Multi-objective Optimization Model Research Articles

Clustering-based multi-objective optimization considering fairness for multi-workflow scheduling on clouds

Distributed computing, such as cloud computing, provides promising platforms for orchestrating scientific workflows' tasks based on their sequences and dependencies. Workflow scheduling plays an important role in optimizing concerned objectives for distributed computing, such as minimizing the makespan and cost. Many researchers have focused on optimizing a specific single workflow with multiple objectives. Currently, there are few studies on multi-workflow scheduling, with most research focusing on objectives such as cost and makespan. However, multi-workflow scheduling requires the design of specific objectives that reflect the unique characteristics of multiple workflows. On the other hand, clustering-based approaches have garnered significant attention in the field of workflow scheduling over distributed computing resources due to their advantage in reducing data communication among tasks. Despite this, the effectiveness of clustering-based algorithms has not been extensively studied and validated in the context of multi-objective multi-workflow scheduling models. Motivated by these factors, we propose an approach for multiple workflows' multi-objective optimization (MOO), considering the new defined metric, fairness. We first mathematically formulate the fairness and define a fairness-involved MOO model. Then, we propose an advanced clustering-based resource optimization strategy in multiple workflow runs. Experimental results show that the proposed approach performs better than the compared algorithms without significant compromise of the overall makespan and cost as well as individual fairness, which can guide the simulation workflow scheduling on clouds.

System simulation and multi-objective optimization methodology for sustainable municipal solid waste classification management: A case study in China

Cities worldwide are actively implementing waste classification policies to develop efficient waste management systems that address the growing challenges of waste disposal and promote sustainable development. However, selecting tailored waste classification solutions is challenging due to the intricate interrelationships within the system and the complex trade-offs between different management objectives. This study presents a comprehensive methodology that simulates the entire waste classification management system by integrating the stages of waste generation, source classification, and final disposal, all linked by waste quantities and properties. A multi-objective optimization model is employed to identify optimal technical pathways that balance economic, environmental, and energy objectives. This methodology was applied in Zhangjiagang, a medium-sized city in China, revealing that economically optimal solutions focusing on landfill and composting as disposal pathways could exacerbate environmental impacts, with indicators deteriorating by 23.1 %–419.1 % except for freshwater eutrophication potential. In contrast, a multi-objective optimization solution prioritizing incineration and anaerobic digestion achieves environmental and energy benefits valued at 18.7 USD/t, with a modest increase in economic costs of 5.7 USD/t compared to the base year. The unconstrained optimal scenarios achieve net negative environmental costs and recover 2.0 × 106 MJ of energy. The study recommends moderate and dynamically adjusted kitchen waste separation rates depending on the stage of implementation. Additionally, the findings reveal that the “lock-in” effects of mismatched disposal facilities can significantly escalate costs. In conclusion, this study underscores the importance of systematic design and multi-objective optimization, offering a comprehensive methodology for cities aiming to enhance their waste classification strategies.

Novel machine learning-driven multi-objective optimization method for EDM trajectory planning of distorted closed surfaces

Highly distorted closed surfaces pose significant challenges for machining trajectory planning due to their intricate surface constraints and closed structures. Despite these challenges, components with such features are prevalent in industries like aerospace. This paper presents a machine learning-driven multi-objective optimization method for electrical discharge machining (EDM) trajectory planning of highly distorted closed surfaces. The method transforms the structural design of forming electrodes and trajectory planning into a multi-objective decision problem. And a discrete point trajectory planning method, guided by surface average curvature, is employed to determine the optimal position and orientation of the electrode. Additionally, an elite dataset, generated using the Monte Carlo method and Arena's Principle, is utilized to train an artificial neural network (ANN). This network predicts hyperparameters for the nonlinear optimization problem. Based on the proposed method, a multi-objective optimization model is formulated for an integral shrouded blisk, considering minimization of iteration count, axial motion, and maximization of machining surface quality. The Pareto front is utilized to obtain the optimal EDM trajectory. Experimental results demonstrate a 17.38 % reduction in the overall machining cycle duration using this trajectory, and the surface roughness and profile accuracy satisfy the design specifications, which proves the effectiveness of this method.

