Multi-objective Particle Swarm Optimization Research Articles

A multi-objective optimisation approach with improved pareto-optimal solutions to enhance economic and environmental dispatch in power systems

This work implements the recently developed nth state Markovian jumping particle swarm optimisation (PSO) algorithm with local search (NS-MJPSOloc) awareness method to address the economic/environmental dispatch (EED) problem. The proposed approach, known as the Non-dominated Sorting Multi-objective PSO with Local Best (NS-MJPSOloc), aims to enhance the performance of the PSO algorithm in multi-objective optimisation problems. This is achieved by redefining the concept of best local candidates within the search space of multi-objective optimisation. The NS-MJPSOloc algorithm uses an evolutionary factor-based mechanism to identify the optimum compromise solution, a Markov chain state jumping technique to control the Pareto-optimal set size, and a neighbourhood’s topology (such as a ring or a star) to determine its size. Economic dispatch refers to the systematic allocation of available power resources in order to fulfill all relevant limitations and effectively meet the demand for electricity at the lowest possible operating cost. As a result of heightened public consciousness regarding environmental pollution and the implementation of clean air amendments, nations worldwide have compelled utilities to adapt their operational practises in order to comply with environmental regulations. The (NS-MJPSOloc) approach has been utilised for resolving the EED problem, including cost and emission objectives that are not commensurable. The findings illustrate the efficacy of the suggested (NS-MJPSOloc) approach in producing a collection of Pareto-optimal solutions that are evenly dispersed within a single iteration. The comparison of several approaches reveals the higher performance of the suggested (NS-MJPSOloc) in terms of the diversity of the Pareto-optimal solutions achieved. In addition, a measure of solution quality based on Pareto optimality has been incorporated. The findings validate the effectiveness of the proposed (NS-MJPSOloc) approach in addressing the multi-objective EED issue and generating a trade-off solution that is both optimal and of high quality. We observed that our approach can reduce ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim $$\\end{document}6.4% of fuel costs and ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim $$\\end{document}9.1% of computational time in comparison to the classical PSO technique. Furthermore, our method can reduce ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim $$\\end{document}9.4% of the emissions measured in tons per hour as compared to the PSO approach.

Study on charge and discharge control strategy of improved PSO for EV

In view of the negative influence of electric vehicle (EV) random charging on power grid load stability and charging cost of users, on the premise of guaranteeing users' travel demand, in this paper, an improved PSO model is proposed with the objective function of optimizing the daily load variance of the power grid and minimizing the charge cost of the vehicle owner. In this model, chaotic mapping is used to initialize the position of particles so that the particles are uniformly distributed in space and the diversity of particle solutions is increased, based on the global search ability and local search ability of the improved algorithm, combined with the analysis of a numerical example, the results show that the improved multi-objective PSO algorithm converges quickly and can jump out of the local optimum, better multi-objective optimization to reduce the peak-valley load difference and charging costs.

Developing a fuzzy bi-objective programming and MCDM model for bridge maintenance strategy optimization

Bridges serve as critical links in road networks, requiring continuous maintenance to ensure proper functionality throughout their lifespan. Given their pivotal role in the urban landscape, connecting various parts of a city, this research presents a multi-objective optimization model for the maintenance and repair of bridges in Babolsar, Iran. The model takes into account budget constraints and aims to minimize the total life cycle and user costs, encompassing traffic delays and vehicle expenses, while maximizing the reliability of the bridge network. Recognizing the inherent complexity of this problem, a multi-objective particle swarm optimization algorithm has been developed for an accurate solution. The study further conducts sensitivity analysis on the objective function concerning the available budget, evaluating key parameters such as hourly costs and the time value of vehicles. The results show that with an increase in the budget level, the number of repairs related to the most costly maintenance has significantly risen. In other words, as the budget expands, the model tends to favor repairs with higher costs because their impact on the bridge’s performance is more substantial.

Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms

In this study, a fuzzy multi-objective framework is performed for optimization of a hybrid microgrid (HMG) including photovoltaic (PV) and wind energy sources linked with battery energy storage (PV/WT/BES) in a 33-bus distribution network to minimize the cost of energy losses, minimizing the voltage oscillations as well as power purchased minimization from the HMG incorporated forecasted data. The variables are microgrid optimal location and capacity of the HMG components in the network which are determined through a multi-objective improved Kepler optimization algorithm (MOIKOA) modeled by Kepler’s laws of planetary motion, piecewise linear chaotic map and using the FDMT. In this study, a machine learning approach using a multilayer perceptron artificial neural network (MLP-ANN) has been used to forecast solar radiation, wind speed, temperature, and load data. The optimization problem is implemented in three optimization scenarios based on real and forecasted data as well as the investigation of the battery's depth of discharge in the HMG optimization in the distribution network and its effects on the different objectives. The results including energy losses, voltage deviations, and purchased power from the HMG have been presented. Also, the MOIKOA superior capability is validated in comparison with the multi-objective conventional Kepler optimization algorithm, multi-objective particle swarm optimization, and multi-objective genetic algorithm in problem-solving. The findings are cleared that microgrid multi-objective optimization in the distribution network considering forecasted data based on the MLP-ANN causes an increase of 3.50%, 2.33%, and 1.98%, respectively, in annual energy losses, voltage deviation, and the purchased power cost from the HMG compared to the real data-based optimization. Also, the outcomes proved that increasing the battery depth of discharge causes the BES to have more participation in the HMG effectiveness on the distribution network objectives and affects the network energy losses and voltage deviation reduction.

Application of Particle Swarm Optimisation in Multi-Objective Cost Optimisation of Engineering Enterprises under the Background of Digital Economy

Engineering projects must meet quality and schedule requirements during construction. This is a typical multi-objective problem and a difficult point in the management of engineering enterprises. To address these issues, a research study proposes an intelligent multi-objective optimisation technique. First, analyse the optimisation objectives of the enterprise in the context of digitalisation. Then, construct a multi-objective cost optimisation model for engineering enterprises. Second, the Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm is introduced to solve multi-objective problems. To improve the multi-objective optimisation effect of the model, the inertia weight parameters and particle learning behaviour are optimised and adjusted, as the model is prone to getting stuck in local optima. In the performance test of the algorithm model, the optimised MOPSO model can accurately search for the minimum value of 0 at the position (0, 0) under the Rastrig in function, and at the same time, the number of iteration convergence is the least. The GA, ACOM, and traditional MOPSO models have more iterative convergence times, and the optimisation results are 0.10, 0.15, and 0.14, respectively. It can be seen that the performance of the optimised MOPSO model is better. In the specific example analysis, using the optimised MOPSO solution, the project cost was reduced from 31 million yuan in the contract to 30.52 million yuan, and the construction period was shortened from 588 days to 540 days, and met the environmental protection and quality requirements. The research content can provide important decision support for engineering project managers.

Rolling Element Bearing Fault Diagnosis Based on Multi-objective Optimized Deep Auto-encoder

Abstract Bearing fault diagnosis holds significant importance, with widespread attention focused on enhancing its accuracy and efficiency. Existing diagnostic methods based on deep learning and transfer learning typically tackle this issue by introducing new function modules and diagnostic strategies, such as attention mechanism, adversarial domain adaptation, etc. However, most studies do not consider the structure and hyperparameters optimization of the network to improve the diagnostic performance of the network itself. To address this limitation, a novel multi-objective optimized deep auto-encoder (MODAE) is proposed in this paper. The optimal network structure and hyperparameters is determined by a multi-objective particle swarm optimization algorithm. Crucially, the method is based on a data-driven approaches to automatically search for network structures with stronger generalization and feature extraction capabilities to address engineering problems in different scenarios. Finally, this method is examined in both multi-fault classification diagnosis and transfer diagnosis scenarios, demonstrating strong self-adaptability through experimental results. In comparison with typical deep learning fault diagnosis methods, the proposed method demonstrates higher diagnostic accuracy and superior generalization ability.

Swarm intelligence for full Stokes dynamic imaging reconstruction of interferometric data

In very long baseline interferometry (VLBI), the combination of multiple antennas permits the synthesis of a virtual telescope with a larger diameter and consequently higher resolution than the individual antennas . However, due to the sparse nature of the array recovering an image from the observed data is a challenging ill-posed inverse problem. The VLBI community is interested in not only recovering an image in total intensity from interferometric data, but also in obtaining results in the polarimetric and the temporal domain. Only a few algorithms are able to work in all these domains simultaneously . In particular, the algorithms based on optimization that consider various penalty terms specific to static total intensity imaging, time-variability and polarimetry are restricted to grids in the domain of the objective function. In this work we present a novel algorithm, multiobjective particle swarm optimization (MO-PSO), that is able to recover the optimal weights without any space-gridding, and to obtain the marginal contribution of each of the playing terms. To this end, we utilized multiobjective optimization together with particle swarm metaheuristics. We let the swarm of weights converge to the best position. We evaluate our algorithm with synthetic data sets that are representative for the main science targets and instrumental configuration of the Event Horizon Telescope Collaboration (EHTC) and its planned successors. We successfully recover the polarimetric, static, and time-dynamic signature of the ground truth movie, even with relative sparsity, and a set of realistic data corruptions. We have built a novel, fast, hyperparameter space gridding-free algorithm that successfully recovers static and dynamic polarimetric reconstructions. Compared to regularized maximum likelihood (RML) methods, it avoids the need for parameter surveys, and it is not limited to the number of pixels, unlike recently proposed multiobjective imaging algorithms. Hence, this technique is a novel useful alternative tool to characterize full Stokes time-(in)dependent signatures in a VLBI data set robustly with a minimal set of user-based choices.

