Multi-objective Optimization Algorithm Research Articles

Multi-Objective optimal scheduling of energy Hubs, integrating different solar generation technologies considering uncertainty

For a few decades, operators of energy systems have sought to achieve appropriate frameworks due to energy crises and rapid growth in energy requirements. In this regard, this study presents a multi-objective optimization model for an energy hub (EH) designed to manage a diverse energy portfolio. The EH receives electricity, natural gas, hydrogen, seawater, and solar energy as inputs, aiming to satisfy electricity, heating, and freshwater demands at the output port while considering a limited available area. The model incorporates the selection of the optimal solar energy technology (photovoltaics, parabolic dish, or parabolic trough collector) through a comprehensive evaluation encompassing technical, economic, and environmental aspects. To achieve optimal scheduling of the EH’s production units, the model factors in forecasts of solar energy availability alongside electrical, heat, and water load demands. The evaluation of the EH’s performance is conducted through a multi-objective framework considering social welfare, CO2 emissions, voltage stability margin (VSM), a newly proposed simplified fast temperature stability index (SFTSI), and a similarly novel simplified fast pressure stability index (SFPSI). The optimization problem is formulated within a MATLAB environment and solved using a multi-objective Archimedes optimization algorithm across five distinct case studies, each characterized by a varying designated area for solar energy generation. The effectiveness of the proposed model and optimization technique is validated through test systems, with the obtained results demonstrating significant improvements compared to a baseline scenario. These improvements include a 36.18% reduction in CO2 emissions, a 14.22% increase in total social welfare, and reductions in the average values of VSM, SFTSI, and SFPSI when incorporating all solar energy technologies.

Co-design of an unmanned cable shovel for structural and control integrated optimization: A highly heterogeneous constrained multi-objective optimization algorithm

Unmanned cable shovel is an intelligence-based large machine used for excavating in open-pit coal mines, with significant implications for the security and development of energy resources. The co-design of unmanned cable shovels, considering elements from both structural and control systems, has demonstrated superior performance in obtaining optimal solutions compared to the traditional approaches that focus on individual systems. However, the inherent complexities of diverse systems often lead to heterogeneous function evaluations, posing challenges for the existing optimization algorithms. To address highly heterogeneous multi-objective optimization problems characterized by surrogate-approximated expensive functions and explicit inexpensive functions, this study proposes a highly heterogeneous constrained multi-objective optimization (HHCMO) algorithm. Leveraging NSGA-III as an evolutionary optimizer and the Kriging model for approximating expensive functions, HHCMO strategically addresses the challenges posed by highly heterogeneous objective evaluations. A Monte Carlo sampling-based expected hypervolume improvement (EHVI) criterion, employing a new nearest point approximation method in a unified sampling space, effectively handles the impact of heterogeneous objective evaluations on the sequential infill of the Kriging model. Thorough evaluations on both unconstrained and constrained multi-objective benchmark problems validate the correctness of HHCMO and its superiority relative to state-of-the-art algorithms. Finally, the effectiveness of the proposed method is successfully demonstrated in an engineering scenario of an unmanned cable shovel, where it adeptly handles expensive functions from the structural system and inexpensive functions from the control system.

Multi-Objective Five-Element Cycle Optimization Algorithm Based on Multi-Strategy Fusion for the Bi-Objective Traveling Thief Problem

In this paper, we propose a Multi-objective Five-element Cycle Optimization algorithm based on Multi-strategy fusion (MOFECO-MS) to address the Bi-objective Traveling Thief Problem (BITTP), an extension of the Traveling Thief Problem that incorporates two conflicting objectives. The novelty of our approach lies in a unique individual selection strategy coupled with an innovative element update mechanism rooted in the Five-element Cycle Model. To balance global exploration and local exploitation, the algorithm categorizes the population into distinct groups and applies crossover operations both within and between these groups, while also employing a mutation operator for local searches on the best individuals. This coordinated approach optimizes parameter settings and enhances the search capabilities of the algorithm. Extensive experiments were conducted on nine BITTP instances, comparing MOFECO-MS against eight state-of-the-art multi-objective optimization algorithms. The results show that MOFECO-MS excels in both Hypervolume (HV) and Spread (SP) indicators, while also maintaining a high level of Pure Diversity (PD). Overall, MOFECO-MS outperformed the other algorithms in most instances, demonstrating its superiority and robustness in solving complex multi-objective optimization problems.