Data-driven multi-objective intelligent optimal control of municipal solid waste incineration process

The municipal solid waste incineration (MSWI) process has become the primary technology for municipal solid waste (MSW) treatment worldwide due to its advantages of harmlessness, reduction, and resource recovery. However, the automatic combustion control (ACC) system developed in developed countries are often not effectively applicable to other countries, particularly China, due to regional differences in MSW composition. To achieve intelligent development of MSWI plants, it is crucial to develop intelligent optimal control technology tailored to national conditions to reduce comprehensive pollutant emission concentration (CPEC) and improve combustion efficiency (CE). This article proposes a data-driven multi-objective intelligent optimal control strategy. Firstly, based on the analysis of the whole process, the Tikhonov regularization-least regression decision tree (TR-LRDT) algorithm is used to establish a MSWI whole process model, incorporating serial controlled objects and parallel pollutant indicators, to support a multi-objective optimization model. Then, leveraging the ACC system and expert experience, a multi-input multi-output loop controller is established using the single neuron adaptive PID (SNA-PID) algorithm to achieve stable control of key controlled variables. Next, a mutation crossover strategy and termination condition are integrated with the multi-objective particle swarm optimization (MOPSO) algorithm to solve the multi-objective optimization model, and domain expert rules are applied to determine the optimal setpoints for key controlled variables in the Pareto front, aiming to reduce CPEC and improve CE. Finally, the effectiveness of the proposed intelligent optimal control strategy is validated using actual data. The experimental results demonstrate that this strategy not only maintains the stability of key controlled variables but also decreases CPEC by 1.82% and increases CE by 2.38%, laying a foundation for further research into intelligent optimization control of the MSWI process. Further, the software system based on the proposed strategy is developed and validated on a hardware-in-loop simulation platform.

Revolutionizing RPAS logistics and reducing CO2 emissions with advanced RPAS technology for delivery systems

To manage remotely piloted aircraft system (RPAS) networks effectively, this research presents a multi-objective location-routing optimization model. This model integrates time window constraints, concurrent pick-up and delivery demands, and rechargeable battery functionality, and also introduces a standardized framework to clarify the RPAS CO2 emission model. These integrations significantly decrease battery consumption in Remotely Piloted Aircraft Systems (RPAS) and lower transportation costs, while also optimizing delivery times, reducing operational risks, and minimizing CO2 emissions. The model’s enhancement for optimizing delivery schedules takes into account uncertain traffic conditions, thus improving accuracy in dynamic environments and further contributing to environmental sustainability. Risk assessment employs the Specific Operations Risk Assessment (SORA) standard, adding a third objective function. This combination of the model, further enhance the efficiency and sustainability of RPAS operations, by optimizing delivery schedules, reducing CO2 emissions and battery consumption, and improving accuracy under dynamic conditions. Also, it make RPAS logistics more practical and effective in real-world applications. As result, the NSGA-II algorithm achieves significant reductions across all objectives: 33.3 % in cost, 6.48 % in time, 33.3 % in risk, and 35.7 % in battery usage within 250 generations. The use of the NSGA-II meta-heuristic method for validation enhances the credibility and practicality of the model. The optimization model’s performance over 250 generations shows rapid initial improvements in cost, time, risk, and battery usage, followed by stabilization, indicating efficient convergence and effective evolutionary computation. Also the findings show that with a CO2 emission rate of 3.773 × 104 kg of CO2 per Wh, highlighting the model’s efficiency and effectiveness.

A multi-objective stochastic optimization model for combined heat and power virtual power plant considering carbon recycling and utilizing

In order to give full play to the energy supply potential of distributed energy resources, this paper studies the scheduling optimization of CHP-VPP. First, the CHP unit and various distributed energy sources are aggregated into VPP. Carbon recycling and utilizing are realized through carbon capture and power-to-gas devices. At the same time, carbon storage and hydrogen storage devices are added to decouple carbon capture and P2G procedures. Then, the risk of VPP real-time scheduling is quantified through uncertainty scenario generation and CVaR. Finally, with the goals of operating cost, carbon emission, and operation risk, a multi-objective stochastic scheduling optimization model of VPP is constructed, and the subjective and objective ensemble weighting method is used to solve the problem. The example results show that the proposed method can boost the wastage of wind and photovoltaic power, and also lower the carbon emissions of VPPs.

