On-line Multi-objective Optimization Research Articles

Online Lifecycle Operating Costs Minimization Strategy for Fuel Cell Buses Considering Power Sources Degradation

The high operating costs limit the popularity and development of fuel cell buses (FCBs). To enhance the lifecycle operating economy of FCBs, this article analyzes the impact of power sources degradation on operating costs and proposes an online operating costs minimization strategy, which integrates online state of health (SOH) estimation, online multiobjective optimization, and online adaptive adjustment into the control strategy framework. Specifically, the SOH of the power sources is estimated by cycle life models in real time. The hydrogen consumption, battery energy consumption, and power sources degradation are incorporated into the objective function and adjusted in real time according to the SOH. Under different SOH, the simulation results indicate that compared with the equivalent consumption minimization strategy, the proposed strategy can reduce fuel cell and battery degradation by 13.8% and 5%, and decrease operating costs by 5.3%. Moreover, the adaptability of the control strategy is enhanced throughout the full lifecycle.

Online multi-objective optimization for tunnel boring machine segment assembly considering stress concentration

To address stress concentration issues during the assembly of tunnel boring machine (TBM) segments, particularly in the context of synchronous excavation and segment assembly (SESA) TBMs, this paper presents an optimization framework focused on hydraulic cylinder pressure redistribution. The paper develops a Bayesian optimization extreme gradient boosting (BO-XGBoost) model for cylinder status detection and devises an online Archimedean optimization algorithm (AOA) for cylinder pressure redistribution. Verification of the proposed approach's effectiveness is conducted using a SESA TBM experimental bench. The results indicate that: 1) The proposed hydraulic cylinder status detection model effectively distinguishes various cylinder operating statuses, achieving a high F1-score of 0.9891. 2) Across the six segment assembly scenarios, the stress concentration phenomenon can be improved by 16.03%, 14.29%, 8.84%, 31.10%, 15.47%, and 11.11%, respectively. 3) In comparison to the offline optimization algorithm, the online algorithm reduces the maximum pressure by 36.69% and decreases algorithmic error by 30.50%. A key contribution from this paper is the introduction of an active pressure control approach integrated with the online multi-objective optimization algorithm, which is helpful in effectively improving the stress concentration phenomenon in the SESA TBM.

Strategical district cooling system operation with accurate spatiotemporal consumption modeling

As a large integrated energy system, district cooling system (DCS) is widely proposed for its favorable comprehensive performance. However, due to the high complexity of DCS structure and comprehensive influential variables on cooling demand, to propose an effective operational strategy that not only enhances energy efficiency but also ensures indoor thermal comfort is quite challenging. This research proposes a novel and online multi-objective optimization strategy for DCS operations, which minimizes both energy consumption and thermal discomfort, to tackle the aforementioned challenge. The proposed strategy incorporates accurate spatiotemporal cooling demand modeling: (1) On the one hand, cooling load models of both cooling dissipations during transport networks and cooling end consumers considering comprehensive influence of outdoor meteorological parameters are described; (2) On the other hand, all power consumption components in the DCS including chiller, pump, fan coil, fan of cooling tower, are built and connected with system cooling demand. Numerical experiments based on a real-world DCS are conducted to validate the proposed strategy. The results show that by adopting the proposed strategy, energy consumption cost and thermal comfort can be effectively optimized and can save an extra 5% of energy consumption comparing with published strategies.

Multiobjective Bayesian optimization for online accelerator tuning

Particle accelerators require constant tuning during operation to meet beam quality, total charge and particle energy requirements for use in a wide variety of physics, chemistry and biology experiments. Maximizing the performance of an accelerator facility often necessitates multiobjective optimization, where operators must balance trade-offs between multiple competing objectives simultaneously, often using limited, temporally expensive beam observations. Usually, accelerator optimization problems are solved off-line, prior to actual operation, with advanced beam line simulations and parallelized optimization methods (NSGA-II, swarm optimization). Unfortunately, it is not feasible to use these methods for online multiobjective optimization, since beam measurements can only be done in a serial fashion, and these optimization methods require a large number of measurements to converge to a useful solution. Here, we introduce a multiobjective Bayesian optimization scheme, which finds the full Pareto front of an accelerator optimization problem efficiently in a serialized manner and is thus a critical step towards practical online multiobjective optimization in accelerators. This method uses a set of Gaussian process surrogate models, along with a multiobjective acquisition function, to reduce the number of observations needed to converge by at least an order of magnitude over current methods. We demonstrate how this method can be modified to specifically solve optimization challenges posed by the tuning of accelerators. This includes the addition of optimization constraints, objective preferences and costs related to changing accelerator parameters.

