Multi-objective Particle Swarm Optimization Research Articles

Global Path Planning Algorithm for Unmanned Vehicles Based on Multi-objective Particle Swarm Optimization Strategy

Global Path Planning Algorithm for Unmanned Vehicles Based on Multi-objective Particle Swarm Optimization Strategy

Multi-objective optimization for electric discharge drilling of waspaloy: A comparative analysis of NSGA-II, MOGA, MOGWO, and MOPSO

This study investigates the impact of pulse-on-time (TON), current (I), pulse-off-time (TOFF), voltage (V), and tool electrode speed (TES) on material removal rate (MRR), average surface roughness (Ra), power consumption (PC) and surface defects for electrical discharge drilling (EDD) of Waspaloy. Based on a Taguchi design with five factors and four levels, experimental trials reveal TOFF, TON, and TOFF as the most influential factors, contributing 50.45%, 34.29%, and 33.09% to MRR, Ra, and PC, respectively. To address conflicting conditions and optimize responses, non-dominated sorting genetic algorithm-II (NSGA-II), multi-objective genetic algorithm (MOGA), multi-objective grey wolf optimizer (MOGWO), and multi-objective particle swarm optimization (MOPSO) are employed. Comparative analysis of 8 optimal solutions provided by four optimization techniques, which exhibit less than 10% errors in confirmatory experiments, has been done, ensuring better prediction accuracy. MOPSO is preferable to the rest of the optimization techniques due to its lower percentage error. NSGA-II, MOGA, MOGWO, and MOPSO exhibit relative improvement in responses based on confirmatory experiments, establishing them as effective tools for exploring EDD applications. Higher current is confirmed as a primary contributor to increased surface damage through microscopic image analysis, and EDX analysis exposes carbon deposition migration on both the tool and workpiece.

Optimal Design of 100–2000 V 4H–SiC Power MOSFETs Using Multi-Objective Particle Swarm Optimization Algorithms

Optimal Design of 100–2000 V 4H–SiC Power MOSFETs Using Multi-Objective Particle Swarm Optimization Algorithms

Financial risk forewarning with an interpretable ensemble learning approach: An empirical analysis based on Chinese listed companies

An efficient and intelligent forewarning model for financial risk is essential to assist company managers, investors, and market regulators in risk management. This research aims to establish an accurate and understandable financial distress forewarning model based on advanced ensemble learning techniques, including Bootstrap Aggregation (Bagging) and Light Gradient Boosting Machine (LightGBM). Meanwhile, the Borderline-Synthetic Minority Over-sampling Technique (Borderline-SMOTE) is employed to address the imbalance problem between financial distress and non-distressed samples. The Multi-Objective Particle Swarm Optimization (MOPSO) is applied to perform multi-objective optimization of the hyperparameters of the proposed model. In addition, the SHapley Additive exPlanations (SHAP) is adopted to conduct an interpretability analysis of the forewarning results. The Chinese listed companies from 2006 to 2022 are considered as the research samples, and the financial ratios of the sample companies in the first, second, and third years before the financial distress is utilized as forewarning features. The experimental findings reveal that the proposed model has the best forewarning performance by using the features from first year before the financial distress. The model proposed in this study also outperforms other comparative models in terms of Accuracy (88.45%), Negative Predictive Value (74.44%), Recall (91.10%), and Specificity (80.26%). Finally, this research has discovered that the Return on Total Assets is the most influential indicator in forewarning financial distress. Therefore, the proposed model can provide listed company managers, investors, and market regulators with an intelligent and effective tool in decision-making for financial distress forewarning.