Design and Optimization of a Cable-Driven Parallel Polishing Robot With Kinematic Error Modeling

Abstract This article presents the design and optimization of a cable-driven parallel polishing robot (CDPPR) with kinematic error modeling and introduces an improved nondominated sorting genetic algorithm II (NSGA-II) for multiobjective optimization. First, the mechanical design and kinematic and static modeling of the CDPPR are conducted. Subsequently, a kinematic error transfer model is established based on the evidence theory by considering the change of exit points of cables, and an error index is derived to measure the accuracy of the robot. Besides, another two performance indices including the workspace and static stiffness are proposed. Thus, a multiobjective optimization model is established to optimize the workspace, static stiffness, and error index, and an improved NSGA-II is developed. Finally, an experimental scaled prototype of the CDPPR is constructed, and numerical examples and experimental results demonstrate the effectiveness of the improved NSGA-II and the stability of the optimal configuration.

Deep learning and optimization enabled multi-objective for task scheduling in cloud computing

ABSTRACT In cloud computing (CC), task scheduling allocates the task to best suitable resource for execution. This article proposes a model for task scheduling utilizing the multi-objective optimization and deep learning (DL) model. Initially, the multi-objective task scheduling is carried out by the incoming user utilizing the proposed hybrid fractional flamingo beetle optimization (FFBO) which is formed by integrating dung beetle optimization (DBO), flamingo search algorithm (FSA) and fractional calculus (FC). Here, the fitness function depends on reliability, cost, predicted energy, and makespan, the predicted energy is forecasted by a deep residual network (DRN). Thereafter, task scheduling is accomplished based on DL using the proposed deep feedforward neural network fused long short-term memory (DFNN-LSTM), which is the combination of DFNN and LSTM. Moreover, when scheduling the workflow, the task parameters and the virtual machine’s (VM) live parameters are taken into consideration. Task parameters are earliest finish time (EFT), earliest start time (EST), task length, task priority, and actual task running time, whereas VM parameters include memory utilization, bandwidth utilization, capacity, and central processing unit (CPU). The proposed model DFNN-LSTM+FFBO has achieved superior makespan, energy, and resource utilization of 0.188, 0.950J, and 0.238, respectively.

Study on Multi-Objective Optimization of Construction Project Based on Improved Genetic Algorithm and Particle Swarm Optimization

Construction projects require concurrent consideration of the three major objectives of construction period, cost, and quality. To address the multi-objective optimization issues of construction projects, mathematical models of construction period, quality, and cost are established, respectively, and multi-objective optimization models are constructed for different construction objectives. A hybrid optimization method combining an improved genetic algorithm (GA) with a time-varying mutation rate and a particle swarm algorithm (PSO) is proposed to optimize construction projects, which overcomes the shortcomings of the original GA and improves the global optimality and stability of results. Various construction projects were considered, and different construction objectives were analyzed individually. Finally, an uncertainty analysis is developed for the proposed GA-PSO algorithm and compared with GA and PSO. The results indicate that the proposed hybrid approach outperforms the PSO and GA algorithms in providing a better and more stable multi-objective optimized construction solution, with performance improvements of 4.3–8.5% and volatility reductions of 37.5–64.4%. This provides a reference for the optimal design of wind farms, buildings, and other construction projects.

A Novel Multi-Objective Dynamic Reliability Optimization Approach for a Planetary Gear Transmission Mechanism

Planetary gear transmission mechanisms (PGTMs) are widely used in mechanical transmission systems due to their compact structure and high transmission efficiency. To implement the reliability design and optimization of a PGTM, a novel multi-objective dynamic reliability optimization approach is proposed. First, a multi-objective reliability optimization model is established. Furthermore, considering the strength degradation of gears during service, a dynamic reliability analysis is conducted based on the theory of nonlinear fatigue damage accumulation. In addition, to improve computing efficiency, a random forest surrogate model based on the particle swarm optimization algorithm is proposed. Finally, an adaptive multi-objective evolutionary algorithm based on decomposition (AMOEA/D) is designed to optimize the mechanism, along with an adaptive neighborhood updating strategy and a hybrid crossover operator. The feasibility and superiority of the proposed approach are verified through an NGW planetary gear reducer. The results show that the proposed surrogate model can reduce the calculation cost and has high accuracy. The AMOEA/D algorithm can improve transmission efficiency, reduce gear volume and ensure reliability at the same time. It can provide guidance for actual gear production.

