Multi-objective Optimization Research Articles

Thermo-economic performance analysis and multi-objective optimization of viscosity ratio and thermal conductivity ratio of copper oxide–palm oil nanolubricants

Through experimental research, this work explores the thermophysical properties, cooling efficiency, and economic viability of copper oxide–palm oil nanolubricants in tribology applications. The viscosity and thermal conductivity of the nanolubricants were tested at three different volume concentrations (0.1, 0.3, and 0.5 vol. %) throughout a temperature range of 30 °C to 80 °C at intervals of 10 °C. Researchers looked attentively at how the viscosity and thermal conductivity ratios of the nanolubricants were affected by temperature and volume concentration. A significant increase in thermal conductivity was noted with increasing concentration and temperature. On the other hand, as temperature increased, viscosity reduced and was dependent on volume concentration. The property enhancement ratio was used to evaluate the nanolubricants' cooling capacity before an economic analysis of their cooling efficacy was conducted. Based on experimental data, the study led to the creation of novel correlations between the viscosity ratio and thermal conductivity ratio. These models showed a high degree of agreement (R2 values of 99.47% for the thermal conductivity ratio and 97.78% for the viscosity ratio) between the expected and actual outcomes. The ideal values of the viscosity and thermal conductivity ratios were 1.10 and 1.62, respectively. These values corresponded to a critical temperature of 37.32 °C and a volume concentration of 0.16 vol. % for nanoadditives. The findings offer valuable insights into optimizing nanolubricants for enhanced cooling performance in tribological systems, with potential applications in improving energy efficiency and reducing operational costs in industrial processes.

Exergy-economic based multi-objective optimization and carbon footprint analysis of solar thermal refrigeration systems

The increasing carbon footprint associated with conventional cooling methods underscores the urgent need for sustainable alternatives. This study investigates the economic and environmental advantages of various solar-thermal cooling systems, with a focus on optimizing their performance across different climate conditions. Employing a multi-objective approach, the research emphasizes exergy-economic indices to optimize selected cycles. The analysis covers multiple refrigeration technologies, including liquid absorption, solid adsorption, and solid desiccant cycles. Results indicate that the liquid absorption cycle performs optimally in hot, arid climates, reducing the payback period to approximately 8 years when optimized. In hot and humid regions, the solid desiccant cycle proves most effective due to its superior humidity control, yielding a payback period of 5.3 years. For cold and mountainous areas, the solid adsorption cycle is preferred, with a payback period of 13.5 years, while moderate and humid climates benefit from the solid desiccant cycle for both cooling and humidity regulation. The exergy-economic factors for the solar refrigeration systems across semi-arid, hot and arid, hot and humid, cold and mountainous, and moderate and humid climates are 0.758, 0.602, 0.698, 0.74, and 0.575, respectively.

Dynamic Multi-objective Optimization based on Classification Response of Decision Variables

In recent years, many dynamic multi-objective optimization algorithms (DMOAs) have been proposed to address dynamic multi-objective optimization problems (DMOPs). Most existing DMOAs treat all decision variables uniformly and respond to them in an identical manner. This paper proposes a dynamic multi-objective optimization algorithm based on the classification response of decision variables (CRDV-DMO). Firstly, CRDV-DMO categorizes the decision variables into convergence variables and diversity variables. Different decision variables adopt distinct response strategies. The response strategy of diversity variable (RSDV) uses Latin hypercube sampling to generate the diversity variables of the new environment. For each dimensional convergence variable, the response strategy of convergence variable (RSCV) first evaluates whether the basic center prediction strategy (CPS) yields positive feedback or negative feedback, further determining the predictability of that dimensional convergence variable. RSCV then decides to either use the basic CPS to generate the convergence variable for that dimension or to retain that dimensional convergence variable from the current environment, based on the predictability of that dimensional convergence variable. The proposed algorithm is extensively studied through comparison with several advanced DMOAs, demonstrating its effectiveness in dealing with the benchmark DMOPs and the parameter-tuning problem of the PID controller on a dynamic system.

