Multi-objective Particle Swarm Optimization Method Research Articles

Smart grid stability prediction model using two-way attention based hybrid deep learning and MPSO

In smart grid management, precise stability prediction is a complicated task that adds to the effective allocation of resources with grid stability. Specifically, demand-side management is considered an essential element of the overall Smart Grids system. Hence, predicting future energy demands is crucial to regulating consumption by aligning utility offerings with consumer demand. This research presents a hybrid deep learning model (Convolutional Neural Network [CNN] with Bi-LSTM) with a two-way attention method and a multi-objective particle swarm optimization method (MPSO) for short-term load prediction from a smart grid. The proposed hybrid model utilizes a two-way attention method at its encoding and decoding stages, in which an encoding attention layer helps to recognize all the essential features from an input vector, and a decoding attention layer helps to resolve the fixed context vector problem by offering better memory capacity. A CNN and Bi-LSTM are used to capture the essential features from the dataset. We also utilize a t-Nearest Neighbours algorithm to pre-process the initial dataset. An MPSO method combines the features of CNN and Bi-LSTM methods, resulting in better prediction accuracy. As far as we know, it is the first work to suggest a dynamic short-term load prediction model that considers different significant features and enables precise predicting outcomes. The performance of the proposed model and existing well-known deep learning models such as Recurrent Neural Network, Gated Recurrent Unit, Long Short-Term Memory (LSTM), Time Series Transformer, CNN-LSTM and various performance measuring parameters MAE, MSE, MAPE and RMSE are calculated on online UCI dataset (Electrical Grid Stability Simulated Dataset). The proposed hybrid model achieved a better prediction result, which proves the efficiency of the proposed model.

Optimizing hybrid energy storage: A multi-objective approach for hydrogen-natural gas systems with carbon-emission management

This article addresses the challenges of load curve variability in integrated energy systems (IES) and emphasizes carbon neutralization in response to global environmental concerns. It introduces a game-theoretic approach to understand the collaborative dynamics within an integrated energy-hydrogen system (HGESS). The study develops a collaborative game model for the IES-HGESS partnership, focusing on carbon neutralization considerations. Using game theory, it investigates the determinants shaping this partnership and extends to create a hybrid model for day-ahead and real-time markets. To foster enduring cooperation, the study introduces a cooperation profit coefficient and devises an income distribution strategy for HGESS-IES interactions. A load management program is incorporated to reduce costs and enhance profitability. Addressing complexity and constraints, the article introduces an advanced multi-objective particle swarm optimization method. Tested on a sample system under various conditions, the results demonstrate that the alliance between IES and HGESS can significantly enhance net profits, achieving mutual benefit and a win-win outcome while advancing sustainability in energy management.

Multi-Objective Optimization for Thrust Allocation of Dynamic Positioning Ship

Thrust allocation (TA) plays a critical role in the dynamic positioning system (DPS). The task of TA is to allocate the rotational speed and angle of each thruster to generate the generalized control forces. Most studies take TA as a single-objective optimization problem; however, TA is a multi-objective optimization problem (MOP), which needs to satisfy multiple conflicting allocation objectives simultaneously. This study proposes an improved multi-objective particle swarm optimization (IMOPSO) method to deal with the non-convex MOP of TA. The objective functions of reducing the allocation error, and minimizing the power consumption and the tear-and-wear of thrusters under physical constraints, are established and solved via MOPSO. To enhance the global seeking ability, the improved mutation strategy combined with the roulette wheel mechanism is adopted. It is shown through test data that IMOPSO converges better than multi-objective algorithms such as MOPSO and nondominated sorting genetic algorithm II (NSGA-II). Simulations are conducted for a DP ship with two propeller–rudder combinations. The simulation results with the single-objective PSO algorithm show that the proposed IMOPSO algorithm reduces thrust allocation errors in the three directions of surge, sway, and yaw by 48.48%, 39.64%, and 15.02%, respectively, and reduces power consumption by 44.53%, which demonstrates the feasibility and effectiveness of the proposed method.

Multi-objective optimization and sensitivity analysis of offset strip fin compact heat exchangers

In this study, the aerothermal performance of Offset Strip Fin Compact Heat Exchangers (OSF-CHE) is optimized with the multi-objective Particle Swarm Optimization method. Available empirical correlations for the Fanning factor and Colburn factor are utilized in the optimization process. Selected off-design Pareto-optimal solutions for compact heat exchanger configurations are reconstructed and simulated by Computational Fluid Dynamics simulations at various boundary conditions after extensive validation processes. Optimized OSF-CHE designs are shown to have improved aerothermal performances among comparable designs in the literature. It has been shown that a high Reynolds number in the range of 1,500–2,000 as a flow attribute provides the Fanning factor ranging from 0.0228 to 0.0619 and the Colburn factor between 0.0067 and 0.0122 and Re is more dominant than geometric design variables on the objective functions. Low and moderate Reynolds number flows at 500 and 1,000 offer a wider selection range for both objective functions on the Pareto-front.

