Multi-objective Optimization Method Research Articles

Accelerated inverse urban design: A multi-objective optimization method to photovoltaic power generation potential, environmental performance and economic performance in urban blocks

Accelerated inverse urban design: A multi-objective optimization method to photovoltaic power generation potential, environmental performance and economic performance in urban blocks

Multi-Objective Optimization of Injection Molding Process Parameters for Junction Boxes Based on BP Neural Network and NSGA-II Algorithm

Many factors affect the quality of the injection molding of plastic products, including the process parameters, mold materials, type and geometry of plastic parts, cooling system, pouring system, etc. A multi-objective optimization method for injection molding process parameters based on the BP neural network and NSGA-II algorithm is proposed to address the problem of product quality defects caused by unreasonable process parameter settings. Taking the junction box shell as the object, numerical simulation was carried out using Moldflow2019 software and a six-factor five-level orthogonal experiment was designed to explore the influence of injection molding process parameters, such as the mold temperature, melt temperature, injection pressure, holding pressure, holding time, and cooling time, on the volume shrinkage rate and warpage deformation of the junction box. Based on a numerical simulation, the BP neural network and NSGA-II algorithm were used to optimize the optimal combination of injection molding process parameters, volume shrinkage rate, and warpage deformation. The research results indicate that the melt temperature has the most significant impact on the quality of the injection molding of junction boxes, followed by the holding time, holding pressure, cooling time, injection pressure, and mold temperature. After optimization using the BP neural network and the NSGA-II algorithm, the optimal process parameter combination was obtained with a melt temperature of 230.03 °C, a mold temperature of 51.27 °C, an injection pressure of 49.13 MPa, a holding pressure of 69.01 MPa, a holding time of 15.48 s, and a cooling time of 34.91 s. At this time, the volume shrinkage rate and warpage deformation of the junction box were 6.905% and 0.991 mm, respectively, which decreased by 33.2% and 3.8% compared to the average volume shrinkage rate (10.34884%) and warpage deformation (1.030764 mm) before optimization. The optimization effect was significant. In addition, the errors between the volume shrinkage rate and warpage deformation predicted by BP-NSGA-II and the simulated values using Moldflow software were 1.9% and 3.4%, respectively, indicating that the optimization method based on the BP neural network model and NSGA-II algorithm is reliable.

Path planning and optimization for transmission line barrier operations in complex terrain based on multimachine collaborative control

Abstract To address the challenges of path planning for transmission line barrier operations in complex terrains, this paper proposes a solution based on multimachine collaborative control. A path-planning algorithm that integrates multiple classical algorithms and incorporates multiobjective optimization methods is constructed to handle the complexities of challenging terrains. Auction algorithm and genetic algorithm are adopted to achieve optimal load balance by dynamically adjusting task allocation. Simulation results show that the proposed method effectively enhances task execution efficiency, optimizes path planning, and reduces energy consumption in complex terrains and dynamic environments, while maintaining high-obstacle avoidance success rates and communication stability.

Multi-Objective Optimization Design of PCS Box Girder Bridges with Small and Medium Spans Using Genetic Algorithms

With the development of algorithms for autonomous decision-making in the field of structural engineering, the design of precast concrete segment (PCS) box girder bridges faces new challenges. This paper proposes using a multi-objective optimization method based on genetic algorithms for the rapid design of PCS box girder bridges with small and medium spans. By considering 20 design parameters such as the physical dimensions of the box girder cross-section, material properties, and prestressing parameters, the paper formulates and quantifies three objective functions: cost, safety, and structural performance. The multi-objective optimization was conducted using four optimization algorithms (NSGA-II, NSGA-III, GDE3, and PSO). An optimization evaluation index (φ[F(x)]) was established and weights were assigned to different optimization objectives. A specific design case based on the general diagram of a 3 × 25 m-long continuous PCS box girder bridge was carried out. The results indicate that genetic algorithms performed exceptionally well on this problem, with the NSGA-III algorithm achieving the best φ[F(x)] value of 0.2789 among all algorithms. A performance analysis was conducted on various optimization models using box plots and sensitivity studies. Scatter plots and surface plots of the Pareto front of the optimized solutions were generated, and corresponding cross-sectional design drawings were created based on the two proposed solutions. Compared with the general graph, the design cases provided by the NSGA-III algorithm model have a change rate of 8.03%, −0.29%, and 75.49% in the three optimization objectives, respectively, indicating a significant improvement effect. The research content of this paper provides a reasonable direction for future studies on intelligent bridge design methodologies.

