Multi-objective Optimization Research Articles

A Design Method for Road Vehicles with Autonomous Driving Control

The past three decades have witnessed extensive studies on motion-planning and tracking-control for autonomous vehicles (AVs). There is, however, a lack of studies on effective design methods for AVs, which consist of the subsystems of the mechanical vehicle, tracking-control, motion-planning, etc. To tackle this problem, this paper proposes a design approach for AVs. The proposed method features a design framework with two layers: at the upper layer, a particle swarm optimization (PSO) algorithm serves as a solver to a multi-objective optimization problem for desired AV trajectory-tracking performance; at the lower layer, a coupled dynamic analysis is conducted among the three subsystems, i.e., a nonlinear model for the mechanical vehicle, a motion-planning module, and a controller based on nonlinear model predictive control (NLMPC) for direction control. The simulation results demonstrate that the proposed method can effectively determine the desired design variables for the NLMPC controller and the mechanical vehicle to achieve optimal trajectory-tracking performance. The research findings from this work provide guidelines for designing AVs.

Multi-objective optimization of school environments to foster nature connectedness using NSGA-III in school design

Multi-objective optimization of school environments to foster nature connectedness using NSGA-III in school design

Evaluation of Link Overstrength Factor for the Seismic Design of Eccentrically Braced Frames

In eccentrically braced frames (EBFs), inelastic behavior is only permitted in the links. All members, except for the links, are designed according to the capacity design concept by using the link overstrength factor, Ω, so that they remain elastic even when the links develop their ultimate strength (including the strain-hardening effect). AISC 341 specifies that the Ω factor of link members must be 1.25 for beam and brace design and 1.1 for column design. In this study, the relevance of the current Ω factor was investigated. A total of 471 K-braced EBF systems with various conditions were designed using a multi-objective optimization technique, and nonlinear dynamic analyses were performed to evaluate the Ω factor. The results indicate that it is reasonable to use the current Ω factor for the design of beam outside link and brace; however, it leads to an overestimation of axial force in columns, especially in the lower stories of tall buildings. From the analysis results, a new Ω factor equation for column design was proposed. It was demonstrated that the structural quantities of 15-story frames designed using the proposed equation decreased by an average of 19% compared to those designed using the current Ω factor.

Advancing SWAT Model Calibration: A U-NSGA-III-Based Framework for Multi-Objective Optimization

In recent years, remote sensing data have revealed considerable potential in unraveling crucial information regarding water balance dynamics due to their unique spatiotemporal distribution characteristics, thereby advancing multi-objective optimization algorithms in hydrological model parameter calibration. However, existing optimization frameworks based on the Soil and Water Assessment Tool (SWAT) primarily focus on single-objective or multiple-objective (i.e., two or three objective functions), lacking an open, efficient, and flexible framework to integrate many-objective (i.e., four or more objective functions) optimization algorithms to satisfy the growing demands of complex hydrological systems. This study addresses this gap by designing and implementing a multi-objective optimization framework, Py-SWAT-U-NSGA-III, which integrates the Unified Non-dominated Sorting Genetic Algorithm III (U-NSGA-III). Built on the SWAT model, this framework supports a broad range of optimization problems, from single- to many-objective. Developed within a Python environment, the SWAT model modules are integrated with the Pymoo library to construct a U-NSGA-III algorithm-based optimization framework. This framework accommodates various calibration schemes, including multi-site, multi-variable, and multi-objective functions. Additionally, it incorporates sensitivity analysis and post-processing modules to shed insights into model behavior and evaluate optimization results. The framework supports multi-core parallel processing to enhance efficiency. The framework was tested in the Meijiang River Basin in southern China, using daily streamflow data and Penman–Monteith–Leuning Version 2 (PML-V2(China)) remote sensing evapotranspiration (ET) data for sensitivity analysis and parallel efficiency evaluation. Three case studies demonstrated its effectiveness in optimizing complex hydrological models, with multi-core processing achieving a speedup of up to 8.95 despite I/O bottlenecks. Py-SWAT-U-NSGA-III provides an open, efficient, and flexible tool for the hydrological community that strives to facilitate the application and advancement of multi-objective optimization in hydrological modeling.