Multi-objective optimization of the appendages of a sailing yacht using the normal boundary intersection method

In this paper, a multidisciplinary design optimization algorithm, the Normal Boundary Intersection (NBI) method, is applied to the design of some devices of a sailing yacht. The full Pareto front is identified for two different design problems, and the optimal configurations are compared with standard devices. The great efficiency of the optimization algorithm is demonstrated by the wideness and density of the identified Pareto front.

Optimizing pyrolysis and Co-Pyrolysis of plastic and biomass using Artificial Intelligence

The rapid increase in biomass and plastic waste poses significant environmental challenges. Co-pyrolysis of biomass with plastic wastes offers a promising avenue for sustainable waste management and renewable energy generation. This study covers several novel aspects: First, it investigates the impacts of feedstock composition and operating conditions in pyrolysis (individual feedstock) and co-pyrolysis (biomass and plastic wastes). The study reveals that synergistic effects, specifically improved yields and optimized temperature, exist in the co-pyrolysis of biomass and plastic wastes compared to individual feedstock. Secondly, a suitable blended machine learning predictive model (with Random Forest, Gradient Boosting Regressor, and XGBoost) and robust optimization framework are developed to address model accuracy, non-linear interactions, and uncertainties in pyrolysis such as temperature, heating rate, and biomass-to-plastic ratio. This study predicts the bio-oil yield quantitatively (amount) and qualitatively (composition) with high accuracy (R2 > 0.97). Thirdly, key factors contributing to yield include plastic content (18 %) and biomass type (13 %) have been identified through Gini feature importance and Shapley Additive Explanation (SHAP) analysis. Furthermore, multi-objective optimization techniques reveal the most optimal bio-oil yield under specific conditions, supported by uncertainty analysis, which confines bio-oil yield to a range of 30–50 %. Finally, it also demonstrates a case study to find the optimal bio-oil yield and quality conditions using co-pyrolysis of local resources, i.e., biomass (wood and bagasse) and plastic wastes. The case study suggests optimal conditions like > 50 °C heating rate, <50 min pyrolysis time, and > 60 % plastic content in a blend of wood and HDPE. This study assists industries and policymakers to assess and understand the viability of co-pyrolysis, optimal design parameters, and process impacts.

Dynamic Multiobjective Optimization Based on Multi-Environment Knowledge Selection and Transfer

Background: Dynamic multiobjective optimization problems (DMOPs) involve multiple conflicting and time-varying objectives, and dynamic multiobjective algorithms (DMOAs) aim to find Pareto optima that are closer to the real one in the new environment as soon as possible. In particular, the introduction of transfer learning in DMOAs has led to good results in solving DMOPs. However, the selection of valuable historical knowledge and the mitigation of negative transfer remain important problems in existing transfer learning-based DMOAs. Method: A DMOA based on multi-environment knowledge selection and transfer (MST-DMOA) is proposed in this article. First, by clustering historical Pareto optima, some representative solutions that can reflect the main evolutionary information are selected as knowledge of the environment. Second, the similarity between the historical and current environments is evaluated, and then the knowledge of multiple similar environments is selected as valuable historical knowledge to construct the source domain. Third, solutions with high quality in the new environment are obtained to form the target domain, which can better help historical knowledge to adapt to the current environment, thus effectively alleviating negative transfer. Conclusions: We compare the proposed MST-DMOA with five state-of-the-art DMOAs on fourteen benchmark test problems, and the experimental results verify the excellent performance of MST-DMOA in solving DMOPs.

Method to Improve Seismic Performance : Selection of Key Components and Multi-Objective Optimization of Risk and Cost

Method to Improve Seismic Performance : Selection of Key Components and Multi-Objective Optimization of Risk and Cost

An Analysis and Reliability-based Optimization Design Method of Trajectory Accuracy for Industrial Robots Considering Parametric Uncertainties