Integrated multi-objective optimization of rough and finish cutting parameters in plane milling for sustainable machining considering efficiency, energy, and quality

Plane milling is one of the most prevalent methods in metalworking, recognized for its potential to uncover energy-saving opportunities by optimizing cutting parameters. However, existing research on cutting parameters optimization primarily adopts a phased independent optimization approach, with some studies focusing on rough milling parameters and others on finish milling parameters under predetermined allowances. These research methods lack integrated optimization for both rough and finish milling parameters, leading to suboptimal outcomes. To address this issue, this paper proposes an integrated multi-objective optimization method for both rough and finish cutting parameters in plane milling. The aim is to achieve sustainable machining by considering efficiency, energy consumption, and quality. Specifically, the research accomplishes three key objectives: (1) developing detailed models for the efficiency, energy, and quality of plane milling; (2) presenting an integrated multi-objective optimization model and approach for determining rough and finish cutting parameters in plane milling; (3) conducting a specific case study to validate the effectiveness and feasibility of the proposed model and approach. Experimental findings demonstrated that optimizing cutting parameters in the plane milling process through an integrated approach can reduce processing time by 19.37%, decrease energy consumption by 16.88%, and significantly improve the surface roughness of the workpiece by 27.07% compared to traditional empirical methods. Furthermore, compared to independent optimization schemes, processing time is reduced by 2.99%, energy consumption is lowered by 4.72%, and surface roughness is improved by 13.16%.

Virtual reality-based site layout planning for building design

This paper addresses the challenge of integrating optimized design solutions in Site Layout Planning (SLP) through Virtual Reality (VR), questioning how VR simulations can enhance their acceptance. The methodology involves a multi-objective optimization model that evaluates critical factors like earthwork volume, cost, and environmental impact, integrated into a VR framework for interactive participant evaluations. Results show a notable 48.3% increase in decision-making accuracy among participants using VR, highlighting VR's potential to significantly improve comprehension and application of complex data-driven designs in SLP. This underlines the transformative impact of VR on enhancing stakeholder engagement and optimizing design outcomes. Future research will broaden the participant base and further investigate the long-term effects of VR integration in professional environments.

Prediction and compensation of stretch-bending springback for L-section profile

This study aimed to achieve one-time stretch-bending forming in an L-shaped profile. Consequently, it focused on two main aspects: springback compensation and process parameter optimization. The process first combined simulation and experimental data, through a Co-Kriging model, facilitating the construction of an approximate model of a profile’s stretch-bending forming. A multi-objective optimization model was then formulated, to minimize springback displacement, deformation, and residual stress. The study employed the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to explore the solution space for optimal combinations in stretch-bending processes. It created a single-objective optimization model to minimize the average positional error across each profile node. Subsequently, a simulated annealing algorithm was used to solve this model, enabling the determination of an optimal die profile, which met the required specifications of the one-time stretch-bending forming process. Moreover, a novel variable compensation factor method was proposed, which accurately determined the optimal compensation factors for each node, thereby enhancing node compensation precision.