A Review On Multi objective Optimisation of Process Parameters in Shielded Metal Arc Welding For Joining Stainless Steel 3041 And Mild Steel 1018

Due to Mechanical properties of the welding metal and heat affected zone (HAZ) is quality of welding in this research work the review of Multi-objective optimization of welding process parameters for obtaining best weld strength with good mechanical properties of dissimilar metals like stainless steel 3041 and Mild steel 1018 is done. This process used for welding is shielded Metal Arc welding and dissimilar metal are stainless steel 3041 and mild steel 1018. Welding speed, depend on electric property like current Voltage electrode angle, feed rate, Arc length are taken as controlling variables. The weld strength (N/mm2) and Bead geometry variables and Heat Affected Zone are obtained by set of experiment, the possible best outcomes and best method has been chosen by this research. Keywords: HAZ, Multi objective optimization, Response surface Methodology, Shielded metal arc welding

Three-Dimensional Turning Force Sensor Based on a Decagonal Ring Structure: Crosstalk Mitigation Design and Performance Optimization

Abstract Real-time data acquisition using high-precision three-dimensional turning force sensors is crucial for intelligent manufacturing. However, existing sensors face challenges such as low integration levels and poor crosstalk resistance, rendering them inadequate for high-precision measurements. Theoretical analysis has demonstrated that a fully centered decagonal ring structure can effectively mitigate the effects of eccentric loading. This study focuses on designing a fully centered turning force sensor with minimal crosstalk. To achieve a balance between sensitivity and stiffness, multi-objective optimization was conducted using GWO-BP and TOP algorithms, resulting in a twofold increase in the sensitivity of the decagonal ring. The sensor design incorporates metallic strain gauges, instrumentation amplifiers, and peripheral circuits into the ring arms of the decagonal structure. Experimental results show that the sensor’s amplified sensitivities in the Fc, Ff, and Fp directions are 11.78 mV/N, 10.31 mV/N, and 1.78 mV/N, respectively. The first three natural frequencies in an unconstrained state are 2518.5 Hz, 2537.5 Hz, and 4256.4 Hz. The sensor's Fc-Fy crosstalk ranges from 0.18% to 0.88%, with noise limits for the Fc, Ff, and Fp directions of 0.035 N, 0.03 N, and 0.23 N, respectively. Cutting experiments have demonstrated that under varying spindle speeds and cutting depths, the sensor effectively detects surges in cutting force caused by rapid tool retraction, as well as other common faults.

Energy-efficient and reliable coordinated scheduling for water distribution systems: enhancing hydraulic conditions and water quality

ABSTRACT Multi-objective operation optimization of water distribution systems (WDSs) in large metropolitan areas is essential; however, it is complex and time-consuming. Effective and reliable scheduling of WDSs can significantly enhance the efficiency and reliability of urban water resources management, which requires further research. This paper develops an optimization method combining genetic algorithm and multiple criteria analysis that coordinates energy conservation, hydraulic condition improvement, and water age optimization in WDSs. Results showed that the optimal scheduling method could achieve a 23.0, 16.7, and 2.5% decrease in energy consumption, and water supply volume with unsatisfied pressure, and average water age, respectively. To achieve optimal results, this study indicated that it is crucial to properly allocate water supply volume and pressure across multiple drinking water treatment plants. This finding provides important guidance for water utilities in scheduling. Furthermore, the emergency scenario analysis shows that the newly proposed method can significantly enhance the social and economic sustainability of WDSs through the coordinated optimization of energy consumption, hydraulic conditions, and water age.

Optimization of machining efficiency and side quality in irregular sheet metal parts milling based on improved multi-objective seagull optimization algorithm

Optimization of machining efficiency and side quality in irregular sheet metal parts milling based on improved multi-objective seagull optimization algorithm

Cooperative, collaborative, coevolutionary multi-objective optimization on CPU-GPU multi-core

Cooperative, collaborative, coevolutionary multi-objective optimization on CPU-GPU multi-core

An enhanced competitive swarm optimizer with strongly robust sparse operator for large-scale sparse multi-objective optimization problem