Multi-objective sand cat swarm optimization based on adaptive clustering for solving multimodal multi-objective optimization problems

Multimodal multi-objective optimization problems (MMOPs) represent a highly challenging class of complex problems, characterized by the presence of several Pareto solution sets in the decision space which map to the identical Pareto-optimal front. The goal of solving MMOPs is to find multiple distinct Pareto sets to sustain a balance between good convergence and diversification of populations. In this paper, a multi-objective sand cat swarm optimization algorithm (MOSCSO) is developed to address MMOPs. In the MOSCSO algorithm, an adaptive clustering-based specific congestion distance technique is introduced to compute the level of crowdedness. This ensures an even distribution of individuals, avoiding excessive crowding in the local area. Subsequently, enhanced search-and-attack prey updating mechanisms are designed to effectively increase not only the exploration and exploitation capabilities of the algorithm but also to enhance the diversity of the swarm in both the decision space and the objective space. To verify the effectiveness of the proposed algorithm, the MOSCSO is applied to solve the CEC2019 complex multimodal benchmark function. The experimental outcomes illustrate that the proposed approach possesses excellent performance in searching for Pareto solutions compared with other algorithms. Meanwhile, the method is also employed to address the map-based distance minimization problem, which further validates the usefulness of the MOSCSO.

Trajectory Optimization to Enhance Observability for Bearing-Only Target Localization and Sensor Bias Calibration.

This study addresses the challenge of bearing-only target localization with sensor bias contamination. To enhance the system's observability, inspired by plant phototropism, we propose a control barrier function (CBF)-based method for UAV motion planning. The rank criterion provides only qualitative observability results. We employ the condition number for a quantitative analysis, identifying key influencing factors. After that, a multi-objective, nonlinear optimization problem for UAV trajectory planning is formulated and solved using the proposed Nonlinear Constrained Multi-Objective Gray Wolf Optimization Algorithm (NCMOGWOA). Simulations validate our approach, showing a threefold reduction in the condition number, significantly enhancing observability. The algorithm outperforms others in terms of localization accuracy and convergence, achieving the lowest Generational Distance (GD) (7.3442) and Inverted Generational Distance (IGD) (8.4577) metrics. Additionally, we explore the effects of the CBF attenuation rates and initial flight path angles.

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.

Multi-objective optimization method for cross-workshop linkage production of partially flexible free-forging with forward single-machine scheduling

Forging is an important sector in China’s machinery manufacturing industry. To complete the processing of forgings, it is often necessary to go through multiple processes, which are commonly performed by different workshops. Due to the complexity of cross-workshop production, there are few studies on cross-workshop scheduling in the forging industry. Therefore, in order to realize resource sharing and collaborative production between multiple workshops, and improve the overall production efficiency and resource utilization rate, it is very important to optimize the scheduling of linked cross-workshop production. In this paper, a new cross-workshop partial flexible hammer forging scheduling model (CSPFH-FSM) is established to solve the scheduling problem of linked cross-workshop production with production time and energy consumption serving as the overall optimization goals in the whole partially flexible free forging production line (P3FPL). A single-machine forward-prediction variable genetic operator NGSA-II algorithm (SPVGO-NGSA II) is proposed to solve the multi-objective optimization problem of partially flexible production, in which the variable genetic operator is added to the effective coding, and the search strategy is dynamically adjusted to avoid reaching locally optimal solutions. Due to the interference of maintenance and the insufficient utilization of energy after forging, a fixed maintenance disturbance and a residual temperature utilization strategy are added to the scheduling process. Finally, the optimization obtained using the proposed variable and traditional fixed genetic operators are compared for different orders, and the algorithm proposed in this paper is compared with the typical multi-objective optimization algorithms. The results validate the effectiveness of the proposed algorithm, and provide a basic scheme for the linked scheduling of the whole production line in practical applications.