Simultaneous consideration of time and cost impacts of machine failures on cellular manufacturing systems

Cellular manufacturing systems (CMS) are essential for achieving efficient production processes, and their success relies on effective cell formation and layout design. This research paper presents a novel nonlinear mathematical programming model that employs the rectilinear distance notion to define the layout within a continuous space. The proposed model incorporates the stochastic nature of machine failures, considering the unreliability with the inclusion of a stochastic time between failures. With a bi-objective approach, the model aims to minimize inter and intra-cell movements of parts, as well as the associated costs related to exceptional elements (EEs), cell reconfigurations, and machine failures. To optimize the proposed model, several multi-objective meta-heuristic algorithms, including the multi-objective particle swarm optimization (MOPSO), multi-objective taboo search (MOTS), and non-dominated sorting genetic algorithm (NSGA-II), are introduced. The effectiveness of these algorithms is validated through numerical instances and a real case study. The results indicate that the MOPSO algorithm exhibits superior performance in optimizing various multi-objective criteria. The presented mathematical model and algorithms provide valuable tools for manufacturers to optimize their CMS, resulting in reduced costs, enhanced production efficiency, and increased competitiveness. By considering the simultaneous effects of time and cost associated with machine failures, this research offers practical insights and solutions for improving the performance of cellular manufacturing systems.

Research on multi-condition and multi-objective control of pumped storage unit optimized by improved multi-objective particle swarm optimization algorithm

Abstract In order to maintain good control quality of the pumped storage unit regulation system during the switching of operating conditions, a multi-objective optimization framework based on improved multi-objective particle swarm optimization algorithm is proposed, which takes into account the control stability, rapidity and robustness under multiple working conditions. Firstly, the fractional order PID controller with higher applicability and flexibility is introduced to replace the traditional PID controller. Then, an objective function considering the ITAE index of low-head and medium-high-head conditions is constructed. On this basis, the multi-objective particle swarm optimization algorithm with pigeon swarm search operator, adaptive Cauchy mutation strategy and improved initialization method is used to optimize the controller parameters, and the multi-condition and multi-objective fractional order PID optimal control of pumped storage unit regulation system is realized. Through simulation, it is proved that the controller parameters based on the framework optimization proposed in this paper have strong adaptability and robustness under different working conditions, which provides a new idea for the optimal control of pumped storage unit regulation system.

Optimization method for capacity configuration and power allocation of electrolyzer array in off-grid integrated energy system

Hydrogen production using renewable energy is in line with China's the goal of carbon peak and carbon neutrality. The construction of off-grid hydrogen energy industrial park can effectively achieve local utilization of renewable energy and develop the green hydrogen industry. However, off-grid hydrogen energy industrial park has not the compensation and sustentation from large power grid, the stable operation and economic production of electrolyzer are affected by significant factors, such as power fluctuation and frequent start stop. Therefore, this paper proposed the optimization method for capacity configuration and power allocation of electrolyzer array in off-grid integrated energy system. Firstly, based on units of energy supply, energy conversion, and energy storage, a structural model of off-grid integrated energy system was established. Then, by analyzing the operational characteristics of single electrolyzer and electrolyzer array, flexible mode of multiple electrolyzers operating in combination was proposed. Furthermore, considering the hydrogen production cost, the mean of hydrogen production volatility and penalty cost, the capacity configuration and power allocation methods for electrolyzer array were proposed, and the optimization problem is solved with multi-objective particle swarm optimization algorithm. Finally, the hydrogen energy industrial park in Zhangye is taken as example to verify the effectiveness of above research content. Compared to a single type of alkaline electrolyzer, the mean of hydrogen production volatility of multiple electrolyzers operating in combination has decreased by 43.2 %, the hydrogen production quantity has increased by 9.74 %. Compared to a single type of proton exchange membrane electrolyzer, the hydrogen production cost has been reduced by 14.16 %. Compared with the balanced running mode, the flexible operation mode can improve the cost-effective and quantity of hydrogen production.