In the real world, the decision variables of large-scale sparse multi-objective problems are high-dimensional, and most Pareto optimal solutions are sparse. The balance of the algorithms is difficult to control, so it is challenging to deal with such problems in general. Therefore, An Enhanced Competitive Swarm Optimizer with Strongly Robust Sparse Operator (SR-ECSO) algorithm is proposed. Firstly, the strongly robust sparse functions which accelerate particles in the population better sparsity in decision space, are used in high-dimensional decision variables. Secondly, the diversity of sparse solutions is maintained, and the convergence balance of the algorithm is enhanced by the introduction of an adaptive random perturbation operator. Finally, the state of the particles is updated using a swarm optimizer to improve population competitiveness. To verify the proposed algorithm, we tested eight large-scale sparse benchmark problems, and the decision variables were set in three groups with 100, 500, and 1000 as examples. Experimental results show that the algorithm is promising for solving large-scale sparse optimization problems.

A dynamic multi-objective optimization evolutionary algorithm based on classification of environmental change intensity and collaborative prediction strategy

A dynamic multi-objective optimization evolutionary algorithm based on classification of environmental change intensity and collaborative prediction strategy

Optimization for fuel consumption and TCO of a heavy-duty truck with electricity-propelled trailer

As renewable energy grows in the heavy-duty truck sector, exploration of diverse solutions with varied energy types and powertrain configurations becomes a necessity. While most studies focus on powertrain configurations in tractors, this study takes a different approach: propose and evaluate new plug-in hybrid configurations with multiple power sources for heavy-duty trucks. The tractor retains its engine powertrain, while the trailer is equipped with electric axle(s), forming a fuel-electricity hybrid propulsion system. The specifications of the electric propulsion system are optimized, using a multi-objective particle swarm optimization algorithm and dynamic programming control algorithm to evaluate the energy consumption and total cost of ownership (TCO). The results highlight the potential of the configuration with three electric axles and single reduction gear in regard to vehicle energy consumption. Conversely, the configuration with one electric axle and a two-speed transmission exhibits the lowest TCO. Under the China World Transient Vehicle Cycle, the fuel consumption of the diesel truck is reduced by up to 44.59 % with the use of the optimized hybrid configuration. The truck's TCO can be then reduced by up to 832 thousand CNY in one-million-kilometer operation. For an actual road operation, these figures change to 34.79 % and 284 thousand CNY, respectively.

Optimized Resource Allocation for Sustainable Development in Beijing: Integrating Water, Land, Energy, and Carbon Nexus

Chinese megacities face significant challenges in reducing carbon emissions while balancing economic growth and social welfare. This study constructs an innovative multi-objective optimization model, the SD-NSGA-III model, integrated with a System Dynamics (SD) model and using the Non-dominated Sorting Genetic Algorithm III (NSGA-III) to optimize resource allocation in Beijing. The model targets environmental, economic, and social goals by establishing a water–land–energy–carbon (WLEC) nexus for analyzing resource allocation strategies and carbon reduction pathways under various constraints. Scenario simulations under the efficiency-oriented scenario indicated a potential reduction in energy carbon emissions of 81.4% by 2030. The fairness-oriented scenario revealed significant trade-offs between equity and emission reductions, emphasizing the need for balanced strategies. Introducing constraints on resources and economic growth significantly reduced median energy carbon emissions to 80 million tons by 2030. These findings demonstrate the effectiveness of the SD-NSGA-III model in providing actionable strategies for achieving carbon neutrality and sustainable development in cities.

Determining method of optimal process parameters using Taguchi method and technique for order preference by similarity to ideal solution in plastic injection molding

Many plastic injection molding (PIM) process optimization problems are multi-objective optimization problems that optimize multiple mechanical properties. This paper proposed a determining method of optimal process parameters and their effect ranks using Taguchi method and TOPSIS in the PIM. TOPSIS was used to convert multiple mechanical properties into single comprehensive response. It was selected as a reasonable multi-attribute decision-making (MADM) method from among some well-known MADM methods based on mean rank correlation coefficient and mean absolute rank deviation. Taguchi method was used to design experiment and find the optimal process parameters. The proposed method was applied to determine the optimal values of the process parameters: melt temperature (MT), packing pressure (PP), cooling time (CT), and injection pressure (IP) for improving tensile strength, elasticity module, flexural modulus, and impact strength in the PIM with Acrylonitrile-Butadiene-Styrene compound as plastic materials and AISI 1020 as mold materials. The optimal values of the process parameters were MT of 280°C, PP of 28 MPa, CT of 22 s, and IP of 50 MPa, and their effect ranking was IP (51.941%), PP (32.280%), CT (9.045%), and MT (6.734%). The method could be widely applied to not only the PIM process optimization but also various manufacturing process optimization problems.