Multi-objective robust optimization of staff scheduling for emergency under stochastic demand

Large-scale centralized emergency rescue staff scheduling under emergencies has always been a challenge. With a specific focus on nucleic acid testing amid the COVID-19 pandemic, this study introduces a model that considers dynamic changes in staff supply at rescue points, the varying demand at sampling points over time, and their impact on the spread of the epidemic. We quantified the emergency weight of sampling points by the demand and the regional risk degree and adopted robust optimization with base constraint. With the objectives of minimum comprehensive service distance, maximum weighted value of demand satisfaction rate, and minimum loss caused by unmet demand, a multi-stage, multi-medical rescue point medical staff scheduling model for nucleic acid sampling points with the influence caused by uncertain demand disturbance was built, and an improved NSGA-II-HC algorithm was designed to solve the optimization problem under the influence of demand disturbances. The testing results proved that the NSGA-II-HC algorithm can tackle the issue of insufficient uniformity and diversity in the solution set of NSGA-II while Multi-Objective Particle Swarm Optimization (MOPSO) failed to do so. Taking the epidemic data of Guangzhou city in May 2021 as a case study, our model and algorithm are verified to be feasible. We offer a scheme selection strategy according to the loss degree of two conflicting objectives, the comprehensive service distance and shortage loss. The results suggest that, compared with the demand deterministic model, the decision mechanism under the robust model can reduce the deviation from the optimization objective due to the demand disruption

Design and multi-criteria optimization and financial assessment of an innovative combined power plant and desalination process

Geothermal power generation programs are crucial due to their long-term sustainability and environmentally friendly characteristics, particularly for potential energy-based applications in the future. Innovative designs can yield improve efficien by mitigating irreversibility, resulting in enhanced power production. Hence, this study investigates an innovative cascade thermal design model that employs geothermal resources to enhance the efficiency and output of low-temperature power generation systems, specifically through the integration of organic flash and organic Rankine cycles. Additionally, this model introduces a novel cogeneration approach by incorporating a desalination unit based on humidification-dehumidification processes, aiming to concurrently produce electricity and fresh water. Employing a combination of thermodynamic analysis and financial assessment, the system's performance was simulated and optimized in MATLAB, using the Multi-Objective Particle Swarm Optimization (MOPSO) method. A sensitivity analysis preceded the optimization, leading to the development of three distinct optimization scenarios focused on balancing power and freshwater production, maximizing exergetic efficiency and net present value, and optimizing fixed capital investment for maximum financial viability. The results highlight the second scenario as particularly effective, achieving a power generation of 254.3 kW, an exergetic efficiency of 44.84%, and a net present value of $405,099. Conversely, the third scenario offers the best balance between freshwater production capacity (0.504 kg/s), fixed capital investment ($820,822), and a payback period of 7.12 years. This research demonstrates the potential of integrating advanced thermal models with geothermal resources for sustainable and efficient energy and freshwater production, marking a significant step forward in the development of eco-friendly cogeneration systems.

A multi-objective particle swarm optimization based on local ideal points

Numerous multi-objective evolutionary algorithms have recently been proposed for the leader selection or archive maintenance process via using reference sets. Despite the potential of this strategy has been demonstrated in the existing literatures, a significant drawback is that this methodology requires additional parameters or predefined reference points, which leads to increasing the complexity in real-world applications and reducing the versatility in addressing various optimization challenges. Novel to this study, a new convergence contribution (CC) evaluator without extra parameters and predefined reference points is presented for convergence evaluation, where the inspiration is drawn from the concept of an ideal point on a Pareto front and the divide-and-conquer technique. Specifically, the local ideal points are introduced in this paper by dynamically fabricating from the approximate Pareto front. Furthermore, the CC evaluator and parallel cell distance (PCD) are cooperatively integrated into a multi-objective particle swarm optimization (MOPSO/CP) to enhance both the global best solution selection and archive maintenance strategies. Comparative experiments on 21 benchmark test functions exhibited that the performances in terms of inverted generational distance and hypervolume of the proposed MOPSO/CP was the best among those of the chosen competitive algorithms. The significant role of the cooperative mechanism and the CC evaluator is further verified by ablation studies. The superiority of the designed algorithm against its competitors is unequivocally highlighted by the experimental results.