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 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.

Geometrical projection improved multi-objective particle swarm optimization for unsupervised nonlinear hyperspectral unmixing

ABSTRACT In recent years, a series of bilinear mixture models (BMMs) have been well studied, based on which nonlinear unmixing algorithms for hyperspectral images have been proposed. However, the performance of unsupervised nonlinear spectral unmixing can be largely affected by the collinearity and the nonconvex unmixing problem’s inherent complexity. To solve the problems, this paper proposes a geometrical projection improved multi-objective particle swarm optimization (GPMOPSO) method for nonlinear unmixing, which consists of two dynamically updating procedures. First, under the assumption of the BMMs, observed pixels are geometrically projected to their approximate linear mixture components to alleviate the collinearity. Second, the linear mixture components are efficiently decomposed to refined endmembers and abundances in an improved linear unmixing framework using multi-objective particle swarm optimization. Two advanced multi-objective optimization strategies are adopted to balance the influence of an endmember distance constraint and an abundance sparsity constraint on the achieved unsupervised linear unmixing process, respectively. Then, accurate unmixing variables can be fed back to the geometrical projection procedure to produce more reliable linear mixture components which facilitate the execution of the former in turn. Experimental results of both simulated data and real hyperspectral remote sensing data verify the superior nonlinear unmixing performance of the proposed method.

Design of a wind-PV system integrated with a hybrid energy storage system considering economic and reliability assessment

Hybrid energy systems (HESs) have garnered significant attention as a sustainable solution to meet the world's growing energy demands while minimizing environmental impact. Achieving cost-effective and resilient HES solutions requires considering economic and reliability factors for optimal design. This research delves into the optimization and design of a wind-PV system integrated with a hybrid energy storage system using the Multi-Objective African Vultures Optimization Algorithm (MOAVOA) in both standalone and grid-connected modes. A comprehensive case study is conducted in Tabuk, Saudi Arabia, focusing on a microgrid and incorporating wind speed, solar radiation, and load data. The effectiveness of MOAVOA is verified using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) methods in achieving the Pareto front of optimal system designs. Then, the mean, entropy, and criteria importance using the inter-criteria correlation (CRITIC) methods are utilized to assign weights to each objective in the obtained Pareto front. Finally, the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method is employed to select the optimal system design from the Pareto front. The results underscore the significance of the optimization approach in assessing the cost, dependability, and ideal configurations of the system. Furthermore, they highlight the diverse outcomes produced by different optimization techniques. While the entropy method yields more cost-effective designs, the mean and CRITIC methods produce comparable results. However, the mean and CRITIC approaches exhibit higher levels of reliability. For instance, in the grid-connected mode, the entropy weighting method yields an optimal system design with a total cost of M$ 42.2 and a reliability index of 91.73 %, while using the mean method, the selected system configuration has a total cost of M$ 42.98 and a reliability index of 92.89 %. The study emphasizes the benefits of diversifying renewable resources by considering different scenarios involving wind and solar generation. For example, in the wind-PV grid-connected system, the total cost is 22.65 % less than in the PV-only grid-connected system with a higher system reliability. The findings provide valuable guidance for system designers in selecting optimal optimization techniques and promoting the integration of renewable energy sources in hybrid energy systems. By presenting a comprehensive framework that incorporates economic and reliability assessments through the utilization of MOAVOA, this study contributes to the field of HES design.

Coal blending optimization in thermal power plants based on multi-strategy fusion multi-objective particle swarm optimization

ABSTRACT Rising coal price and increasingly stringent emission policy highlight the importance of balancing economic benefit and sustainable development of coal-fired power plants. In order to make the power plant operate economically and safely, and reduce the emission of pollutants as much as possible, this paper proposed a coal blending optimization framework for coal-fired power plants. This framework constructs a set of mathematical model based on the minimization objective function, including the economy, safety and environmental protection of coal. And a multi-strategy fusion multi-objective particle swarm optimization (MSF-MOPSO) method is proposed to optimize the coal blending model. The feasibility, effectiveness and superiority of the method are theoretically verified by convergence analysis and several sets of simulation experiments. And the practical application of this framework shows that under the coal blending strategy based on different weight combinations, the algorithm proposed in this paper can produce high-quality Pareto spatial distribution. Comparing with other state-of-the-art coal blending algorithms, this method can reduce the coal purchase cost by 4.42% and S O 2 emission by 5.11% at least under the same condition. It has significant environmental protection and economic benefit.