Optimization study on circadian tunability and Rec. 2020 of RGB-LED-based displays considering the users with different ages

The reasonable design of spectral power distribution (SPD) of GaN-based light-emitting diode (LED) display becomes more and more important and popular. In this work, we perform an investigation on the circadian effects of three-primary (red R, green G, and blue B, also denoted as RGB) LED based self-luminous displays (such as emerging mini-LED or micro-LED based displays) considering the users with different ages. With the increment of peoples’ ages from 1 year to 100 years, the optical transmittance of humans’ eyes may decrease due to the aging of their body functions. Therefore, it is important and meaningful for performing a spectral optimization study on the circadian effects of light that is emitting from self-luminous LED-based displays for the users with different ages. Here, the melanopic efficacy of luminous radiation (MELR) is mainly used to evaluate the circadian effects of white light coming from RGB-LED displays. With the aid of multi-objective optimization genetic algorithm (MOGA), the maximum MELR and the minimum MELR value are separately obtained for the standard observers at the age of 32, while maintaining at a certain high color gamut of Rec. 2020 standard (i.e., 60 %, 70 %, 80 %, and 90 % Rec. 2020, respectively) for these LED-based displays. Then, an extrapolation of optimal SPD at the age of 32 is done to other ages. Another parameter that can be denoted as circadian stimulus (CS) is also calculated and discussed for different ages. It is believable that this work is helpful for guiding the design and fabrication of future RGB-LED-based displays in consideration of the users with different ages, meeting large requirements of human-centric lighting (HCL) in the future.

On formal modeling, analysis and optimization of reconfigurable manufacturing systems

ABSTRACT This paper deals with the formal specification and multi-objective optimization of reconfigurable manufacturing systems (RMSs). RMSs are characterized by their dynamic structure, crucial for responding to functional variations, adjusting production capacity and overcoming operational dysfuntions promptly and cos-effectively. The effective utilization of this dynamic capability depends on thorough study and optimization of RMS configurations. To tackle this challenge, a novel formalism called genetic reconfigurable object nets (Gen-RONs) is introduced. Gen-RONs formalism integrates the modeling, verification and reconfiguration capabilities of reconfigurable object nets with advanced multi-objective techniques from genetic algorithms. Within Gen-RONs, Petri nets serve to: (i) encode RMS configurations, (ii) manage RMS scheduling and (iii) employ graph transformation theory rules as genetic algorithm operators for real-time system reconfiguration. Thus, Gen-RONs formalism provides a robust framework for optimizing RMSs using genetic algorithms. To ensure deadlock-free RMS configurations, Gen-RONs incorporate a binary tree-based technique. Furthermore, Taguchi’s method is employed to fine-tune genetic algorithm parameters. The contribution of this paper is demonstrated through a case study involving an RMS and the widely used genetic algorithm NSGA-II. Significant gains in terms of the number of solutions (i.e. configurations) and the criteria values are achieved.

Multi-objective optimization towards cooling performance of teardrop section Kagome trusses array jet impingement composite cooling structure with cross-flow

Teardrop Section Kagome trusses array can be a spoiler for a new generation of advanced combustion chambers. In this study, multi-objective optimizations are applied to optimize a jet impingement composite cooling structure with cross-flow. Four structural parameters with five levels and three performance parameters are calculated in design of experiment and fitted by response surface methodology. The database is expanded by non-dominated sorting genetic algorithm-II, and then technique for order preference by similarity to an ideal solution is used to calculate the optimal structures under different operating conditions by different weights of the performance parameter. The conclusions are as follows: The minimal Equality of variances and fitting accuracy are 35.4371 and 93.6%. Diameter of the cross-section has the largest positive main effect on average Nusselt number and pressure loss coefficient, while angle between the rod and the horizontal plane has the largest positive effect on temperature uniformity coefficient. The interaction of trailing edge angle with angle between the front rod and the back rod in the horizontal plane is large. Case15 is chosen, where the weight ratio is 1:5:5, improvement of the corresponding optimized performance parameters of the simulation results with respect to the Origin of three performance parameters are 26.72%, −2.93%, 91.59% respectively. It provides a feasible reference for future advanced combustion chambers and accelerates their development process.