Adaptive Controller Tuning Method Based on Online Multiobjective Optimization: A Case Study of the Four-Bar Mechanism.

The efficient speed regulation of four-bar mechanisms is required for many industrial processes. These mechanisms are hard to control due to the highly nonlinear behavior and the presence of uncertainties or disturbances. In this paper, different Pareto-front approximation search approaches in the adaptive controller tuning based on online multiobjective metaheuristic optimization are studied through their application in the four-bar mechanism speed regulation problem. Dominance-based, decomposition-based, metric-driven, and hybrid search approaches included in the algorithms, such as nondominated sorting genetic algorithm II, multiobjective evolutionary algorithm based on decomposition and differential evolution, S-metric selection evolutionary multiobjective algorithm, and nondominated sorting genetic algorithm III, respectively, are considered in this paper. Also, a proposed metric-driven algorithm based on the differential evolution and the hypervolume indicator (HV-MODE) is incorporated into the analysis. The comparative descriptive and nonparametric statistical evidence presented in this paper shows the effectiveness of the adaptive controller tuning based on online multiobjective metaheuristic optimization and reveals the advantages of the metric-driven search approach.

Indirect adaptive control using the novel online hypervolume-based differential evolution for the four-bar mechanism

Four-bar mechanisms have increased their use in current applications from industrial to rehabilitation systems. These applications become more demanding over time, and the control systems are required to provide them higher accuracy, lower energy consumption, and an extended lifetime, among other conflicting features. In addition to the previously mentioned demands, four-bar mechanisms have highly nonlinear dynamics and are often subject to external loads that make them difficult to control. In this paper, an indirect adaptive control based on online multi-objective optimization is proposed to regulate the speed of the four-bar mechanism and increase its lifetime by smoothing the control action under the effects of uncertainties. This consists of a multi-objective optimization process for the online identification of the model parameters that fulfill the performance demands of the mechanism. In this process, a multi-objective optimization problem is stated and then solved by the novel Online Hypervolume-based Differential Evolution (O-HV-MODE) in such a way that several promising model parameter configurations are found in real-time, with different trade-offs among the performance demands. O-HV-MODE takes advantage of the past problem knowledge to accelerate the search for new solutions and uses the Hypervolume metric to increase their convergence and diversity. Then, a single model parameter configuration is selected based on the application necessities and is further used in the nonlinear compensator of the computed-torque controller, while a fixed-gain PD control loop is used for stabilization. The proposed control is validated through experimental tests and the reliability of the results with the 99% Confidence Interval test. Also, the proposal is compared with state-of-the-art linear and non-linear control approaches.

Model-based multi-objective predictive scheduling and real-time optimal control of energy systems in zero/low energy buildings using a game theory approach

Online optimal control of energy systems plays a significant role in achieving the zero/low energy goal and high energy efficiency for zero/low energy buildings. Increasing amount of studies are conducted on the multi-objective optimal control of building energy systems in recent years since multiple objectives are often concerned in practice. The weighted sum method is widely used to solve the multi-objective optimization problems for online optimal controls. However, it is often difficult and even impractical to determine proper weights for objectives of different natures. Existing studies on online optimal control of energy systems in zero/low energy buildings focused on the predictive scheduling, and very few studies addressed the real-time optimal control aspect which is also essential in operation. In this study, a coordinated online multi-objective control strategy, consisting of two control optimization schemes, is therefore proposed for predictive scheduling and real-time optimal control of energy systems in zero/low energy buildings. A cooperative game theory-based method is adopted for the online multi-objective optimizations. The control strategy was tested and evaluated via simulation of the energy systems in a zero energy building with battery storage on two typical days. Control variables and weights of objectives were optimized to minimize the combined optimization objective involving energy cost and grid impact. The test results show that it is essential and beneficial to coordinate the predictive scheduling and real-time optimal control in actual operation. The cooperative game theory-based method is effective for the online multi-objective optimization without the need of setting weights of different objectives.