To address the challenges of poor trajectory accuracy in industrial robots, which has emerged as a technological bottleneck hindering further robots’ applications in high-precision manufacturing industries, this paper proposes a method for the analysis and reliability-based optimization design for industrial robots’ trajectory accuracy considering parametric uncertainties. Firstly, the dynamic equation of an articulated industrial robot with six degrees of freedom is derived, incorporating the Stribeck joint friction model, followed by the uncertain parameter identification of this dynamic model. Subsequently, an uncertainty simulation system for the robot is established based on the constructed dynamic model and the sensitivity of system uncertain parameters to the robot trajectory accuracy is analyzed, where 10 key parameters are obtained among 54 uncertain parameters. Finally, a reliability-based multi-objective optimization design methodology is proposed synthesizing the robot trajectory accuracy, manufacturing cost, and quality loss, to achieve tolerance design of the robot's parameters, and enables minimizing costs and quality losses while ensuring the robot's trajectory accuracy reliability. The performance and practicality of the proposed method were validated using a six-degree-of-freedom rotary joint serial industrial robot as an example.

Redefining Urban Morphology Using Multi-Objective Optimization for Enhanced Thermal Comfort in Neighborhoods

Redefining Urban Morphology Using Multi-Objective Optimization for Enhanced Thermal Comfort in Neighborhoods

Coordinated intelligent control strategy and power management for marine gas turbine under pulsed load using optimized neural network

Faced with the challenges of potentially irreversible damage due to instantaneous large pulsed power loads onboard, considering all-electric ship's small power grid capacity and complex gas turbine system, this study proposes a coordinated intelligent control strategy. It utilizes multi-objective optimization neural network to control a validated 20MW ship three-shaft gas turbine, ensuring global optimization tailored to pulsed load variations. Research findings reveal that the gas turbine efficiency at the design point is 32.3%, with a steady-state error within 2.8% and a maximum steady time error of 3.4 seconds, demonstrating acceptable accuracy. Under slope-type pulsed load of 20MW change in 5s, the proposed control strategy effectively manages speed while utilizing the energy storage system to smooth load inputs. It is worth noting that, increasing the fuel flow rate to 1.28 kg/s and inlet guide vane to 5.8 degrees reduces overshoot by 0.7% and rotor steady time by 15.4 seconds. Under transient-type pulsed load of 10MW back and forth change in 5s, the coordinated inlet guide vane control strategy mitigates surge margin and exhaust temperature issues. Adjusting the fuel flow to 1.17 kg/s and inlet guide vane to 9.96 degrees enables the gas turbine to operate within safety boundaries, reducing rotor speed overshoot by 0.4% and steady time by 6.1 seconds. Importantly, the optimized control strategy without energy storage system provides faster and safer dynamic regulation and power transmission for ship acceleration processes under radar load. However, considering only the coordinated strategy of the fuel sub-control loop, it is advisable to avoid compressor surge and over-temperature protection for electromagnetic ejection load scenarios. The research results will lay a valuable technical foundation for the intelligent control and smooth power transfer of the all-electric ship gas turbine power system with pulse loads in the extreme missions of ships in the future.

Adaptive decomposition-based evolutionary algorithm for many-objective optimization with two-stage dual-density judgment

In order to better balance the convergence and diversity of MOEA/D for many objective optimization problems (MaOPs) with various Pareto fronts (PFs), an adaptive decomposition-based evolutionary algorithm for MaOPs with two-stage dual-density judgment is proposed. To solve the problem that weighted Tchebycheff decomposition may produce weakly Pareto optimal solutions when the solution is not unique or the uniqueness is difficult to guarantee, an augmented weighted Tchebycheff decomposition is adopted. To balance the convergence and diversity of non-dominated solutions in the external archive, different sparsity-level evaluations using vector angles or Euclidean distances are used to measure the distribution of solutions at different stages. To improve the diversity of solution sets obtained by MOEA/D for various PFs, an adaptive weight vector adjustment method based on two-stage dual-density judgment is presented. For weight vector addition, the potential search area is found according to the two-stage density judgment, and then a two-stage sparsity level judgment on the solutions of this area is performed for a second density judgment. For weight vector deletion, the degree of crowding is used to delete the weight vectors with a high crowding degree. Compared with nine advanced multi-objective optimization algorithms on DTLZ and WFG problems, the results demonstrate that the performance of the proposed algorithm is significantly better than other algorithms.