Optimization and Comparative Evaluation of Novel Marine Engines Integrated with Fuel Cells Using Sustainable Fuel Choices

Large ships are more utilized than ever before to facilitate transporting heavy goods and oils overseas. However, they rely on fossil fuels and cause severe environmental impacts. Utilizing alternative fuels and implementing new engine designs are considered promising methods to reduce greenhouse gas emissions and improve engine performance. This paper compares three developed, hybridized marine engines operated using five fuel blends of hydrogen, methanol, dimethyl ether, ethanol, and methane. In addition, the paper presents a multi-objective particle swarm optimization to increase the engine power and its exergetic efficiency and reduce the cost and environmental impact. A marine engine (SRC-GT-SOFC) containing a steam Rankine cycle, a gas turbine, and a solid oxide fuel cell is considered to weigh less and perform better. It is found that the optimized SRC-GT-SOFC engine power is increased by 10% to 16269 kW with an increased exergetic efficiency of 70.6%. Its specific fuel and product exergetic costs are 12 $/GJ and 15.67 $/GJ, respectively, and its specific fuel and product environmental impacts are 7.4 Pt/GJ and 9.3 Pt/GJ, respectively. This results in a significant reduction in the relative cost difference and the relative environmental impact difference of the optimized SRC-GT-SOFC of 30% and 25.4%, respectively.

Optimization of a syngas-fueled SOFC-based multigeneration system: Enhanced performance with biomass and gasification agent selection

In light of abundant sources of biomass feedstocks and regarding the higher performance in syngas-fueled-based SOFC systems, this work proposes a novel multigeneration system based on the combination of syngas-fuel SOFC, Kalina cycle, humidification-dehumidification desalination unit, and proton exchange membrane electrolyzer. This system is designed for simultaneous production of power, heating, freshwater, and hydrogen. Comprehensive energy, exergy, exergoeconomic, environmental, and economic analyses have been conducted to evaluate its performance. The optimum input biomasses, gasification agents, and operating conditions were determined using a coupled approach of an artificial neural network, multi-objective Particle Swarm Optimization algorithm, and the Linear Programming Technique for Multidimensional Analysis of Preference decision-making method. Under base input conditions, the energy and exergy efficiencies of the proposed system were found to be 53.66 % and 38.2 %, respectively. The system also demonstrated the capability to produce 0.1633 kg/s of freshwater and generate a net power output of 377.6 kW. The results indicate that using oxygen-enriched air in the gasification process notably enhances efficiency while reducing emissions. Straw and CO₂ were identified as the optimal feedstock and gasification agent, yielding an exergy efficiency of 42.71 % and a total product cost of 6.29 $/GJ at the optimum point. Moreover, with a selling price of $0.20/kWh for electricity and a fuel cost of $6/GJ, the system can achieve total revenue of approximately $1.65 million with a payback period of 4.01 years. These findings underscore the proposed plant's profitability under specific pricing conditions, making it an attractive investment opportunity.

Visibility-based layout of a hospital unit - An optimization approach.

Apatient fall is one of the adverse events in an inpatient unit of a hospital that can lead to disability and/or mortality. Themedical literature suggests that increased visibility of patients by unit nurses is essential to improve patient monitoring and, in turn, reduce falls. However, such research has been descriptive in nature and does not provide an understanding of the characteristics of an optimal inpatient unit layout from a visibility-standpoint. To fill this gap, we adopt an interdisciplinary approach that combines the human field of view with facility layout design approaches. Specifically, we propose a bi-objective optimization model that jointly determines the optimal (i) location of a nurse in a nursing station and (ii) orientation of a patient's bed in a room for a given layout. The two objectives are maximizing the total visibility of all patients across patient rooms and minimizing inequity in visibility among those patients. We consider three different layout types, L-shaped, I-shaped, and Radial; these shapes exhibit the section of an inpatient unit that a nurse oversees. To estimate visibility, we employ the ray casting algorithm to quantify the visible target in a room when viewed by the nurse from the nursing station. The algorithm considers nurses' horizontal visual field and their depth of vision. Owing to the difficulty in solving the bi-objective model, we also propose a Multi-Objective Particle Swarm Optimization (MOPSO) heuristic to find (near) optimal solutions. Our findings suggest that the Radial layout appears to outperform the other two layouts in terms of the visibility-based objectives. We found that with a Radial layout, there can be an improvement of up to 50% in equity measure compared to an I-shaped layout. Similar improvements were observed when compared to the L-shaped layout as well. Further, the position of the patient's bed plays a role in maximizing the visibility of the patient's room.Insights from our work will enable understanding and quantifying the relationship between a physical layout and the corresponding provider-to-patient visibility to reduce adverse events.