Pore-Scale multi-objective shape optimization of triply periodic minimal surface cellular architectures: Volumetric Nusselt number versus friction factor

Triply Periodic Minimal Surface (TPMS) cellular architectures, i.e., minimal surfaces that are periodic in three independent directions, have attracted interest in the engineering community for their potential in heat transfer applications with severe volume constraints due to their high surface-to-volume ratio, unique geometrical properties and smooth porous structure. Such performance can be conveniently controlled by tuning the TPMS unit type, relative density, and structural parameters. In this study, a multi-objective optimization process is carried out to investigate how pore-scale geometric characteristics affect the heat transfer performance of TPMS, and which combinations result in an optimal trade-off between heat dissipation capability and associated pressure drop in terms of the Nusselt number and friction factor. The analysis is performed using an in-house Genetic Algorithm routine that generates the structure, allowing for a wider and denser range of investigation, and incorporates a commercial finite element CFD software as a black box fitness function. Over 14,000 unique combinations between two heat transfer boundary conditions (constant wall heat flux, fixed wall temperature) and several flow inlet conditions are investigated. The optimization achieved an increase in performance of up to 245 % in terms of volumetric Nusselt number, which was accompanied, however, by a significant, although reasonable, increase in friction factor values. The results also show specific trends among the geometric characteristics studied, which are then contextualized through the use of CFD analysis. Finally, empirical correlations are derived to cluster the non-dominated solutions obtained, enabling the reader to choose the best cellular TPMS structure by selecting the desired level of trade-off between convection heat transfer and flow resistance.

Multi-objective spectral range optimization for different heat source temperatures to maximize thermophotovoltaic performance using NSGA-II

The energy conversion efficiency (ECE) and output power density of thermophotovoltaic (TPV) cells are mutually constraining. To better utilize this relationship, a framework was proposed to integrate the internal quantum efficiency (IQE) of TPV cells with a multi-objective genetic algorithm (NSGA-II) to optimize both efficiency and power density across diverse operating conditions. A spectrally selective emitter with a temperature of 1000–3000 K was selected as the radiation source for the studied TPV devices, capable of emitting a spectrum between 0.4 and 2.0 μm. The gallium antimonide (GaSb) cell was selected as the exploration cell, operating within a temperature range of 0–200 °C. Through theoretical calculation, the changes in physical parameters and IQE curves of GaSb cells at various temperatures were determined. The optimal spectral range for varying cell and emitter temperatures was determined using the NSGA-II algorithm. It was found that the IQE curve decreases with increasing temperature. Due to the spectral mismatch, the TPV conversion efficiency is much less than 20 % when the spectral range of the cell is 0.4–2.0 μm at room temperature. By incorporating IQE into the multi-objective optimization, the efficiency and power density distribution curves can be divided into three regions. In practical applications, the corresponding regions can be selected based on different efficiency and output power requirements.

Enhancing disaster management through multi-objective water wave optimization for medical supplies storage and distribution

This paper conducts a comparative analysis of advanced methodologies aimed at addressing the intricate task of scheduling medical supplies in both civilian and military sectors for epidemic prevention and control. This study introduces a multi-objective water wave optimization (MOWWO) algorithm and enhance its efficacy by incorporating a dynamically adjusted component to the metaheuristic approach (DAMOWWO). The primary goal of this research is to assess the proposed approach in contrast to established state of the art methods with similar objectives. The aim of this study is to optimize multiple aspects simultaneously, including the overall satisfaction rates of medical supply delivery and the reduction of scheduling costs, while ensuring a minimum military supply reservation ratio. This paper offers a comprehensive evaluation of the MOOW algorithm, emphasizing its potential applications in emergency response scenarios.