Integrated fault estimation and control for unknown discrete-time systems: a data-based multivariable-coordinated optimisation method

This paper addresses the integrated fault estimation and robust control problem for unknown linear discrete-time systems. The considered problem is formulated as a multivariable multiobjective optimisation one. A data-based coordinated design strategy that co-designs an output feedback H ∞ / H ∞ controller and a residual generator is proposed to optimise both H ∞ fault estimation and robust control performances. Based on the input and output data, the design parameters of the controller and the residual generator are determined by using Q-learning technique and introducing a new matrix block identification Q-learning method, respectively. Compared with the existing single-variable multiobjective optimisation methods, the proposed strategy can achieve better fault estimation performance, remove the restriction condition of using input and output data, and extend the traditional Q-learning technique to the case of designing a residual generator with a low-rank condition. Finally, simulation results illustrate the effectiveness of the proposed method.

Integrated BIM + Parametric Modelling: A decision-support tool for human-centric campus simulation, automation, and optimization

Developing masterplans of large-scale areas such as campuses involves formulating multiple scenarios, considering various social, functional, environmental, and aesthetic factors, and pursuing multi-objective optimization to fulfill various conflicting quantifiable and non-quantifiable design objectives. However, current practice lacks an integrated methodology that enables automatic evaluation of those scenarios and visualization of their results to stakeholders. Using computer-performed simulations with innovative design methods to dynamically investigate and empirically assess how campus characteristics influence its inclusiveness, users’ behavior, and social interactions can help identify designs that stimulate human-centered principles. This paper introduces the integrated parametric BIM-based campus life simulation as a decision-support tool. It unifies the design and analysis cycles with scenario generation and design automation through a human-centered methodology. The supports interactive design processes that involve space exploration, analysis, and optimization, allowing the usage of multi-objective optimization methods to develop detailed design alternatives that fulfill conflicting quantifiable and non-quantifiable objectives. Thus, it functions as a descriptive and predictive tool and supports theory-testing and development.

Optimization of Ultra Lightweight Mirror and Opto-Mechanical-Thermal Coupling Analysis Based on Solar Thermal Radiation.

To improve maneuverability, the focus of photoelectric theodolites is on reducing the weight of the primary mirror and enhancing its optical performance. This study uses MOAT and Sobol methods to identify key parameters that affect design. Using the high-sensitivity part as the optimization domain, six optimization results were obtained based on the multi-objective SIMP topology optimization method and synthesized into a compromise optimization structure. The performance of the mirror before and after optimization was compared on the opto-mechanical-thermal level. Modal analysis shows the optimized structure has a first natural frequency of 716.84 Hz, indicating excellent stiffness and avoiding low-frequency resonance, with a 30.37% weight reduction. Optical performance is also improved, with a 6 μm reduction in the spot diagram radius and an 8.95 nm decrease in RMS. Simulations under real-world conditions show that the lightweight mirror performs better in resisting gravity deformation and maintaining imaging quality. At maximum thermal deformation, the spot diagram radius of the optimized mirror is 1521.819 μm, with only a 0.145% difference in imaging quality compared to the original. In conclusion, the optimized structure shows comprehensive advantages. Constructing the optical system components and the real physical environment of the site provides a valuable reference for the optimization and analysis of the mirror.

Survey of interactive evolutionary decomposition-based multiobjective optimization methods.

