Single-objective Optimization Method Research Articles

A Scoping Review on Simulation-Based Design Optimization in Marine Engineering: Trends, Best Practices, and Gaps

AbstractThis scoping review assesses the current use of simulation-based design optimization (SBDO) in marine engineering, focusing on identifying research trends, methodologies, and application areas. Analyzing 277 studies from Scopus and Web of Science, the review finds that SBDO is predominantly applied to optimizing marine vessel hulls, including both surface and underwater types, and extends to key components like bows, sterns, propellers, and fins. It also covers marine structures and renewable energy systems. A notable trend is the preference for deterministic single-objective optimization methods, indicating potential growth areas in multi-objective and stochastic approaches. The review points out the necessity of integrating more comprehensive multidisciplinary optimization methods to address the complex challenges in marine environments. Despite the extensive application of SBDO in marine engineering, there remains a need for enhancing the methodologies’ efficiency and robustness. This review offers a critical overview of SBDO’s role in marine engineering and highlights opportunities for future research to advance the field.

Optimal design of mixed dielectric coaxial-annular TSV using GWO algorithm based on artificial neural network

The single-objective and single-parameter optimization method is commonly used in the structure optimization of TSV to improve the transmission characteristics, for which a structure design scheme that simultaneously satisfies multiple target requirements is difficult to obtain. Moreover, the method cannot simultaneously optimize different design parameters. Aiming at the above problems, a global optimization method based on the gray wolf optimization (GWO) algorithm and artificial neural network (ANN) model is proposed. With the presented mixed dielectric coaxial-annular TSV model, firstly six key design parameters A-F are selected as optimization variables by the control variable method. The L25(56) orthogonal experiment is designed for Taguchi analysis and analysis of variance (ANOVA). Then, three prediction models, ANN, support vector machine (SVM), and extreme learning machine (ELM), are developed with the extended orthogonal data as the training sets. It is found that the ANN model performed best. To search for the global optimal solution, the genetic algorithm (GA) and GWO algorithm, combined with the ANN model are applied, respectively. The results show that the GWO algorithm is more successful in solving the problem of falling into the local optimum than GA, and the convergence speed is faster and more stable. After GWO-ANN optimization, the performance of each S-parameter index is greatly improved, S11 reduces by 14.05 dB, S21 increases by 0.33 dB, and S31 reduces by 12.50 dB at 30 GHz.

A New Multi-Objective Optimization Strategy for Improved C3MR Liquefaction Process

In the traditional C3MR process (T-C3MR), the boiling gas (BOG) output from the last stage of the gas–liquid separator is directly discharged, in which the excellent low-temperature capability is not utilized, and the system efficiency is decreased. In liquefied natural gas (LNG), single-objective optimization methods are commonly used to optimize system parameters, which may result in incomplete system analysis. To solve the above problems, this paper proposes a multi-objective optimization strategy for the improved C3MR process(I-C3MR) based on a new multi-objective optimization algorithm called EHR-GWO-GA. Firstly, the main work proposes an I-C3MR structure. Secondly, an optimization strategy of the I-C3MR with the maximization of liquefaction amount, minimization of unit energy consumption and minimization of exergy loss as objective functions are proposed. Based on the optimization results, the influence of decision variables on liquefaction amount, unit energy consumption and exergy loss are analyzed, and the results show that the decision variables have good adaptability. Finally, a detailed exergy analysis of the equipment used is made, and the results show that the main exergy losses come from the water coolers and compressors, accounting for 32% and 34%, respectively. Compared to the T-C3MR, the improved C3MR based on EHR-GWO-GA(E-C3MR) has an approximate 8% increase in liquefaction amount—a roughly 23% decrease in unit energy consumption and a decrease of nearly 24% in exergy loss.