Fair Federated Learning with Multi-Objective Hyperparameter Optimization

Federated learning (FL) is an attractive paradigm for privacy-aware distributed machine learning, which enables clients to collaboratively learn a global model without sharing clients’ data. Recently, many strategies have been proposed to improve the generality of the global model and thus improve FL effect. However, existing strategies either ignore the fairness among clients or sacrifice performance for fairness. They cannot ensure that the gap among clients is as small as possible without sacrificing federated performance. To address this issue, we propose ParetoFed , a new local information aggregation method dedicated to obtaining better federated performance with smaller gap among clients. Specifically, we propose to use multi-objective hyperparameter optimization (HPO) algorithm to gain global models that are both fair and effective. Then, we send Pareto Optimal global models to each client, allowing them to choose the most suitable one as the base to optimize their local model. ParetoFed not only make the global models more fair but also make the selection of local models more personalized, which can further improve the federated performance. Extensive experiments show that ParetoFed outperforms existing FL methods in terms of fairness, and even achieves better federated performance, which demonstrates the significance of our method.

Optimized task scheduling approach with fault tolerant load balancing using multi-objective cat swarm optimization for multi-cloud environment

Multi-cloud environment enables an organization to access services from more than one cloud service providers the use of multiple cloud computing and it can be treated as single heterogeneous environment. It enables autonomy to run the tasks on private or public cloud based on business or technical requirements. In a multi-cloud platform, load balancing is an essential task to serve the requests from multiple users with different resources effectively. It helps to improve utilization of the cloud resources, throughput, reduce makespan and avoid overload at resources. Load balancing also facilitates the redirection of traffic to resources running in another cloud when a failure occurs in a cloud. Hence, it is more vital to have optimized load balancing methods in multi-cloud infrastructure in order to improve the system performance. This paper presents an optimized fault tolerant load balancing method using multi-objective cat swarm optimization algorithm called MCSOFLB and the results are then compared against other powerful optimization algorithms. The experimental results evidently show that the proposed algorithm ranks first on the whole. The MCSOFLB method produces an average improvement of 31 % makespan, 6 % resource utilization, 12 % cost, 6 % success rate and 32 % average throughput over other benchmark algorithms.

Development and analysis of a novel power-to-gas-to-power system driven by the Allam cycle for simultaneous electricity and water production

The global energy sector faces dual challenges of reducing carbon emissions and managing the intermittency of renewable energy sources. Addressing the challenge of renewable energy intermittency, the power-to-gas technique emerges as a potent solution and the Allam cycle stands out as a remarkably efficient, semi-closed, supercritical carbon dioxide cycle, boasting a zero-carbon emission process. In the current study, the combination of the Allam cycle and power-to-gas technology yields a zero-carbon emission process, culminating in a highly efficient multi-generation system. This innovative system aims to generate power, methane, and freshwater simultaneously. The waste heat produced as a by-product of the Allam cycle is effectively utilized by integrating it with the multi-effect desalination-thermal vapor compression unit. The system’s performance was evaluated using Engineering Equation Solver for exergy and economic assessments, while MATLAB incorporates an artificial neural network and moth-flame algorithm for multi-objective optimization. The optimization process operates under the influence of nine key decision variables, ensuring comprehensive analysis and robust decision-making. Based on the optimization results and Technique for Order Preference by Similarity to Ideal Solution method, the optimal solution for freshwater mass flow rate, exergy efficiency, and cost rate is determined to be 378.5 kg/s, 39.03 %, and 11,021 $/h, respectively. Finally, this innovative system demonstrates the potential to address renewable energy intermittency and carbon emissions simultaneously, offering a promising approach for sustainable energy generation and water production.