Interactive methods support decision-makers in finding the most preferred solution for multiobjective optimization problems, where multiple conflicting objective functions must be optimized simultaneously. These methods let a decision-maker provide preference information iteratively during the solution process to find solutions of interest, allowing them to learn about the trade-offs in the problem and the feasibility of the preferences. Several interactive evolutionary multiobjective optimization methods have been proposed in the literature. In the evolutionary computation community, the so-called decomposition-basedmethods have been increasingly popular because of their good performance in problems with many objective functions. They decompose the multiobjective optimization problem into multiple sub-problems to be solved collaboratively. Various interactive versions of decomposition-based methods have been proposed. However, most of them do not consider the desirable properties of real interactive solution processes, such as avoiding imposing a high cognitive burden on the decision-maker, allowing them to decide when to interact with the method, and supporting them in selecting a final solution. This paper reviews interactive evolutionary decomposition-based multiobjective optimization methods and different methodologies utilized to incorporate interactivity in them. Additionally, desirable properties of interactive decomposition-based multiobjective evolutionary optimization methods are identified, aiming to make them easier to be applied in real-world problems.

An Adaptive Voltage Reference-Based Multi-Objective Optimal Control Method for the Power Flow Symmetry of Multi-Terminal DC Systems with the Large-Scale Integration of Offshore Wind Farms

The optimization of the symmetry of MTDC systems after a contingency is crucial for the stable and economic operation of the MTDC systems. In this paper, a multi-objective optimal control method for the power flow symmetry of MTDC systems for the large-scale integration of offshore wind farms is proposed. A mirror relationship between the available headroom of DC lines and VSCs and their actual power flow distribution performance is established. A corresponding symmetry index is established for the MTDC network, and the multi-objective optimization problem is converted into a series of single-objective problems by the normal boundary intersection method, and solved by the original dyadic interior point method, so as to obtain the Pareto optimal solution with uniform distribution. The compromise optimal solution is decided according to the entropy weight double-basis point method, which provides decision-making guidance for the operators. The simulation results show that the normal boundary intersection method can solve the multi-objective dynamic optimal control problem of the VSC-HVDC system quickly and efficiently, and improve the symmetry of the power flow in an MTDC network.

A novel Kriging-assisted multi-objective optimisation method considering infeasibility ratio under input uncertainty

Many real-life engineering applications come with various sources of uncertainty. Uncertainty can also impact the feasibility of solutions in constrained optimisation. Classic robust optimisation approaches often significantly increase the computational burden and might overlook different tolerable risks, resulting in conservative sub-optimal solutions. We reformulate the classic constrained problem as a multi-objective optimisation problem and propose a Kriging-assisted multi-objective optimisation method that trades off Infeasibility Ratio (IR) with robust performance: (1) a formulation to approximate IR is developed and introduced as an additional objective and (2) an acquisition function balancing exploration (refining feasible boundaries) and exploitation of promising areas with multiple designs is proposed. Furthermore, a new quality metric has been developed to effectively measure the convergence and diversity of the obtained Pareto solution set. The proposed framework is tested on five numerical problems and applied to an engineering case: the design of a honeycomb vibration isolator. A comparison study with other optimisation methods is conducted to verify its effectiveness and performance with promising results.

Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty

We present a multi-objective Bayesian active learning strategy, which greatly accelerates the discovery of super high-strength and high-ductility lead-free solder alloys. The active learning strategy demonstrates that a machine learning model will have high generalizability if experimental data uncertainty is included, which greatly improves the model prediction or the material design accuracy. The feature-point-start forward method in multi-objective optimization adopts two Gaussian process regression (GPR) models, one for strength and one for elongation, and their outputs build up the acquisition-function-modified objective space of strength and elongation. Then, Bayesian sampling is applied to design the next experiments by balancing exploitation and exploration. Seven multi-objective active learning iterations discovered two novel super high-strength and high-ductility lead-free solder alloys. After that, various material characterizations were conducted on the two novel solder alloys, and the results exhibited their high performances in melting properties, wettability, electrical conductivity, and shear strength of the solder joint and explored the mechanism of high strength and high ductility of the alloys. The present work systematically analyzes the important role of experimental uncertainty in machine learning, especially in the global optimization for material design, which demands high generalizability of predictions.