Minimization of Rebar Cutting Waste Using BIM and Cutting Pattern-Oriented Multiobjective Optimization

Minimization of Rebar Cutting Waste Using BIM and Cutting Pattern-Oriented Multiobjective Optimization

Multi-objective optimization of supersonic separator for gas removal and carbon capture using three-field two-phase flow model and non-dominated sorting Genetic Algorithm-II (NSGA-II)

Supersonic separator (SS) is an efficient technology used for gas removal and carbon capture. To enhance its performance, many researchers have studied its structure; however, existing studies have primarily used traditional CFD models for single-objective structural optimization of the separator’s separation performance. However, in the SS, separation efficiency and pressure-loss ratio are the most important and conflicting performance parameters, and evaluating separation performance in isolation from either one is incomplete. Therefore, in this paper, a gas–liquid two-phase three-field CFD model considering a liquid film is firstly established, and then this model is combined with the non-dominated Sorting Genetic Algorithm-II (NSGA-II) for multi-objective optimization of the coupled multiple structural parameters with the objective of the separation efficiency and pressure-loss ratio. The results indicated that the maximum relative errors between simulated and predicted values for the four Pareto optimal solutions in computing pressure loss ratio and separation efficiency are 5.4% and 5.3%, respectively, with these solutions achieving the maximum reduction in pressure loss ratio of 28.3% at the same 90% separation efficiency compared to the original structure.

Multi-objective optimization of metal foam parameters for enhanced performance of LHTES unit in shell-and-tube configurations

Multi-objective optimization of metal foam parameters for enhanced performance of LHTES unit in shell-and-tube configurations

Analysis and Optimization of Laser Beam Welding Parameters for Aluminium Composite (Al-Zn-Cu Alloy) by Grey Relational Optimization

Aluminium and its composites are widely used in production to enhance the strength of lightweight objects. In this study, an AA7075/SiC composite was fabricated using a stir casting route. Multi-objective optimization and finite element analysis were performed with various process parameters on a manufactured aluminium composite (AA7075 + SiC) undergoing a laser beam welding process. Four welding parameters, i.e., pulse frequency, power, welding speed (transverse), and wire size were taken for laser welding as per the L-9 orthogonal array for experimental study. Tensile strength, deflection, temperature distribution, Rockwell hardness (fusion zone), and Rockwell hardness (heat affected zone) were taken as output parameters after welding. The standard deviation objective weighting–grey relational optimization method optimized the process parameter. ANSYS APDL 23 software was utilized to simulate the entire laser welding method with a cylindrical heat source to predict the temperature distribution in the butt-welded plates. This software uses finite element analysis and gives a deviation of only 5.85% for temperature distribution with experimental results. This study helps to understand the effect of various parameters on the welding strength of the aluminium composite.

Design of Integrated CO2 Transportation-Injection-Storage System based on Multi-Objective Optimization

Design of Integrated CO2 Transportation-Injection-Storage System based on Multi-Objective Optimization

Exploring Evolutionary Algorithms for Multi-Objective Optimization in Seismic Structural Design

The seismic design of structures is an emerging practice in earthquake-resistant construction. Therefore, using energy-dissipation devices and optimizing these devices for various purposes are important. Evolutionary computation, nature-inspired, and meta-heuristic algorithms have been studied more in recent years for the optimization of these devices. In this study, the development of evolutionary algorithms for seismic design in the context of multi-objective optimization is examined through bibliometric analysis. In particular, evolutionary algorithms such as genetic algorithms and particle swarm optimization are used to optimize the performance of structures to meet seismic loads. While genetic algorithms are used to improve both the cost and seismic performance of the structure, particle swarm optimization is used to optimize the vibration and displacement performance of structures. In this study, a bibliometric analysis of 661 publications is performed on the Web of Science and Scopus databases and on how the research in this field has developed since 1986. The R-studio program with the biblioshiny package is used for the analyses. The increase in studies on the optimization of energy dissipation devices in recent years reveals the effectiveness of evolutionary algorithms in this field.