Multi-objective optimal predictive control of signals in urban traffic network

Traffic congestion in urban network has been a serious problem for decades. In this paper, a novel dynamic multi-objective optimization method for designing predictive controls of network signals is proposed. The popular cell transmission model (CTM) is used for traffic prediction. Two network models are considered, i.e., simple network which captures basic macroscopic traffic characteristics and advanced network that further considers vehicle turning and different traveling routes between origins and destinations. A network signal predictive control algorithm is developed for online multi-objective optimization. A variety of objectives are considered such as system throughput, vehicle delay, intersection crossing volume, and spillbacks. The genetic algorithm (GA) is applied to solve the optimization problem. Three example networks with different complexities are studied. It is observed that the optimal traffic performance can be achieved by the dynamic control in different situations. The influence of the objective selection on short-term and long-term network benefits is studied. With the help of parallel computing, the proposed method can be implemented in real time and is promising to improve the performance of real traffic network.

Flexible online multi-objective optimization framework for ISA100.11a standard in beacon-enabled CSMA/CA mode

Maintaining high quality of service (QoS) for sensor nodes is important in industrial wireless sensor networks (IWSNs). Wide range of factors severely affects QoS; however more research is still needed. In this article, a flexible online multi-objective optimization framework (FOMOF) in carrier sense multiple access with collision avoidance (CSMA/CA) mode is proposed. Nodes are predominantly grouped based on their QoS requirements and then optimized in online mode using a constrained genetic based multi-objective approach. FOMOF to maintain a minimum desired QoS of the pre-determined groups with maximum number of nodes. Furthermore, the packet delivery ratio (PDR) obtained in FOMOF was higher than the one obtained by ISA100.11a standard. Accordingly, the grouping strategy enhanced the number of nodes by 12.5% with End-to-End delay reduction of 16.36% for 27 nodes.

A new approach for online multiobjective optimization of mechatronic systems

We present a new concept for online multiobjective optimization and its application to the optimization of the operating point assignment for a doubly-fed linear motor. This problem leads to a time-dependent multiobjective optimization problem. In contrast to classical optimization where the aim is to find the (global) minimum of a single function, we want to simultaneously minimize k objective functions. The solution to this problem is given by the set of optimal compromises, the so-called Pareto set. In the case of the linear motor, there are two conflicting aims which both have to be maximized: the degree of efficiency and the inverter utilization factor. The objective functions depend on velocity, force and power, which can be modeled as time-dependent parameters. For a fixed point of time, the entire corresponding Pareto set can be computed by means of a recently developed set-oriented numerical method. An online computation of the time-dependent Pareto sets is not possible, because the computation itself is too complex. Therefore, we combine the computation of the Pareto set with numerical path following techniques. Under certain smoothness assumptions the set of Pareto points can be characterized as the set of zeros of a certain function. Here, path following allows to track the evolution of a given solution point through time.

An interactive on-line multi-objective optimization approach with application to metal cutting turning operation

An on-line supervisory computer control model for optimization of multiple objective programming problems is developed and applied to machining problems. Specifically, the turning operation is formulated and solved by the approach. For the first time, the characteristics of on-line multi-objective problems are described and an approach for solving such problems is developed. The proposed system maintains feasibility of operations and optimizes objectives based on the machine performance and on-line interactions with the decision-maker (DM). The model is hierarchical and consists of two levets: level 1 is designed to protect automated machinery and the workpiece by monitoring the process outputs; and level 2 is designed to optimize the on-line multi-objective problem. The tasks of both levels are accomplished by on-line monitoring and control of changes in process outputs via real-time information provided by sensors. The recommended parameters, obtainable from the multi-objective optimization approach, ar...