Improvements to steepest descent method for multi-objective optimization

Improvements to steepest descent method for multi-objective optimization

A multi-objective optimization method based on internal search algorithm for wind energy access to rural microgrid power supply grid architecture

A multi-objective optimization method based on internal search algorithm for wind energy access to rural microgrid power supply grid architecture

A Review of Multi-Objective Optimization Methods of Grid-Connected Hybrid Renewable Energy Systems Combined with Electrical Vehicle Technique

Renewable Energy Sources (RESs) are regarded as highly promising and rapidly advancing forms of renewable energy. The commonly used RESs are solar energy and wind energy. However, obstacles are found in designing RESs. Using vehicle-to-grid (V2G) technology in combination with electric vehicles (EVs) and RESs in smart grid are of great significance for ensuring energy security, preventing air pollution, and promoting energy saving and emission reduction. Over the past decade, V2G technology has enabled EVs to become a potential energy storage capacity for alleviating the random fluctuation in renewable energy generation. Nevertheless, the primary drawbacks of these systems are inefficient energy conversion and substantial initial investment. The sizing of each piece of equipment in the hybrid renewable energy system (HRES) is challenging. Hence, it is imperative to employ a precise sizing technique to determine an ideal arrangement and meet the requisite load requirements. Hence, before the installation of RESs, it is crucial to consider the types and arrangements of photovoltaic (PV) panels and wind turbines (WTs), mathematical models of PV modules and WTs, storage battery options, environmental-economic-technical considerations, sizing methods based on techno-economic objectives, and the ultimate selection of the most optimal configuration. This work lists the general classification of optimization formulation framework, and multi-objective optimization methods of HRESs integrated with electrical vehicle technique are reviewed. This workprovided a thorough examination of the current advancements in the design optimization of HRESs using multi-objectiveoptimization (MOO). This study aims to select the most appropriate design before installing HRESs and provides afoundational platform for scholars interested in exploring the integration of RESs with EVs for further advancement.

Multi-Objective Economic Dispatch Problems: An Evolutionary Programming Approach

Optimizing multiple objectives is a common challenge in complex decision-making scenarios, where improving one objective often comes at the expense of another. In power system generation, two critical objectives must be simultaneously minimized: the total generation cost and the total emissions released. This study introduces a multi-objective optimization method developed to address these dual objectives. The proposed approach aims to identify the optimal solution to the economic dispatch problem, balancing the trade-off between minimizing generation costs and reducing environmental impact by lowering total emissions. Heuristic optimization is used to solve this multi-objective combined economic and emission dispatch (MOCEED) problems through the implementation of Multi-Objective Evolutionary Programming (MOEP). In this study, the Weighted Sum Method (WSM) is combined with the Evolutionary Programming (EP) technique to minimize both objectives. The developed approach is validated on the IEEE 30-Bus RTS, which consists of six generators. The effectiveness of the developed technique is evaluated and compared the results against scenarios without optimization. Simulations are performed using MATLAB software, and the results demonstrate that the proposed Multi-Objective Evolutionary Programming (MOEP) successfully identifies the optimal generator outputs, achieving significant reductions in both total generation cost and emissions. These results highlight the method's capability to provide superior solutions for multi-objective economic dispatch problems.

Multi-Objective Optimization Accelerates the De Novo Design of Antimicrobial Peptide for Staphylococcus aureus.

Humans have long used antibiotics to fight bacteria, but increasing drug resistance has reduced their effectiveness. Antimicrobial peptides (AMPs) are a promising alternative with natural broad-spectrum activity against bacteria and viruses. However, their instability and hemolysis limit their medical use, making the design and improvement of AMPs a key research focus. Designing antimicrobial peptides with multiple desired properties using machine learning is still challenging, especially with limited data. This study utilized a multi-objective optimization method, the non-dominated sorting genetic algorithm II (NSGA-II), to enhance the physicochemical properties of peptide sequences and identify those with improved antimicrobial activity. Combining NSGA-II with neural networks, the approach efficiently identified promising AMP candidates and accurately predicted their antibacterial effectiveness. This method significantly advances by optimizing factors like hydrophobicity, instability index, and aliphatic index to improve peptide stability. It offers a more efficient way to address the limitations of AMPs, paving the way for the development of safer and more effective antimicrobial treatments.

Optimization of the Metal Injection Molding Process with 3l6L Stainless Steel Powder and Influence Analysis of Process Parameters Using the Taguchi-MADM-Based Hybrid Method.