Kriging and Radial Basis Function Models for Optimized Design of UAV Wing Fences to Reduce Rolling Moment

In the present study, the effects of the wing fence on the wing tip vortices and control surfaces located at the tip of the wing in a flying wing aircraft have been investigated using a numerical method. For the size of the fences, the average dimensions extracted from the wing tip vortices at different angles of attack are used. The basic determining parameter is the rolling torque coefficient, which is tried to be shown by employing a parametric study of the flow behavior in different situations of fence placement. These effects on the rolling torque of the aircraft are measured due to the presence of the split drag rudder control system. In this study, the fences were installed at three different heights and three different positions along the length of the wing, which were investigated at angles of attack of 7 to 16 degrees. The next stage of the research is to design the dimensions of the fence using the single-objective optimization method (a method to find the best solution for a problem with a specific goal). The designing of the fences at three points based on the dimensions of the wing tip vortex is carried out with the computational fluid dynamics (CFD) method (CFD is a computational method that uses physical laws to predict the behavior of fluids.). The aim of this research is to achieve the best design that converges to an optimal solution with minimum time and cost (CFD solution is long). However, CFD analysis requires a lot of computational time. To address this challenge, we employed a hybrid learning model comprising the radial basis function (RBF), a type of artificial neural network, and Kriging, a Gaussian process-based interpolation technique. The dataset for training the hybrid model was obtained from numerical solutions of CFD simulations involving a fence placed at various locations on the wing. Additionally, a genetic algorithm was employed as the optimization method in all instances where it was required. Using the power of machine learning techniques helped us identify the optimal placement of the fence to prevent it from being engulfed by the vortex and to optimize the utilization of the split drag system, yielding significant improvements.

Simulation Analysis and Optimization Design of Paddy Field Mud Spreader Blades for Uniform Dispersion

To improve the distribution of mud particles collected in the tray during the operation of paddy field mud spreader blades, the optimal combination of parameters for the blades that results in the best uniformity of mud dispersion needs to be identified. In this study, a thorough force analysis was conducted on the spreading process, and computational equations were formulated to describe the motion of mud particles. By utilizing the discrete element simulation technique, a simulation model was developed to accurately represent the intricate interaction between the blades and mud particles. Through the single-factor simulation experiments, the ranges of key parameters such as the rotation radius, bending angle, sub-blade tilt angle, forward velocity, and rotational speed of the blade were determined. A secondary orthogonal rotational combination design was employed to establish a regression prediction model between the non-uniformity of mud dispersion and the key blade parameters. Subsequently, a multivariate single-objective optimization method was used to develop an optimization model for the non-uniformity of mud dispersion. The results indicate that the hierarchical order of factors influencing the non-uniformity of mud dispersion is as follows: rotation radius > rotation speed > bending angle > forward velocity > sub-blade tilt angle. To achieve a minimum spreading non-uniformity of 29.63%, a specific configuration is required, which includes a blade rotation radius of 188 mm, a bending angle of 121°, a sub-blade tilt angle of 30°, a forward velocity of 400 mm/s, and a rotation speed of 191 r/min. Finally, the accuracy of the optimization results was verified by means of bench tests. The research results provide a crucial reference for enhancing the uniformity of mud dispersion in paddy field mud spreader blades.

Robust design optimization of a multi-body system with aleatory and epistemic uncertainty

There are several issues in robust design optimization of the multi-body system with aleatory and epistemic uncertainty, such as the difficulty in the multi-source uncertainty characterization and low efficiency in dynamic modeling, analysis and the corresponding solving strategy for its robust design optimization. To address these problems, this paper firstly constructs a unified analysis framework for aleatory and epistemic uncertainty for the multi-body system based on probability theory and evidence theory. Taking a multi-body system of a shipborne artillery model as an example, the transfer matrix method is next employed to rapidly calculate the dynamic response of the muzzle. The results are compared with those by the finite element method to verify the accuracy and efficiency of the transfer matrix method. A decision model of the robust design optimization with both aleatory and epistemic uncertainty is proposed. Based on the polynomial chaos expansion-Kriging model, subset simulation optimization method and a weighted single-objective solving strategy, the multi-objective robust design optimization problem involving aleatory and epistemic uncertainty is then transformed into a single-objective optimization one. Finally, results of both the multi-objective and weighted single-objective robust design optimization methods for the shipborne artillery model are compared to demonstrate the performance of the proposed method.