Energy management of renewable energy connected electric vehicles charging using Internet of things: Hybrid MRA-SDRN approach

This manuscript presents a novel hybrid technique, termed the MRA-SDRN method, for optimizing renewable energy-based electric vehicle (EV) charging within an Internet of Things (IoT) architecture. The key objective is to enhance the efficiency of PV-connected EV systems while concurrently reducing electricity costs and pollutant emissions. The MRA-SDRN approach leverages the Mud Ring Algorithm (MRA) and the Spiking Deep Residual Network (SDRN) to optimize several parameters critical for effective EV charging and renewable energy utilization. Comparative analysis with existing techniques conducted in MATLAB demonstrates notable reductions in computation time, with the MRA-SDRN method achieving a calculation time of 13.04 s, outperforming simulated annealing (SA), multi-objective particle swarm optimization and genetic algorithms (GA).Additionally, significant decreases in pollutant emissions are observed, with levels measured at 472.38 g compared to 570.48 g for GA, 528.35 g for SA, and 502.01 g for MOPSO. Regarding electricity costs, the MRA-SDRN approach achieves a competitive rate of 0.306 yuan/kWh, emphasizing its cost-effectiveness. These numerical results underscore the efficacy of the MRA-SDRN method in optimizing renewable energy management for EV charging, thereby contributing to enhanced reliability, efficiency, and sustainability in the transportation sector within the IoT framework.

A change severity degree-based dynamic multi-objective optimization algorithm with adaptive response strategy

Many real-world optimization problems are dynamic by nature, exhibiting temporal variations in objective functions, constraints, and parameters. These problems present significant challenges for algorithm convergence and diversity, as they require the ability to adapt appropriately to new environments. To address these challenges, a Dynamic Multi-objective Particle Swarm Optimization algorithm with an Adaptive Response Strategy (DMOPSO-ARS) is proposed in this paper. The DMOPSO-ARS possesses the capability to detect the degree of change and apply a suitable response strategy accordingly. Specifically, the change response strategy encompasses initialization-based prediction and elite-based learning methods, designed to speed up convergence speed and enhance algorithm diversity in the face of high/low severity environmental changes. To assess the effectiveness of the DMOPSO-ARS, we select the standard CEC 2018 benchmark, noisy test functions, and the recently released OL-DOP 2022 benchmark. Empirical studies indicate the robustness of the DMOPSO-ARS in tracking evolving solutions over time, showcasing its significant superiority compared to state-of-the-art methods.

Multi-objective fuzzy green scheduling optimization method of special vehicle body-in-white prototype shop considering equipment preventive maintenance

Based on the advantages of non-contact, flexibility, and small heat-affected zone, laser processing technology is widely used in material removal, addition, modification and other manufacturing fields. However, the emission of large amounts of smoke during laser processing of metal materials will result in environmental pollution in the job shop. Additionally, the processing time of laser equipment becomes uncertain as the power decays with prolonged use. Therefore, this paper is mainly focus on a multi-objective flexible job shop fuzzy green scheduling problem to minimize the fuzzy makespan, fuzzy total energy consumption and fuzzy total smoke emission. To solve it, an improved multi-objective particle swarm optimization algorithm has been designed. Firstly, a preventive maintenance strategy is proposed to reduce the makespan and equipment failure frequency by considering periodic power attenuation of laser equipment. Next, different particle updating strategies are employed to enhance particles’ exploration capabilities. Subsequently, five neighborhood structures are introduced to improve the performance of local search. Finally, comparison experiments are conducted with an expanded common benchmark, and a production case study in a special vehicle body-in-white prototype job shop. The results effectively demonstrate the feasibility and effectiveness of the developed model and improved multi-objective particle swarm optimization algorithm.

Automated Test Case Generation for Path Coverage by Using Multi-Objective Particle Swarm Optimization Algorithm with Reinforcement Learning and Relationship Matrix Strategies

Software testing is an important phase in software life cycle, and test case generation occupies the main part of the testing workload. Path coverage is the most stringent and difficult coverage criteria, so automatic test case generation for path coverage (ATCG-PC) is an important task in software testing. ATCG-PC is usually abstracted as an optimization search problem, and the commonly used solution algorithm is swarm intelligence algorithm. This paper proposes a path coverage test case generation algorithm based on improved multi-objective particle swarm optimization (IMOPSO) algorithm. The algorithm uses the multi-objective strategy to generate test cases for a group of similar paths at a time, and is supplemented by the particle action selection strategy based on reinforcement learning and the secondary search algorithm based on relationship matrix, which makes use of the similarity information between paths to speed up the early convergence and help to quickly reach the optimal solution around the local optimal position. By grouping the paths according to the similarity, the algorithm reduces the test case consumption, and improves the efficiency of test case generation. Experimental results show that, compared with the existing methods, the proposed algorithm can achieve higher path coverage with less test case consumption and is also suitable for large-scale programs.

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

To 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.

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.

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.