Optimisation design of child seat based on chaos mutation MOPSO

To improve the safety of the children in child seats when a vehicle collision occurs, a finite element model of a child safety seat is established, and LS-DYNA is used for crash simulation. Select backrest inclination, headrest inclination, and seat belt centre height as design variables to establish response surface models. The application of chaotic mutation theory in particle swarm optimisation (PSO) is proposed, and the optimal solution of child seat parameters is obtained by using the chaotic mutation based grouped multi-objective particle swarm optimisation (MOPSO). The optimal solution was used to carry out the simulation experiment again, and compared with the original performance. It turned out that the Head Performance Criterion (HPC) is 47.08% better than the original child seat. Besides, the rib deformation is reduced by 21.30%, the neck shear stress is reduced by 55.74%, and the thigh stress is reduced by 48.55%, which indicate significant improvements. The results suggest that a certain parameter combination of the child seat leads to the optimal overall performance in child injury prevention, which indicates significant improvement in the damage of all parts of the child.

IMO-PSO FO-PID controller based insulin infusion system for type 1 diabetes patients during post-operation condition

Blood Glucose level controls of type 1 diabetic patients (T1D) in post operative conditions are extremely important. This work proposes controllers for management and control of insulin injections to T1D in post operative care. Proposed work investigated IMO-PSO FO-PID controller as a novel approach to improve insulin delivery. The controller utilizes optimization algorithms to regulate insulin levels in real time. Particle swarm optimization (PSO) based tuning is already a proven method in insulin regulation and multiobjective PSO is well suited when combined with FO-PID which is tested. Additionally, system aims to achieve lower insulin levels, as high insulin levels have been linked to increase risks of complications for T1D patients. Results showed that IMO-PSO FO-PID controller is effective in improving glucose control, with patients experiencing fewer fluctuations and instances of hypoglycemia or hyperglycemia. Simulation results show that the use of the controller is very useful in managing blood glucose levels over a longer period and corresponding insulin infusion levels are also controlled well, which is much less than the previously reported control methodologies. This paper highlights potential benefits of using this controller in managing T1D and improving glucose control.

On-machine measurement and compensation of thin-walled surface

On-machine measurement technology is considered as a key technology for realizing closed-loop feedback control in intelligent manufacturing due to the reduction of the transfer process. However, the complexity of the machine tool process system introduces some uncertainty into the accuracy of on-machine measurement, which severely limits the application in the actual industrial field. To overcome the shortcomings of the existing uncertainty, an inspection framework for on-machine measurement of thin-walled surface is proposed. Firstly, a low-cost wireless on-machine measurement system based on potential signals is established and integrated into a manufacturing process line for automatic sampling. Then, the similarity of momentum conservation is introduced into sampling planning, and an adaptive sampling model based on momentum conservation and multi-objective particle swarm optimizer is proposed. Finally, a stacked deep learning model under the vertical inspection direction is proposed to improve the inspection accuracy by correcting the sampling data. Compared with existing sampling methods, the proposed model is similar to an attention mechanism that enables adaptive enhancement of profile features. The inspection performance by data correction is improved by about 16.14% in the mean inspection error. Simulations and experiments show that the proposed method has great advantages in terms of efficiency and robustness, which can provide a theoretical reference for adaptive toolpath correction for thin-walled surfaces.