Sistem Pendukung Keputusan Seleksi Peserta Lomba Karnaval Murid TK Di Kabupaten Pidie Menggunakan Metode Multi Objective Optimization On The Basic Of Ratio Analysis (MOORA) Berbasis Web

Kindergarten is an example of an educational sector that has been impacted by advances in computer technology. One of the most important parts of a kindergarten is student assessment and the creativity of school children. In one kindergarten there are dozens of students and each has different creativity values. However, there are problems at the Pidie Regency Education Office in assessing the process of determining the selection as the best participant to take part in the kindergarten student carnival competition in Pidie Regency. This writing will discuss the design of a decision support system using the Multi-Objective Optimization On The Basis Of Ratio Analysis (MOORA) method to select participants in the kindergarten student carnival competition in Pidie Regency. The aim of this research is to assist the Pidie District Education Office in making decisions by evaluating and ranking existing alternatives so that it is easier to determine which kindergarten student participants can take part in the carnival competition. The programming language used in designing this application is PHP using the CodeIgniter framework and HTML. For display using CSS3, namely using the Bootstrap and Jquery framework. The database uses MySQL. The tools and editors used are XAMPP for Windows, and Microsoft Visual Studio Code. The final result of this system is to provide a recommendation output ranking participants from the largest alternative value to the smallest. With this system, the Education Department will be helped and made easier in determining competition participants.

Optimization and synthesis on the dynamics performance of the tensioning and relaxing wearable system in a novel knee exoskeleton using co-simulation

Abstract In this paper, a novel tensioning and relaxing wearable system is introduced to improve the wearing comfort and load-bearing capabilities of knee exoskeletons. The research prototype of the novel system, which features a distinctive overrunning clutch drive, is presented. Through co-simulation with ANSYS, MATLAB, and SOLIDWORKS software, a comprehensive multi-objective optimization is performed to enhance the dynamics performance of the prototype. Firstly, the wearing contact stiffness of the prototype and the mechanical parameters of the relevant materials are simulated and fitted based on the principle of functional equivalence. And then, its equivalent nonlinear circumferential stiffness model is obtained. Secondly, to enhance the wearing comfort of the exoskeleton, a novel comprehensive performance evaluation index, termed wearing comfort, is introduced. The index considers multiple factors such as the duration of vibration transition, the acceleration encountered during wear, and the average pressure applied. Finally, through the utilization of this indicator, the system’s dynamics performance is optimized via multi-platform co-simulation, and the simulation results validate the effectiveness of the research method and the proposed wearable comfort index. The theoretical basis for the subsequent research on the effectiveness of prototype weight-bearing is provided.

Deep reinforcement learning-based multi-objective optimization for electricity–gas–heat integrated energy systems

With the increasing global attention on energy efficiency and carbon emissions, the optimization of integrated energy systems (IES) has become the key to improve energy efficiency and reduce pollution emissions. However, most of the existing optimization methods cannot effectively deal with the complexity of high dimensional continuous action space. Therefore, this paper focuses on a novel multi-objective optimization strategy for the electricity–gas–heat integrated energy systems (EGH-IES). Firstly, considering the absorption capacity of wind power and the emission of pollutant gases, a multi-objective optimization model is constructed based on the mechanism model and operation constraints of each device in EGH-IES, in which the integrated operation cost and the environmental factors are taken as optimization objectives. Then, the multi-objective optimization problem is designed as the optimal strategy of interaction learning between agent and environment in reinforcement learning, and the output power of the devices constitutes the action of reinforcement learning. Additionally, the Ornstein–Uhlenbeck process is introduced to enhance the training efficiency and exploration performance of the agent, and the deep deterministic policy gradients (DDPG) algorithm is employed to optimize the action, thus the output power of the appliances could be obtained. Finally, the simulation results show that compared with deep Q network (DQN) method and proximal policy optimization (PPO) method, the reward function value of the proposed method increases by 2.43% and 6.09%, respectively, which represents a reduction in economic cost and pollutant emissions. These verify the effectiveness and superiority of the proposed multi-objective optimization scheme in cost reduction and benefit improvement for the EGH-IES.