Improvement of Blocked Long-Straight Flow Channels in Proton Exchange Membrane Fuel Cells Using CFD Modeling, Artificial Neural Network, and Genetic Algorithm

To further improve the performance of the Proton Exchange Membrane Fuel Cell (PEMFC), in this paper, we designed a blocked flow channel with trapezoidal baffles, and geometric parameters of the baffle were optimized based on CFD simulation, Artificial Neural Network (ANN), and single-objective optimization methods. The analysis of velocity, pressure, and oxygen distribution in the cathode flow channel shows that the optimized trapezoidal baffle can improve oxygen transport during the reaction. The comparison of the optimization model with the straight flow channel model and the rectangular baffle model shows that the power density of the optimized model is 4.0% higher than that of the straight flow channel model at a voltage of 0.3 V, and the pressure drop is only 37.83% of that of the rectangular baffle model. For on-road PEMFC with a voltage of 0.6 V, the influence of pump power is significant, and the optimized trapezoidal baffle model has a net power increase of 1.47% compared to the rectangular baffle model at 50% pump efficiency and 3.94% at 30% pump efficiency.

Interval Pareto front-based multi-objective robust optimization for sensor placement in structural modal identification

Considering the multi-performance development of complex systems and the requirement of structural modal identification with typical uncertainties, the nominal single-objective optimization method is not suitable for sensor placement. Therefore, by combining conventional optimal sensor placement with non-probabilistic theory, this study proposes an uncertainty-oriented multi-objective robust optimization method for optimal sensor placement. The Fisher information matrix and ill-posedness comprise one eigenvalue-based optimization objective, and the mean and minimum off-diagonal values in the modal assurance criterion comprise another. Considering the high-cost limitation of the statistical method for handling uncertainties, uncertainty propagations are realized by a dimension-wise analysis with better accuracy and efficiency, thus avoiding the overestimation incurred by the classical Taylor expansion method. The multi-objective robust optimization is established by uncertain eigenvalue- and eigenvector-based indices with interval numbers and solved using the multi-objective optimization algorithm. Considering the solution sets located at the Pareto front, an interval possibility was developed using interval Pareto fronts to determine the optimal number of sensors. A numerical example demonstrated the validity of the proposed method with an optimal number of sensors and corresponding configurations.

Community Detection in Modular Complex Networks Using an Improved Particle Swarm Optimization Algorithm

Community detection is an important and interesting topic for better understanding and analyzing complex network structures. Detecting hidden partitions in complex networks is proven to be an NP-hard problem that may not be accurately resolved using traditional methods. So it is solved using evolutionary computation methods and modeled in the literature as an optimization problem. In recent years, many researchers have directed their research efforts toward addressing the problem of community structure detection by developing different algorithms and making use of single-objective optimization methods. In this study, we have continued that research line by improving the Particle Swarm Optimization (PSO) algorithm using a local improvement operator to effectively discover community structure in the modular complex networks when employing the modularity density metric as a single-objective function. The framework of the proposed algorithm consists of three main steps: an initialization strategy, a movement strategy based on perturbation genetic operators, and an improvement operator. The key idea behind the improvement operator is to determine and reassign the complex network nodes that are located in the wrong communities if the majority of their topological links do not belong to their current communities, making it appear that these nodes belong to another community. The performance of the proposed algorithm has been tested and evaluated when applied to publicly-available modular complex networks generated using a flexible and simple benchmark generator. The experimental results showed the effectiveness of the suggested method in discovering community structure over modular networks of different complexities and sizes.

Application of multiobjective optimization methods for solving of a ship design problem

In the present paper we demonstrate the application of single-objective and multi-objective optimization methods for solving the problem of ship design characteristics optimization. The impact analysis of local criteria on the values of design characteristics and elements of a ship has been carried out. The most frequently used methods of multicriteria optimization and their application to solving the problem of ship design characteristics substantiation, with taking into account the requirements to seakeeping and economic efficiency are considered. The multicriteria optimization results obtained by different methods are compared with the results of single-criteria optimization. Finally, numerical examples are implemented to demonstrate that an attempt to find a compromise solution often decrease the values of local criteria of optimality. It is established that the application of multi-criteria optimization methods for solving the ship design problems makes clear the role of each local optimality criteria and assess it influence on functional efficiency and design characteristics values.