Automated test case generation for path coverage by using multi-objective particle swarm optimization algorithm with reinforcement learning and relationship matrix strategies

Automated test case generation for path coverage by using multi-objective particle swarm optimization algorithm with reinforcement learning and relationship matrix strategies

Enhancing thermal transfer in airfoil structures through advanced simulation techniques and MOPSO optimization

This research employs advanced simulation techniques by integrating Python, CAD, ANSYS Fluent, and other execution programs, to optimize the thermal transfer performance of an airfoil structure. The study involves a design of experiments (DOE) analysis employing optimal Latin hypercube (OLH) sampling to study the influence of airfoil structural parameters on Nu, f, and comprehensive heat index (CHI). Subsequently, multi-objective particle swarm optimization (MOPSO) is applied, resulting in a remarkable 93.4% increase in CHI, elevating it from 101.13 to 195.61. Experimental validation of the optimized airfoil structure demonstrates close agreement with simulated results, affirming the reliability of our numerical simulations. The results establish the superior heatexchange capabilities of the optimized airfoil structure over the unoptimized reference airfoil and the traditional circular structure. This research contributes significantly to the structural optimization of heat exchange equipment, particularly for configurations characterized by substantial height-to-diameter ratios.

Multi-Objective Particle Swarm Optimization Algorithm based on Position Vector Offset

In response to the issue that particle swarm optimization algorithms tend to fall into local optima when dealing with multi-objective optimization tasks, a multi-objective optimization algorithm based on particle swarm is proposed. This algorithm is based on the relationship between the position vectors of particles, changing the selection and movement strategies of particles to find the true Pareto front. Firstly, two additional position vectors are added around the iterating particles to enhance their search capability; then, a non-dominated vector archive is established to record the non-dominated solutions of the iterating particles and the additional position vectors, increasing particle diversity. Finally, additional position vectors with high fitness are selected to produce a shift in the iterating particle's position, accelerating particle convergence. Comparing this algorithm with dMOPSO, SMPSO, NMPSO, and MOPSOCD algorithms, simulation experiments show that the proposed PVSPSO algorithm has stronger optimization ability.

Economic Dispatch of Multi Regional Power Systems Based on CMOPSO Algorithm

With the progress of society and the aggravation of environmental pollution, the economic dispatch of the power system is developing towards multiple environmental and economic goals. To improve energy utilization efficiency, this study innovatively proposes a multi-objective particle swarm optimization algorithm based on competitive learning, and uses this algorithm to solve multi regional environmental and economic scheduling problems. In addition, the study solves static and dynamic economic (S-DE) scheduling problems in multiple regions through improved competitive group optimization algorithms. The research results show that under different testing systems, the average distribution uniformity indicators of the research algorithm built on competitive learning are 0.8058 and 0.8457, and the average anti generation distance is 67.6316 and 1664.0978. The improved competitive group optimization algorithm solves the maximum, minimum, and average fuel costs for static economic scheduling in multiple regions, which are 656.2243 $/h, 655.8592 $/h, and 655.9866 $/h, respectively. Thus, the designed algorithm can effectively solve economic scheduling problems, which is of great significance for resource integration, saving power generation costs, and reducing pollution emissions.

Individual Updating Strategies-based Elephant Herding Optimization Algorithm for Effective Load Balancing in Cloud Environments