Enhanced Structural Design of Prestressed Arched Trusses through Multi-Objective Optimization and Multi-Criteria Decision-Making

The structural design of prestressed arched trusses presents a complex challenge due to the need to balance multiple conflicting objectives such as structural performance, weight, and constructability. This complexity is further compounded by the interdependent nature of the structural elements, which necessitates a comprehensive optimization approach. Addressing this challenge is crucial for advancing construction practices and improving the efficiency and safety of structural designs. The integration of advanced optimization algorithms and decision-making techniques offers a promising avenue for enhancing the design process of prestressed arched trusses. This study proposes the use of three advanced multi-objective optimization algorithms: NSGA-III, CTAEA, and SMS-EMOA, to optimize the structural design of prestressed arched trusses. The performance of these algorithms was evaluated using generational distance and inverted generational distance metrics. Additionally, the non-dominated optimal designs generated by these algorithms were assessed and ranked using multiple multi-criteria decision-making techniques, including SAW, FUCA, TOPSIS, PROMETHEE, and VIKOR. This approach allowed for a robust comparison of the algorithms and provided insights into their effectiveness in balancing the different design objectives. The results of the study indicated that NSGA-III exhibited superior performance with a GD value of 0.215, reflecting a closer proximity of its solutions to the Pareto front, and an IGD value of 0.329, indicating a well-distributed set of solutions across the Pareto front. In comparison, CTAEA and SMS-EMOA showed higher GD values of 0.326 and 0.436, respectively, suggesting less convergence to the Pareto front. However, SMS-EMOA demonstrated a balanced performance in terms of constructability and structural weight, with an IGD value of 0.434. The statistical significance of these differences was confirmed by the Kruskal–Wallis test, with p-values of 2.50×10−15 for GD and 5.15×10−06 for IGD. These findings underscore the advantages and limitations of each algorithm, providing valuable insights for future applications in structural optimization.

Optimized green infrastructure planning at the city scale based on an interpretable machine learning model and multi-objective optimization algorithm: A case study of central Beijing, China

Green infrastructure (GI) has developed as a sustainable approach to the mitigation of urban floods. While machine learning (ML) models have exhibited advantages in urban flood simulation, their direct application to support the quantitative planning of GI at the city scale remains a challenge. To address this, an interpretable ML model based on support vector machine (SVM) and the Shapley additive explanations (SHAP) approach is integrated with the non-dominated sorting genetic algorithm-II (NSGA-II) in this study. The model is applied to the case of central Beijing, China, and demonstrates a robust performance with a high area under curve (AUC) value of 0.94. The results of the urban flood susceptibility assessment identify the urban-rural transition zone in the study area as being under a greater flood threat. Via model interpretation with SHAP, the dominant roles of GI and grey infrastructure (GrI) in preventing flood are revealed and the non-linear complementarity between the two is demonstrated to be more significant in study units with a GI proportion of less than 0.45. Supported by the NSGA-II-based optimization framework, optimal GI plans under different total implementations of GI are achieved, among which a solution with a 3.21% increase in the total GI area is selected as that with the best investment efficiency. The pattern of GI implementation is suggested to be dispersed and small-scale by model. This study provides a tool with broad application prospects, effectively integrating GI implementation with urban planning. The findings of this study not only provide important references for the determination of the priority areas of new ecological space in Beijing, but also provide areas that share similar characteristics with new insight into GI planning and the management of urban floods.

An interactive reference-point-based method for incorporating user preferences in multi-objective structural optimization problems

Although multi-objective evolutionary algorithms (MOEAs) are often used to solve multi-objective structural optimization problems (MOSOPs), the incorporation of decision-maker (DM) preferences into MOEAs to search for regions of interest from the Pareto front has received limited attention in these problems. Recently, three MOEAs have shown promising results in solving MOSOPs incorporating DM’s preferences based on a priori reference points. However, providing this information can be challenging as the DM may not know an initial reference point for the MOSOP. Therefore, this study analyzes three interactive multi-objective optimization (IMO) algorithms, called I-NSGA-II, I-GDE3, and I-GDE3+APM, in which the reference points are progressively incorporated during the search process. These IMOs are applied to benchmark MOSOPs and evaluated using well-known performance indicators. The results demonstrate that I-NSGA-II outperforms I-GDE3 and I-GDE3+APM, which have similar overall performances. Additionally, the IMOs considered here outperformed their respective a priori approaches in MOSOPs where the reference points are located at the central sector of the Pareto front. Also, we identified the performance indicators in which the approaches considered in this work performed better than their corresponding a priori ones. Furthermore, the research findings suggest that these IMOs can generate good results without defining a priori reference points, making them a promising approach for solving MOSOPs and enabling DMs to make better and more reliable decisions.

