%, 39.64%, and 15.02%, respectively, and reduces power consumption by 44.53%, which demonstrates the feasibility and effectiveness of the proposed method.

The application of multiple UAVs in complicated tasks has been widely explored in recent years. Due to the advantages of flexibility, cheapness and consistence, the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV. Accordingly, several constraints should be satisfied to realize the efficient cooperation, such as special time-window, variant equipment, specified execution sequence. Hence, a proper task allocation in UAVs is the crucial point for the final success. The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics. To this end, a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints. In addition, four optimization objectives: completion time, target reward, UAV damage, and total range, are introduced to evaluate various allocation plans. Subsequently, to efficiently solve the multi-objective optimization problem, an improved multi-objective quantum-behaved particle swarm optimization (IMOQPSO) algorithm is proposed. During this algorithm, a modified solution evaluation method is designed to guide algorithmic evolution; both the convergence and distribution of particles are considered comprehensively; and boundary solutions which may produce some special allocation plans are preserved. Moreover, adaptive parameter control and mixed update mechanism are also introduced in this algorithm. Finally, both the proposed model and algorithm are verified by simulation experiments.

Writing-in-the-air (WIA) system provides a novel input experience using the fingertip as a virtual pen based on the color and depth information from only one Kinect camera. We present a new fingertip detection and tracking framework for the robust and realtime fingertip position estimation and further improve the air-writing character recognition accuracy. Firstly, we propose a new physical constraint and an adaptive threshold with the mode temporal consistency in order to classify various hand poses into two modes, i.e., the side-mode and frontal-mode. In the side-mode, a new choose-to-trust algorithm (CTTA) is proposed for the hand segmentation. The final segmentation result is generated by selecting a more trustable color or depth model-based segmentation result according to the fingertip-palm relationship. In the frontal-mode, we propose to estimate the fingertip position by a joint detection-tracking algorithm that successfully incorporates the temporal and physical constraints. By using three new features defined by the joint detection-tracking algorithm, the fingertip position is determined by a multi-objective optimization strategy. We have collected two large fingertip writing data set with different difficulties. According to our experiments in both data sets, our proposed framework has the best accuracy on the fingertip position estimation by comparing with four popular methods. More importantly, the final character recognition rate increases significantly and reaches 100% in the first five candidates for all types of characters.

A regenerative gas turbine cycle with two particular tubular recuperative heat exchangers in parallel is considered for multi-objective optimization. It is assumed that tubular recuperative heat exchangers and its corresponding gas cycle are in design stage simultaneously. Three objective functions including the purchased equipment cost of recuperators, the unit cost rate of the generated power, and the exergetic efficiency of the gas cycle are considered simultaneously. Geometric specifications of the recuperator including tube length, tube outside/inside diameters, tube pitch, inside shell diameter, outer and inner tube limits of the tube bundle and the total number of disc and doughnut baffles, and main operating parameters of the gas cycle including the compressor pressure ratio, exhaust temperature of the combustion chamber and the air mass flowrate are considered as decision variables. Combination of these objectives anddecision variables with suitable engineering and physical constraints (including NO x and CO emission limitations) comprises a set of mixed integer non-linear problems. Optimization programming in MATLAB is performed using one of the most powerful and robust multi-objective optimization algorithms, namely non-dominated sorting genetic algorithm. This approach is applied to find a set of Pareto optimal solutions. Pareto optimal frontier is obtained, and a final optimal solution is selected in a decision-making process.

Integration of multi-objective reliability-based design optimization into thermal energy management: Application on phase change material-based heat sinks

Design and optimization of a non-TEMA type tubular recuperative heat exchanger used in a regenerative gas turbine cycle

This paper attempts to investigate the multi-objective optimization (MOO) of accommodating distributed generation (DG) based on a modified multi-objective particle swarm optimization algorithm (MMPSO). In view of the significance of economic operation, power quality and system stability, MOO model with physical, technical, and operational constraints for optimally accommodating DG has been established to minimize the total active power loss, the total voltage deviation, and the voltage stability index of the system. Due to the incompatibility between the objectives and the need for flexibility of solutions, instead of converting the multiple objectives into a single one, a MMPSO algorithm has been presented and testified, and then applied to MOO of accommodating DG in an IEEE-33 bus system. Simulation results reveal the advantage of the proposed MOO model and the effectiveness of applying MMPSO to MOO of accommodating DG in the distribution system.

Pareto design of Load Frequency Control for interconnected power systems based on multi-objective uniform diversity genetic algorithm (MUGA)

COLREGs-Compliant Path Planning for Autonomous Surface Vehicles: A Multiobjective Optimization Approach

With the rise of more electric and all-electric aviation power systems, engineering efforts of system optimization shift to the electrical domain. A substantial amount of time and resources are dedicated to finding the best system architecture and design specifications to meet energy efficiency goals and physical constraints. Current processes utilize models of power system components to determine the optimal designs. However, such modeling is computationally expensive as numerous iterations are required to settle on an optimal design. This paper proposes a machine learning (ML) enabled constrained multi-objective optimization solver to drastically reduce the amount of design iterations required for Pareto set discovery for power systems. The process contributes significantly to design automation. A heavy-duty vertical-takeoff-landing (VTOL) unmanned aerial vehicle (UAV) power system is selected to demonstrate the efficacy and limitation of ML enabled optimization. Two extreme trials were run: 1) a search throughout the entire design space with only 9% valid designs within constraints; 2) a search throughout the valid design space. While Trial 1 was unsuccessful in discovering the Pareto front, Trial 2 uncovered all Pareto optimal designs with a 99% reduction of iterations compared to a brute force method.

Multiobjective Optimization in the Water Resources Problems of Kakogawa River Basin

PurposeThis study explores the potential of computational design processes in creating contextually responsive envelopes for high-rise residential buildings in the Middle East. This includes considering both physical constraints and social preferences, with a focus on balancing sunlight exposure, privacy and views.Design/methodology/approachA two-phase simulation study analyzed various exterior envelope systems in Baghdad high-rise buildings. The first phase examined two commonly used exterior envelopes – fully glazed and window-based – to assess sunlight exposure, privacy and views. In the second phase, a multi-objective optimization process was applied to derive contextually optimized design solutions addressing the challenges identified in the first phase.FindingsThe study reveals that contextually optimized design solutions significantly improved direct sunlight exposure and privacy while maintaining satisfactory views. Although fully glazed exterior envelopes provided better-uninterrupted views, the optimized solutions offered more balanced performance across all factors, demonstrating the potential of computational design processes in creating contextually responsive building envelopes.Originality/valueThis paper emphasizes the importance of considering both physical and social contexts in the development of algorithms for architecture in the Middle East. This paper supports a progressive interpretation of traditional building references and demonstrates how computational design processes can create contextually responsive building envelopes that satisfy social needs and provide better-performing buildings for inhabitants.