In this manuscript, an Individual Updating Strategies-based Elephant Herding Optimization Algorithm are proposed to facilitate the effective load balancing (LB) process in cloud computing. Primary goal of proposed Individual Updating Strategies-based Elephant Herding Optimization Algorithm focus on issuing the workloads pertaining to network links by the purpose of preventing over-utilization and under-utilization of the resources. Here, NIUS-EHOA-LB-CE is proposed to exploit the merits of traditional Elephant Herd Optimization algorithm to achieve superior results in all dimensions of cloud computing. In this NIUS-EHOA-LB-CE achieves the allocation of Virtual Machines for the incoming tasks of cloud, when the number of currently processing tasks of a specific VM is less than the cumulative number of tasks. Also, it attains potential load balancing process differences with the help of each individual virtual machine’s processing time and the mean processing time (MPT) incurred by complete virtual machine. Efficacy of the proposed technique activates the Cloudsim platform. Experimental results of the proposed method shows lower Mean Response time 11.6%, 18.4%, 20.34%and 28.1%, lower Mean Execution Time 78.2%, 65.4%, 40.32% and 52.6% compared with existing methods, like Improved Artificial Bee Colony utilizing Monarchy Butterfly Optimization approach for Load Balancing in Cloud Environments (IABC-MBOA-LB-CE), An improved Hybrid Fuzzy-Ant Colony Algorithm Applied to Load Balancing in Cloud Computing Environment (FACOA-LB-CE), Hybrid firefly and Improved Multi-Objective Particle Swarm Optimization for energy efficient LB in Cloud environments (FF-IMOPSO-LB-CE) and A hybrid gray wolf optimization and Particle Swarm Optimization algorithm for load balancing in cloud computing environment (GWO-PSO-LB-CE).

A metaheuristic particle swarm optimization for enhancing energetic and exergetic performances of hydrogen energy production from plastic waste gasification

In recent decades, there has been a surge in demand for the development of renewable energies, leading to extensive research efforts focused on the gasification process. Consequently, a wide range of studies has been conducted to explore different feedstocks for gasification, with particular emphasis on plastic waste. Although a wide range of studies conducted various optimization methods for the gasification process, there was no specific study to employ metaheuristic methods in the field of plastic waste gasification. This study aimed to perform a multi-objective particle swarm optimization (MOPSO) method to solve a multi-objective plastic waste gasification optimization problem for enhancing energetic and exergetic viewpoints. Moreover, exact methods including non-linear optimization and response surface methodology (RSM) were conducted to be compared with the results derived from MOPSO. Pareto-front solutions obtained from MOPSO were ranked using grey relational analysis (GRA) and the optimum responses were 517 kJ/kg, 84 %, and 57 % for lower-heating value, cold gas efficiency, and exergy efficiency, respectively, which were compared to optimum options obtained from multi-objective non-linear optimization and RSM techniques. While the MOPSO model may not have performed as well as other methods in this particular problem, it is important to acknowledge its capabilities. Future research can concentrate on improving the behavior and performance of the MOPSO method when dealing with a larger number of points or when applied to different problem domains.

Multi-objective optimization of methanol reforming reactor performance based on response surface methodology and multi-objective particle swarm optimization coupling algorithm for on-line hydrogen production

The utilization of high-temperature exhaust to drive methanol steam reforming for on-line hydrogen production in engines is an effective approach to solving hydrogen storage and transportation challenges. In this work, the response surface methodology (RSM) is combined with the multi-objective particle swarm optimization (MOPSO) algorithm, and is first utilized for multi-objective optimization of the performance of a methanol reforming reactor. Regression models for optimization objectives are developed based on RSM, and the reliability and significance of regression models are determined through analysis of variance. The influence mechanism of the interaction between design variables on optimization objectives is elucidated by visual analysis of the response surface. A multi-objective optimization of reactor performance is performed utilizing the MOPSO algorithm, and the Pareto optimal solution is obtained from the Pareto optimal frontier based on the technique for order preference by similarity to ideal solution method. Optimization results demonstrate that the Pareto optimal solution is obtained at an exhaust temperature of 585.21 K, an exhaust flow rate of 0.0097 kg·s−1, a steam to methanol ratio of 1.48, and a weight hourly space velocity of 1.2. The hydrogen yield, methanol conversion and carbon monoxide selectivity corresponding to the Pareto optimal solution are 68.53 mol·h−1, 0.83 and 0.011, respectively. Under optimal operating parameters, the methanol reforming reactor achieves 21.92 % exhaust energy recovery.