Multi-objective Particle Swarm Optimization Research Articles

Optimization of dual-function suspension structures using particle swarm optimization approaches

The suspension system integrating both vibration control and energy harvesting capabilities is denoted as Dual-function Suspension (DFS). The principal objectives for DFS encompass lightweight structure, high output force, extensive adjustability in damping, and minimized energy consumption. In pursuit of optimizing the linear motor and magnetorheological damper (MRD) amalgamated into the DFS, a multi-objective Particle Swarm Optimization (PSO) algorithm is conceived, emphasizing primary and secondary objectives to enhance the holistic performance of the DFS. A comprehensive mathematical model of the DFS is established, and subsequent to this modeling, the structural parameters of DFS are meticulously analyzed. Drawing upon the insights from this analysis, primary and supplementary optimization objectives are delineated, employing PSO for the refinement of the DFS structure. Following this, the Pareto solution set, derived from the optimization process, is judiciously selected utilizing fuzzy theorem principles. The outcomes reveal that, under the constraints of unaltered suspension packaging dimensions and overall energy consumption, the optimized suspension system manifests a 50% augmentation in output force, a 30% expansion in adjustable damping range, and a 39% reduction in thrust ripple compared to its pre-optimized state.

A multi-objective particle swarm optimization with a competitive hybrid learning strategy

AbstractTo counterbalance the abilities of global exploration and local exploitation of algorithm and enhance its comprehensive performance, a multi-objective particle swarm optimization with a competitive hybrid learning strategy (CHLMOPSO) is put forward. With regards to this, the paper first puts forward a derivative treatment strategy of personal best to promote the optimization ability of particles. Next, an adaptive flight parameter adjustment strategy is designed in accordance with the evolutionary state of particles to equilibrate the exploitation and exploration abilities of the algorithm. Additionally, a competitive hybrid learning strategy is presented. According to the outcomes of the competition, various particles decide on various updating strategies. Finally, an optimal angle distance strategy is proposed to maintain archive effectively. CHLMOPSO is compared with other algorithms through simulation experiments on 22 benchmark problems. The results demonstrate that CHLMOPSO has satisfactory performance.

Optimization of Renewable Energy Hydrogen Production Systems Using Volatility Improved Multi-Objective Particle Swarm Algorithm

Optimizing the energy structure to effectively enhance the integration level of renewable energy is an important pathway for achieving dual carbon goals. This study utilizes an improved multi-objective particle swarm optimization algorithm based on load fluctuation rates to optimize the architecture and unit capacity of hydrogen production systems. It investigates the optimal configuration methods for the architectural model of new energy hydrogen production systems in Xining City, Qinghai Province, as well as the internal storage battery, ALK hydrogen production equipment, and PEM hydrogen production equipment, aiming at various scenarios of power sources such as wind, solar, wind–solar complementary, and wind–solar–storage complementary, as well as intermittent hydrogen production scenarios such as hydrogen stations, hydrogen metallurgy, and continuous hydrogen production scenarios such as hydrogen methanol production. The results indicate that the fluctuation of hydrogen load scenarios has a significant impact on the installed capacity and initial investment of the system. Compared with the single-channel photovoltaic hydrogen production scheme, the dual-channel hydrogen production scheme still reduces equipment capacity by 6.04% and initial investment by 6.16% in the chemical hydrogen scenario with the least load fluctuation.

Reactive Power Optimization in Distribution Networks of New Power Systems Based on Multi-Objective Particle Swarm Optimization

The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply. Compared to distribution networks in traditional power systems, the new-generation distribution network offers notable advantages in improving energy efficiency, reliability, environmental protection, and system flexibility, but it also faces a series of new challenges. These challenges include potential harmonic issues introduced by the widespread use of power electronic devices (such as inverters for renewable energy generation systems and electric vehicle charging stations) and the voltage fluctuations and flickering caused by the intermittency and uncertainty of renewable energy generation, which may affect the normal operation of electrical equipment. To address these challenges, this study proposes an optimization model aimed at minimizing network losses and voltage deviations, utilizing traditional capacitor adjustments and static var compensators (SVCs) as optimization measures. Furthermore, this study introduces an improved version of the multi-objective particle swarm optimization (MOPSO) algorithm, specifically enhanced to address the unique challenges of reactive power optimization in modern distribution networks. The test results show that this algorithm can effectively generate a large number of Pareto optimal solutions. The application of this algorithm on a 33-node network case study demonstrates its advantages in reactive power optimization. The optimization results highlight the effectiveness and feasibility of the proposed improved algorithm in the application of distribution network reactive power optimization, offering users a uniform and diverse range of reactive compensation solutions.