Multi-level model predictive control based multi-objective optimal energy management of integrated energy systems considering uncertainty

Integrated energy systems (IES) with renewable energy systems (RES), carbon capture systems (CCS) and energy storage systems (ESS) are considered efficient in supporting the low-carbon energy supply with both economic and environmental benefits. Effective energy management is required to ensure the economical, environmental and reliable operation of the IES. However, the optimal IES operation is considered a non-trivial task due to the renewable generation uncertainty and the optimization of multiple contradictory objectives (e.g. economic, environmental and risk costs). This paper aims to provide a multi-level optimization model for the real-time optimal IES operation consisting of RES, ESS and CCS. This work quantifies the uncertainty by the Conditional Value at Risk (CVaR) theory in the optimization model. The uncertainty is further reduced by improving the operation strategy through a model predictive control (MPC)-based method. Also, the multi-objective optimization model is adopted to minimize the economic cost, carbon dioxide emissions (CDE) and primary energy consumption (PEC) for optimal energy scheduling in the intra-day stage. Based on the result of the intra-day stage, the feedback correction model is applied to adjust the schedule to balance the difference between the forecasting and actual values. Numerical results show that the proposed solution can provide the trade-off between economical and environmental performance. Through ablation experiments, the proposed method with feedback correction can carry out demand response with lower costs, CDE and PEC. The proposed solution is further confirmed with outperformed performance compared with single-objective optimization methods and other stochastic optimization methods. In addition, a robustness analysis is conducted to quantify the benefits of RES, ESS and CCS in IES.

Genetic optimization of longitudinal ribs on comprehensive heat transfer performance in hollow rotor within axial through flow

The gas foil bearing (GFB) is widely applied to in high-speed, low-power and high-precision oil-free turbo-machinery system, and it faces a thermal management issue of aerodynamic heat caused by viscous dissipation in ultra-high speed conditions. The longitudinal ribs are applied to enhance the convective heat transfer of axial through flow, and the effects of rib parameters, including rib height (e), rib width (θ) and rib number along circumferential distribution (n), on the heat transfer and flow losses are numerically studied by CFD calculations. The radial basis function neural network (RBFNN) coupling with genetic algorithm is applied to optimize the comprehensive heat transfer performance of the longitudinal ribs. The comprehensive performance factor (PEC) is considered as the unique optimization goal for the single-objective optimization method, while the heat transfer coefficient (Nu¯/Nu0) and relative pressure loss (f/f0) are both set as the optimization objectives for the multi-objective optimization method. It shows that the Opt-4 optimized structure searched by single-objective optimization method has a best comprehensive heat transfer performance, with a relatively low and uniform distribution of rotor temperature and a moderate pressure loss. The multi-objective optimization method is more suited to the two optimization targets difficult to unify or with additional restrictions.

Single Objective Optimization Methods in Electrical Power Systems: A Review

Although the scheduling of maintenance tasks for generators is not a new issue, it has recently attracted new attention due to the significant rise in demand for expanding power system size in modern power systems. Generator Maintenance Scheduling (GMS) is a nonlinear optimization problem, highly dimensional and constrained, and determines when power-producing units must undertake well-planned preventative maintenance. The objective function includes binary variables to indicate whether a generator is undergoing maintenance at a given time and is subject to several restrictions described in this paper. However, the biggest concern of GMS is to produce a precise timetable for preventive maintenance of generating units with low cost and high reliability. Despite that, regrettably, a large volume of research works has accomplished solutions towards a model of GMS with the consideration of either maximizing system reliability or minimizing operation costs as an objective of their research work. This is called Single-Objective Problem (SOP), which involves one objective function that needs to be optimized. SOP is solved by Single-Objective Optimization Method (SOOM). The primary purpose of the research is to present a review of SOOM methods used in solving GMS problems.

Daylight optimization in a hypothetical classroom using single-objective optimization methods: Case study in Lhokseumawe, Indonesia

This study evaluates and optimizes daylighting performance in a hypothetical classroom located in Lhokseumawe, Indonesia. The classroom daylighting performance is represented by the metrics aUDI250-750lx, sDA300/50%, and ASE1000,250. Under the baseline conditions, poor daylighting performance was revealed as indicated by a low aUDI250-750lx value and a high ASE1000,250h value. To obtain good daylighting performance, this study utilized a computational simulation method using Rhinoceros, Grasshopper, and Honeybee Plus platforms. Optimization is conducted using Opossum with the RBFOpt algorithm. The optimization result suggests the building envelope design parameters and external shading device design which results in a better natural lighting performance as indicated by the increase of aUDI250-750lx value at about 45%, the sDA300/50% value remaining at around 94%, and the ASE1000,250h value has decreased by 14% compared to the baseline condition of the classroom.