Performance optimization of latent heat storage device based on surrogate-assisted multi-objective evolutionary algorithm and CFD method

The design of fin structures and the formulation of heat conductivity enhancers have always been critical factors influencing the performance of latent heat storage devices. However, achieving the optimal parameters of fin structures and the fraction of heat conductivity enhancers when adjusting multiple parameters simultaneously remains a time-consuming and labor-intensive task. In this paper, a combination of machine learning methods and computational fluid dynamics (CFD) was employed to optimize the parameters of fin structures and the mass fraction of expanded graphite with minor computational resources and time costs, thereby obtaining a set of Pareto optimal latent heat storage (LHS) devices that simultaneously considers energy storage power and energy storage capacity. The combination essentially is a surrogate assistant multi-objective evolutionary optimization algorithm, which utilizes batch recommendation strategies to recommend multiple high-quality candidate solutions that achieve a balance between two conflicting optimization objectives in CFD simulations. This method significantly reduces the number of model iterations and saves the time and computational resources required for multi-objective optimization. The results showed that the proposed algorithm only required 80 cases of CFD calculations to obtain two well-fitted Gaussian process models, leading to a 91 % improvement in computational efficiency. According to the recommendations from the algorithm, the LHS device with 78 fins of 2.0 mm thickness and 20 % mass fraction of expanded graphite could achieve the optimal trade-off of energy storage power and energy storage capacity. Compared to the prototype, the energy storage power and total energy storage capacity increased by 46.7 % and 22.1 %, respectively. The method proposed in this study can provide guidance for the optimal configuration design of LHS devices.

Optimization of Sailing Speed for Inland Electric Ships Based on an Improved Multi-Objective Particle Swarm Optimization (MOPSO) Algorithm

Sailing speed is a critical factor affecting the ship’s energy consumption and operating costs for a voyage. Inland waterways present a complex navigation environment due to their narrow channels, numerous curved segments, and significant variations in water depth and flow speed. This paper constructs a model of a ship’s energy consumption based on an analysis of ship resistance and the energy transfer relationship of ships. The K-means clustering algorithm is introduced to divide the Yangtze River waterway into multiple segments based on the similarity of navigation environments. Considering the constraints of the ship’s main engine and the desired arrival time, a multi-objective particle swarm optimization (MOPSO) algorithm, improved with cosine decreasing inertial weight and Gaussian random mutation, is employed to optimize segmented navigation speeds to achieve different goals. Finally, four cases are studied with a fully electric ship navigating the reaches of the Yangtze River. The results indicate that the optimized speed can reduce ship energy consumption by up to 6.18% and significantly reduce ship energy consumption and operational costs under different conditions.

MOHCOA: Multi-objective hermit crab optimization algorithm for feature selection in sentiment analysis of Covid-19 Twitter datasets

MOHCOA: Multi-objective hermit crab optimization algorithm for feature selection in sentiment analysis of Covid-19 Twitter datasets

Multi-objective preventive maintenance strategy and optimization considering unavailability and cost: A case study on VOBC