Metal injection molding (MIM) is an advanced manufacturing technology for producing complex metal parts with precise dimensions. Multiattribute decision making (MADM) can convert multiple quality attributes into a single overall quality score (OQS). To improve multiple quality attributes of the MIM compacts, a reasonable multiobjective optimization method should be applied. This paper proposes a Taguchi-MADM-based hybrid optimization method of the MIM process. It consists of the following main steps: (1) designing the experiment using a Taguchi orthogonal array, (2) conducting the experiment and measuring multiple quality attributes at each experimental trial, (3) calculating the OQS values at every experimental trial using some popular MADM methods, (4) selecting the best suitable MADM method from among some MADMs, and (5) determining optimal MIM process parameters to maximize the OQS obtained from the selected MADM using the Taguchi method. By using this method, this work determines the optimal MIM process parameters (injection pressure, injection temperature, powder loading, mold temperature, holding pressure, and injection speed) for improving multiple quality attributes (flexural strength, density, and surface appearance) of the MIM compacts with 3l6L stainless steel powder. The proposed method could be actively applied to many practical multiple quality attribute optimization problems in the manufacturing industry.

Group Decision Making in Multiobjective Optimization: A Systematic Literature Review

AbstractGroup decision making has been studied from several viewpoints and a variety of methods has been proposed. However, in the literature on solving multiobjective optimization problems, the main focus has been on supporting a single decision maker. We conducted a systematic literature review to examine and synthesize the state-of-the-art of multiobjective optimization methods developed for group decision making. We analyze group decision making methods of multiobjective optimization according to how preferences of several decision makers are incorporated into the solution process, how to select the most preferred solution for the group, different types of decision makers, types of groups and how the group is to operate during the solution process. In addition, we identify the key issues in the literature that are required to be considered in further method development to increase the methods’ applicability in solving real-world problems. Finally, we guide how to select a method for solving real-world multiobjective optimization problems with multiple decision makers and suggest future research directions.

Preload Multi-Objective Optimization Method for Ultrasonic Motors Based on NSGA-II

The preload has a significant impact on the output performance of ultrasonic motors. To achieve optimal output from the ultrasonic motor, this paper proposes a novel multi-objective optimization method for preload, utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve the problem of determining the optimal preload. Firstly, a platform for preload adjustment and testing is established to conduct experimental tests on the motor’s characteristics. Based on the experimental data, the influence of preload on the motor’s output performance is analyzed. Secondly, linear regression models are established for three performance indicators: no-load speed, stall torque, and output efficiency with respect to preload, and the objective functions are fitted accordingly. Finally, the NSGA-II algorithm is used for multi-objective optimization with three objectives to obtain the Pareto front and determine the optimal preload for the ultrasonic motor. Simulation and experimental results show that after preload optimization, the no-load speed increased by 4.93%, stall torque increased by 26.10%, and output efficiency increased by 11.28%. Compared to existing optimization methods, this approach has lower computational complexity and better optimization performance, ensuring higher optimization accuracy and precision. The preload optimization method based on NSGA-II can improve the performance of ultrasonic motors in practical applications.

Multi-objective optimization of building envelope of rural housing in the severely cold region of China based on energy consumption, thermal comfort and cost-effectiveness

The performance of rural residential buildings has a great energy-saving potential due to the great spontaneity and ignorance of the rural residential construction. To solve this problem, this paper provides a multi-objective optimization model based on Chebyshev inequality. The model takes energy consumption, indoor thermal comfort, project cost as optimization objectives and considers operating conditions in winter and summer. This study taking a rural residential house as an example and analysed the influence of its envelope parameters on building performance using EnergyPlus. The parameters of the building envelope were optimized, which verified the superiority of the method. Optimization results showed that the heating energy consumption was reduced by 29%, and the air conditioning energy consumption was reduced by 6%. This study analysed the relationship between the building energy consumption, indoor thermal comfort and the cost by multi-objective optimization method, to provide a new research idea for rural residential research. Optimal design solutions were generated under various weighting coefficients. The model aimed to meet the climatic and economic conditions of northern China and similar regions. Therefore, based on these optimization results, decision-makers can choose optimal design and construction solutions according to their own will.