Optimization techniques for energy efficiency in machining processes-a review.

Metal working process is one of the main activities in mechanical manufacturing industry; it is considered as a major consumer of energy and natural resources. In material removal process, the selection of cutting parameters and cooling or cutting liquid is necessary to save energy and achieve energy efficiency as well as sustainability. During the last two decades, the number of publications in this field has rapidly increased and has shown the importance of this research area. This review paper identifies and reviews in detail a total of 166 scientific studies which exhibit original contributions to the field and address multiple energy efficiency challenges. The recently developed models of energy consumption and different materials used in the machining process are presented. Therefore, this study describes various techniques for modeling and optimizing machining operations such as turning, milling, and drilling. Modeling techniques, experimental methods, multi-objective and single-objective optimization methods, and hybrid techniques optimization are presented in a detailed manner compared to previous review papers where only energy models are discussed. It can help practitioners and researchers to select the most appropriate approach for the desired experience and to highlight the progress of these methods in terms of machining energy efficiency. Additionally, this paper provides a review of different cutting fluids adopted in machining processes. This paper assists researchers and manufacturers in making advantageous technical decisions that have substantial economics in terms of energy saving.

A Novel Simulated Annealing-Based Hyper-Heuristic Algorithm for Stochastic Parallel Disassembly Line Balancing in Smart Remanufacturing.

Remanufacturing prolongs the life cycle and increases the residual value of various end-of-life (EoL) products. As an inevitable process in remanufacturing, disassembly plays an essential role in retrieving the high-value and useable components of EoL products. To disassemble massive quantities and multi-types of EoL products, disassembly lines are introduced to improve the cost-effectiveness and efficiency of the disassembly processes. In this context, disassembly line balancing problem (DLBP) becomes a critical challenge that determines the overall performance of disassembly lines. Currently, the DLBP is mostly studied in straight disassembly lines using single-objective optimization methods, which cannot represent the actual disassembly environment. Therefore, in this paper, we extend the mathematical model of the basic DLBP to stochastic parallel complete disassembly line balancing problem (DLBP-SP). A novel simulated annealing-based hyper-heuristic algorithm (HH) is proposed for multi-objective optimization of the DLBP-SP, considering the number of workstations, working load index, and profits. The feasibility, superiority, stability, and robustness of the proposed HH algorithm are validated through computational experiments, including a set of comparison experiments and a case study of gearboxes disassembly. To the best of our knowledge, this research is the first to introduce gearboxes as a case study in DLBP which enriches the research on disassembly of industrial equipment.

Grey Wolves Attack Process for the Pareto Optimal Front Construction in the Multiobjective Optimization

We propose a new metaheuristic, HmGWOGA-MO, for solving multiobjective optimization problems operating with a population of solutions. The method is a hybridization of the HmGWOGA method, which is a single objective optimization method, and the ϵ-constraint approach, which is an aggregation technique. The ϵ-constraint technique is one of the best ways to transform a problem with many objective functions into a single objective problem because it works even if the problem has any kind of Pareto optimal front. Previously, the HmGWOGA method was designed to optimize a positive single-objective function without constraints. The obtained solutions are good. That is why, in this current work, we combined have it with the ϵ-constraint approach for the resolution of multiobjective optimization problems. Our new method proceeds by transforming a given multiobjective optimization problem with constraints into an unconstrained optimization of a single objective function. With the HmGWOGA method, five different test problems with varying Pareto fronts have been successfully solved, and the results are compared with those of NSGA-II regarding convergence towards the Pareto front and the distribution of solutions on the Pareto front. This numerical study indicates that HmGWOGA-MO is the best choice for solving a multiobjective optimization problem when convergence is the most important performance parameter.