The Vehicle On-Board Controller (VOBC) is a core component of urban rail transit trains, with high maintenance costs and serious consequences in case of failure. Ensuring long-term VOBC operation while minimizing maintenance costs is of paramount importance. This paper proposes a multi-objective preventive maintenance (PM) strategy and optimization considering system unavailability and maintenance costs. Firstly, we provide a statement of the problem and analyze the impact of different maintenance actions on VOBC. Next, the decreasing process parameter is introduced to adjust the PM interval dynamically. The mathematical models of system unavailability and maintenance costs are derived to construct a multi-objective PM strategy. Then, we develop a hybrid heuristic algorithm including multi-objective particle swarm optimization and genetic algorithm (MOPSO-GA), which is used to obtain the optimal Pareto frontier for maintenance strategies. In the study, we select maintenance strategies by introducing an aversion factor to fulfill the preferences of different decision-makers. A comprehensive sensitivity analysis is performed to illustrate the impact of several critical parameters on the multi-objective PM strategy. The case study on Beijing subway’s VOBC is also executed. The outcomes indicate that the proposed method can dynamically adjust to match the preferences of diverse decision-makers and the MOPSO-GA algorithm has excellent robustness on most parameters. This approach allows for creating field maintenance strategies catering to the varied maintenance requirements of urban rail transit VOBC.

Chaotic self-adaptive sine cosine multi-objective optimization algorithm to solve microgrid optimal energy scheduling problems

Researchers are increasingly focusing on renewable energy due to its high reliability, energy independence, efficiency, and environmental benefits. This paper introduces a novel multi-objective framework for the short-term scheduling of microgrids (MGs), which addresses the conflicting objectives of minimizing operating expenses and reducing pollution emissions. The core contribution is the development of the Chaotic Self-Adaptive Sine Cosine Algorithm (CSASCA). This algorithm generates Pareto optimal solutions simultaneously, effectively balancing cost reduction and emission mitigation. The problem is formulated as a complex multi-objective optimization task with goals of cost reduction and environmental protection. To enhance decision-making within the algorithm, fuzzy logic is incorporated. The performance of CSASCA is evaluated across three scenarios: (1) PV and wind units operating at full power, (2) all units operating within specified limits with unrestricted utility power exchange, and (3) microgrid operation using only non-zero-emission energy sources. This third scenario highlights the algorithm’s efficacy in a challenging context not covered in prior research. Simulation results from these scenarios are compared with traditional Sine Cosine Algorithm (SCA) and other recent optimization methods using three test examples. The innovation of CSASCA lies in its chaotic self-adaptive mechanisms, which significantly enhance optimization performance. The integration of these mechanisms results in superior solutions for operation cost, emissions, and execution time. Specifically, CSASCA achieves optimal values of 590.45 €ct for cost and 337.28 kg for emissions in the first scenario, 98.203 €ct for cost and 406.204 kg for emissions in the second scenario, and 95.38 €ct for cost and 982.173 kg for emissions in the third scenario. Overall, CSASCA outperforms traditional SCA by offering enhanced exploration, improved convergence, effective constraint handling, and reduced parameter sensitivity, making it a powerful tool for solving multi-objective optimization problems like microgrid scheduling.

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.

A double-layer Q-learning driven memetic algorithm for integrated scheduling of procurement, production and maintenance with distributed resources

The existing research on integrated production scheduling typically focuses on activities related to both production and post-production (e.g., operation and maintenance), with limited consideration for simultaneously integrating pre-production activities (e.g., raw material procurement). However, achieving the equilibrium between the manufacturer and the demander for intelligent manufacturing systems requires the optimal scheduling solution that integrates both pre-production and post-production activities. Inspired by this, we investigate a novel integrated scheduling problem that concurrently considers raw material procurement, production scheduling and equipment maintenance (abbreviated as SIPPM). A mixed-integer linear programming model is developed to simultaneously minimize the total costs for the manufacturer and the demander. Furthermore, a double-layer Q-learning driven memetic algorithm (DQMA) is proposed to solve the SIPPM. In DQMA, a well-tailored three-layer hybrid encoding method is presented for chromosome representation. The global search of DQMA employs three crossover and three mutation operators. Moreover, a knowledge-based local search operator with six methods, guided by an effective double-layer Q-learning structure, is devised to enhance local exploitation capabilities. The superiority of DQMA is verified through comparison with three popular multi-objective optimization algorithms on 108 newly established benchmark instances. The proposed integrated scheduling mode is proven to be more effective than two separated scheduling modes without considering raw material procurement.