Multi-objective optimization and artificial neural network models for enhancing the overall performance of a microchannel heat sink with fins inspired Tesla valve profile

Microchannel heat sinks (MCHSs) are advanced cooling systems used in various applications such as electronics cooling, thermal management of high-power microprocessors, and microelectronic devices. These heat sinks are designed to efficiently dissipate heat generated by electronic components to maintain optimal operating temperatures and ensure device reliability. Employing appropriate fins within MCHSs is a crucial strategy to enhance heat removal efficiency. Since few investigations have been performed on the simultaneous thermo-hydraulic features in microchannels with Tesla valves, this research addresses this gap by suggesting that the microchannels of a heat sink be reinforced with novel Tesla valve-shaped fins. Tesla valves are designed to offer different levels of resistance depending on the flow direction. This characteristic allows for selective control of pressure drop and heat transfer efficiency by simply switching the flow direction. Utilizing Tesla valve-shaped fins is an almost new approach that differentiates the current research from conventional designs. The advantage of this structure is that under different circumstances, depending on whether more heat transfer is required or less pressure drop (ΔP), the flow direction can be changed, and the maximum efficiency of the MCHS can be accessed. The other innovative aspect of this study lies in the integration of artificial neural network (ANN) models to optimize the efficiency of MCHSs equipped with Tesla valve-shaped fins. The simulations were conducted to characterize the flow and thermal features of the MCHS. Then, these data were used as the benchmark for training the ANN models. Four ANN models were introduced to anticipate the Nusselt number (Nu) and ΔP in each forward and reverse direction. The divider's acute angle (α), the divider's obtuse angle (β), and the divider's radius (R) were selected as the input data. Multi-objective optimization and genetic algorithms were used to achieve the optimal conditions. In the optimal thermal design (Setting 2: MCHS with fin variables of α = 60 β = 180, and R = 80 μm in reverse flow direction), the overall performance had a significant enhancement of 57.1 % compared to the device lacking fins. Furthermore, in Setting 2, the overall performance of the MCHS and its diodicity experienced notable improvements of approximately 12.1 % and 38.5 %, respectively. Implementing the parameters of Setting 5 (MCHS with fin variables of α = 60, β = 152.32, and R = 73.10 μm in forward direction) during fin production resulted in a notable enhancement of about 41.1 % in the device's overall performance.

Optimized design of a heliostat mirror field

This study addresses the optimization of heliostats for tower-based solar thermal power plants, crucial for carbon neutrality. Initial work establishes solar position and DNI for heliostat efficiency assessment. Analytical geometry and matrix transformations are used to calculate efficiencies, achieving average annual values of 0.7338 for optical, 0.6797 for cosine, 0.8775 for shading, and 0.8983 for truncation, leading to a 294.6007 MW thermal output and 0.3908 MW per square meter. A multi-objective optimization model, employing a particle swarm algorithm, is used to determine an optimal heliostat arrangement with a circular configuration around the tower, resulting in a total of 2650 heliostats over 9540 sq m of reflectivity. Finally, we optimize the size and height of heliostats to reduce shadows, enabling 1546 heliostats to cover a parabolic distribution of 55656 sq m. This study combines mathematical precision with computational techniques, greatly improving the solar energy conversion efficiency of heliostats and promoting the development of clean energy technology.

Seismic retrofit optimization of water distribution systems based on the reduction of uncertain damage scenarios

Pre-event interventions, such as retrofit of critical components, are effective strategies to improve the seismic performance and resilience of infrastructure systems. This paper proposes a damage scenario reduction method aims to reduce the computation burden within the seismic retrofit optimization for water distribution systems (WDSs) considering the uncertain damages of pipelines. The optimization framework consists of a multi-objective optimization model for WDS pipeline retrofit and a sequential optimization model for the reduction of uncertain damage scenarios. The multi-objective optimization model is established to minimize the retrofit cost and maximize the seismic performance of WDS. The sequential optimization model, namely weighted optimization-based probabilistic scenarios (OPS + W), was designed to mitigate the computational burden of the multi-objective optimization model, which arises from considering pipeline damage uncertainties through quasi-Monte Carlo (QMC) sampling that generates numerous damage scenarios. The OPS + W selects a few representative damage scenarios from numerous QMC scenarios, aiming to minimize the error of pipeline damage probabilities counted by the representative scenarios, where the error is weighted by the importance of pipeline. The effectiveness of OPS + W is verified through its application in seismic retrofit optimization of two WDS cases and compared with other scenario reduction methods. Application results reveal that the retrofit strategies derived from OPS + W have the highest similarity to those obtained from QMC scenarios while requiring only 3 %–10 % of the computation time compared to using QMC scenarios.