Calibration and Test of Contact Parameters between Chopped Cotton Stalks Using Response Surface Methodology

The accuracy of the material parameter settings directly affects the reliability of the results of the discrete element method simulation. It is necessary to calibrate the relevant parameters to obtain accurate discrete element simulation results when separating the cotton stalk particles from the residual film after crushing. The repose angle of the chopped cotton stalk particles was used as the response value to calibrate the contact parameters between particles. Physical tests measured the intrinsic particle and contact parameters between the cotton stalk particles and the contact material, which provided data for the simulation tests. According to the biological structure characteristics of cotton stalk, the discrete element method model of cotton stalk particles was constructed by bonding the elements of nonequal-diameter basic particles. Based on the response surface methodology, the stacking test of particles was simulated. The response model between the contact parameters and repose angle was established, and the effect law of the single-factor terms and interaction terms on the repose angle was analyzed. The optimal combination of contact parameters was obtained through the single-objective and multi-variable optimization methods. Finally, the contact parameter combination was verified by a simulation test of the repose angle. The results showed that the average relative error of the repose angle between the simulation test and the physical test was 1.04%, which verified the accuracy of the calibrated contact parameters and the reliability of the simulation test. These parameters provide a basis for the discrete element simulation study of cotton stalk motion in the separation process of cotton stalks and residual film and the subsequent gas–solid coupling simulation research.

Optimization of a Spark Ignition Engine Knock and Performance Using the Epsilon-Constrained Differential Evolution Algorithm and Multi-Objective Differential Evolution Algorithm.

Since the advent of the internal combustion engine, knock has been a vital issue limiting the thermal efficiency of spark ignition engines under heavy load conditions. The occurrence of knock is also directly influenced by several operating parameters simultaneously. In order to investigate the effects of multiple variables on economic performance and power performance under knock limits, this study adopts single-objective optimization and multi-objective optimization methods to optimize the engine operating parameters, including exhaust gas recirculation rate, exhaust valve timing, spark timing, and intake valve timing. The optimization aims to obtain maximum volumetric efficiency, brake mean effective pressure, and minimum brake specific fuel consumption on the knock limit. First, based on the bench test data at the operation point 2800 rpm and 11.42 bar, a one-dimensional simulation engine model is established in GT-power software and verified. Second, four engine operating parameters are input into the GT-power model as controlled parameters. The epsilon-constrained differential evolution algorithm and the multi-objective differential evolution algorithm are employed to optimize the above four parameters to minimize the knock index and the damage to engine performance due to knock suppression, respectively. Finally, the results show that the two optimization algorithms optimize four parameters. The results of the epsilon-constrained differential evolution algorithm indicate that the decreasing extent of the knock index is 73.3%. In addition, the decreasing extent of brake mean effective pressure is 10.2%. What is more, the increased brake specific fuel consumption is only 0.07%. The multi-objective differential evolution algorithm gives a set of nondominated Pareto optimal solution sets. The optimal solution has a 64.4% decrease in the knock index, a 5.78% decrease in brake mean effective pressure, and a 1.45% decrease in brake specific fuel consumption.

An intelligent green scheduling system for sustainable cold chain logistics

This study proposes an intelligent green scheduling system for cold chain logistics (IGSS-CCL) to support the integration and coordination of resources. Post-COVID-19, the traditional cold product market is rapidly converting to retail stores and e-commerce portals owing to social distancing restrictions, which creates a requirement and opportunities for the development of cold chain logistics. However, urban governance requirements, such as pandemic prevention, traffic restriction, energy conservation, and emissions reduction, have added challenges to this development. Therefore, it is vital to design a cold chain logistics scheduling system that considers the economic, safety, and environmental factors. The proposed system includes three parts: (1) the framework structure of the cold chain logistics intelligent scheduling system; (2) a multi-objective scheduling optimization model to allow for efficient and dynamic coordination between the distribution, demand, and external environment; and (3) a two-stage optimization algorithm based on Dijkstra's algorithm and a non-dominated sorting genetic algorithm to support intelligent scheduling operations. Numerical experiments were conducted to analyze the performance of the proposed system and demonstrate its application. The results highlight that multi-objective tactical optimization in the IGSS-CCL is conducive to saving resources, protecting the environment, and promoting the sustainable development of cold chain logistics, which remains ahead of the traditional single-objective optimization method. Managers can use the suggested IGSS-CCL as a decision-support tool to control and supervise the scheduling operations of cold chain logistics.