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 about 10 %, corresponding to 16269 kW with an increased exergetic efficiency of 70.6 %, respectively. Its specific fuel and product exergetic costs are found to be 12 $/GJ and 15.67 $/GJ, 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.

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

Designing a multi-objective energy management system in multiple interconnected water and power microgrids based on the MOPSO algorithm

In this paper, a method of the energy management system (EMS) in multiple microgrids considering the constraints of power flow based on the three-objective optimization model is presented. The studied model specifications, the variable speed pumps in the water network as well and the storage tanks are optimally planned as flexible resources to reduce operating costs and pollution. The proposed method is implemented hierarchically through two primary and secondary control layers. At the primary control level, each microgrid performs local planning for its subscribers and energy generation resources, and their excess or unsupplied power is determined. Then, by sending this information to the central energy management system (CEMS) at the secondary level, it determines the amount of energy exchange, taking into account the limitations of power flow. Energy storage systems (ESS) are also considered to maintain the balance between power generation by renewable energy sources and consumption load. Also, the demand response (DR) program has been used to smooth the load curve and reduce operating costs. Finally, in this article, the multi-objective particle swarm optimization (MOPSO) is used to solve the proposed three-objective problem with three cost functions generation, pollution, and pump operation. Additionally, sensitivity analysis was conducted with uncertainties of 25% and 50% in network generation units, exploring their impact on objective functions. The proposed model has been tested on the microgrid of a 33-bus test distribution and 15-node test water system and has been investigated for different cases. The simulation results prove the effectiveness of the integration of water and power network planning in reducing the operating cost and emission of pollution in such a way that the proposed control scheme properly controls the performance of microgrids and the network in interactions with each other and has a high level of robustness, stable behavior under different conditions and high quality of the power supply. In such a way that improvements of 41.1%, 52.2%, and 20.4% in the defined objective functions and the evaluation using DM, SM, and MID indices further confirms the algorithm's enhanced performance in optimizing the specified objective functions by 51%, 11%, and 5.22%, respectively.

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 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.

Multi-objective optimization study on the performance of double Darrieus hybrid vertical axis wind turbine based on DOE-RSM and MOPSO-MODM

The Double Darrieus Vertical Axis Wind Turbine (DD-VAWT) effectively enhances wind energy efficiency at low tip speed ratios. In this study, the multi-objective optimization method is constructed for DD-VAWT. The optimization objective is to enhance the power coefficient as well as reduce the maximum instantaneous torque coefficient, while decision variables include the inner ring wind turbine diameter, inner ring wind turbine height, inner ring wind turbine blade airfoil chord length and inner and outer ring wind turbine phase angle. Firstly, the computational fluid hydrodynamics model of DD-VAWT is established and validated by experimental data from the literature. Then, the regression model between critical structural parameters and power coefficients, maximum instantaneous torque coefficients is constructed using response surface analysis. Finally, the multi-objective particle swarm optimization algorithm is used to optimize the objective and the optimal solution is selected from the Pareto frontier solution. This work provides a direction to improve the operating performance of DD-VAWT at low tip speed ratios as well as contributes to energy saving and emission reduction.

Hybrid optimization for integration of renewable energy systems into smart grids

The significant increase in the human population, energy consumption in daily living is increasing. One approach is to raise the amount of power produced to match the growth in human population, although this is typically practically unfeasible. By controlling demand, electricity consumption and associated expenses may be reduced. As a consequence, we employ Demand-side Management (DSM) to plan different gadgets on loads to reduce energy usage. Incorporating renewable energy sources and reducing energy costs are two significant roles that the smart grid plays. As a result, we suggest a hybrid swarm based Harris Hawks' optimization (S–HHO) algorithm to overcome the optimization difficulties. The suggested approach includes loads of home and business variety. The proposed technique was compared to binary particle swarm optimization (BPSO), multi-objective particle swarm optimization (MOPSO), wind driven optimization (WDO), and multi-objective genetic algorithms (MOGA). Results from simulations were produced using MATLAB software. The results show that the improved hybrid optimization S–HHO reduces the Peak-to-average ratio (PAR) and computational time better than other methods such as WDO, BPSO, MOPSO, and MOGA.

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.