Research on the Optimal Trajectory Planning Method for the Dual-Attitude Adjustment Mechanism Based on an Improved Multi-Objective Salp Swarm Algorithm

In this study, an optimization method for the motion trajectory of attitude actuators was investigated in order to improve assembly efficiency in the automatic docking process of large components. The self-developed dual-attitude adjustment mechanism (2-PPPR) is used as the research object, and the structure is symmetrical. Based on the modified Denavit–Hartenberg (MDH) parameter description method, a kinematic model of the attitude mechanism is established, and its end trajectory is parametrically expressed using a five-order B-spline curve. Based on the constraints of the dynamics and kinematics of the dual-posture mechanism, the total posturing time, the degree of urgency of each joint, and the degree of difficulty of the mechanism’s posturing are selected as the optimization objectives. The Lévy flight and Cauchy variation algorithms are introduced into the salp swarm algorithm (SSA) to solve the parameters of the multi-objective trajectory optimization model. By combining the evaluation method of the multi-objective average optimal solution, the optimal trajectory of the dual-tuning mechanism and the motion trajectory of each joint are obtained. The simulation and experiment results show that the trajectory planning method proposed in this paper is effective and feasible and can ensure that the large-part dual-posture mechanism can complete the automatic docking task smoothly and efficiently.

Optimization of isolation valve placement to improve reliability and resilience of water distribution systems

Optimization of isolation valve layout is critical to improve post-failure response restoration of water distribution systems (WDSs) when pipes are damaged. Many studies have been done to optimize isolation valves to improve reliability of WDSs, but less attention has been given to improving the seismic resilience. This paper proposes a multi-objective optimization model for isolation valves placement considering reliability and seismic resilience simultaneously. The newly developed model employs topology-based reliability and resilience indicators to improve efficiency of optimization algorithm by identification of accidental segments and unintended isolation segments using graph theory without hydraulic simulation. The optimization model was applied to two networks with different loop structures. Subsequently, searching trajectory guided by constraints is investigated and performance of isolation valve placements under different valve densities are assessed including topological structure of isolation valves, criticality of segments and isolation valves, and seismic resilience. The results showed that optimal layout of isolation valves obtained through optimization can improve the reliability and resilience of the system. Our novel approach can benefit water utilities that frequently encounter optimal placement of isolation valves to improve the reliability and resilience of WDSs.

Multi-Objective Constrained Optimization Model and Molten Iron Allocation Application Based on Hybrid Archimedes Optimization Algorithm

The challenge of distributing molten iron involves the optimal allocation of blast furnace output to various steelmaking furnaces, considering the blast furnace’s production capacity and the steelmaking converter’s consumption capacity. The primary objective is to prioritize the distribution from the blast furnace to achieve a balance between iron and steel production while ensuring that the volume of hot metal within the system remains within a safe range. To address this, a constrained multi-objective nonlinear programming model is abstracted. A linear weighting method combines multiple objectives into a single objective function, while the Lagrange multiplier method addresses constraints. The proposed hybrid Archimedes optimization algorithm effectively solves this problem, demonstrating significant improvements in time efficiency and precision compared to existing methods.

Research on Joint Distribution Path Planning of Electric Logistics Vehicles with Different Recharge Modes

To address the route planning issues under the community group purchase model for joint delivery, this study thoroughly considers electric logistics vehicles with different recharging methods. The objective is to minimize the sum of operating costs, recharging costs, time window penalty costs, and carbon emission costs. Separate multi-objective optimization models for route planning are constructed for both charging and battery-swapping logistics vehicles. An improved seagull optimization algorithm, guided by the golden sine strategy of the Lévy flight guidance mechanism, is employed to avoid local optima and enhance the solution efficiency. The feasibility of the models and the algorithm is verified through simulation examples. Experimental results show that, at the current stage, battery-swapping logistics vehicles display significant advantages over charging electric logistics vehicles. Although battery-swapping logistics vehicles extend delivery time, they can reduce the total delivery costs to a certain extent. Therefore, the future development prospects of battery-swapping logistics vehicles will be even broader.