A novel solar tower assisted pulverized coal power system considering solar energy cascade utilization: Performance analysis and multi-objective optimization

Solar-assisted pulverized coal power systems offer higher solar energy utilization efficiency, enabling pulverized coal power plants to rapidly achieve energy-saving and emission-reduction targets. This study proposes the incorporation of two solar heaters to create a new solar tower assisted pulverized coal power (STPCP) system for the cascade utilization of solar energy. A comprehensive performance analysis, considering both power-boosting (PB) and fuel-saving (FS) operation modes, has been conducted. The multi-objective particle swarm optimization method is then applied to optimize the system from both thermal and economic perspectives. The findings demonstrate that in FS mode, annual coal consumption decreases by 24.35 × 103 tons, while in PB mode, annual power generation increases by 94.12 GWh. The optimization analysis demonstrates that the optimal cost of electricity (COE) and annual standard coal consumption rate in PB mode are 1.4 × 10−4 USD/kWh and 4.3 g/kWh lower than those in FS mode. Additionally, as the solar field cost declines, the optimal COE and annual standard coal consumption rate gradually decrease. While, increasing coal prices result in higher optimal COE, but lower optimal annual standard coal consumption rate. Based on this study, it can be inferred that the STPCP system shows promising prospects in the future.

Designing a Log-Logistic-Based EWMA Control Chart Using MOPSO and VIKOR Approaches for Monitoring Cardiac Surgery Performance

This study aims to develop a risk-adjusted Exponentially Weighted Moving Average (EWMA) control chart for computer-based performance monitoring in cardiac surgery. Patients have distinct risk factors that impact the surgical process before it even begins. As a result, risk adjustment is carried out utilizing the Accelerated Failure Time (AFT) model to consider these factors. Before using the risk-adjusted EWMA chart, the optimal parameter design should be established, considering the required statistical and economic factors. A model for Multi-Objective Decision Making (MODM) with multiple assignable causes has been proposed to accomplish this. The model is solved using a two-stage methodology based on the Multi-Objective Particle Swarm Optimization (MOPSO) method and the VIekriterijumsko KOmpromisno Rangiranje (VIKOR) method. An actual case study for cardiovascular patients has been undertaken to demonstrate the performance and effectiveness of the suggested model. The multi-objective and pure economic models have been thoroughly compared. The economic model with statistical constraints and the multi-objective model have also been compared again. The findings suggest that the multi-objective design of the risk-adjusted EWMA chart exhibits higher statistical performance in both cases against a small augment in cost.

Environmental–Economic Analysis of Multi-Node Community Microgrid Operation in Normal and Abnormal Conditions—A Case Study of Indonesia

This paper presents a comprehensive analysis of the operation management of a multi-node community microgrid (MG), emphasizing power flow constraints and the integration of photovoltaic (PV) and battery systems. This study formulates MG operation management as a multi-objective optimal power flow problem, aiming to minimize costs (maximize profits) and emissions simultaneously. The multi-objective particle swarm optimization (MPSO) method is employed to tackle this complex optimization challenge, yielding a Pareto optimal front that represents the trade-offs between these conflicting objectives. In addition to the normative operation scenarios, this research investigates the robustness of the MG system in the face of abnormal situations. These abnormal scenarios include damage to the PV system, sudden increases in the MG load, and the loss of connection to the main electricity grid. This study focuses on Lombok Island, Indonesia as a practical case study, acknowledging the ongoing efforts to implement the community MG concept in this region. It is observed that when the access to the electricity grid is limited, the energy not served (ENS) increases to 2.88 MWh. During the fault scenario in which there is a 20% increase in the hourly load of each MG, a total of 4.5 MWh ENS is obtained. It is concluded that a resilient operation management system is required to ensure a consistent and reliable energy supply in community MGs in the face of disruptions.

An Improved Multi-Objective Particle Swarm Optimization Method for Rotor Airfoil Design

In this study, a multi-objective aerodynamic optimization is performed on the rotor airfoil via an improved MOPSO (multi-objective particle swarm optimization) method. A database of rotor airfoils containing both geometric and aerodynamic parameters is established, where the geometric parameters are obtained via the CST (class shape transformation) method and the aerodynamic parameters are obtained via CFD (computational fluid dynamics) simulations. On the basis of the database, a DBN (deep belief network) surrogate model is proposed and trained to accurately predict the aerodynamic parameters of the rotor airfoils. In order to improve the convergence rate and global searching ability of the standard MOPSO algorithm, an improved MOPSO framework is established. By embedding the DBN surrogate model into the improved MOPSO framework, multi-objective and multi-constraint aerodynamic optimization for the rotor airfoil is performed. Finally, the aerodynamic performance of the optimized rotor airfoil is validated through CFD simulations. The results indicate that the aerodynamic performance of the optimized rotor airfoil is improved dramatically compared with the baseline rotor airfoil.

A novel optimization framework for minimizing the surface roughness while increasing the material processing rate in the SLM process of 316L stainless steel

Purpose316 L stainless steel alloy is potentially the most used material in the selective laser melting (SLM) process because of its versatility and broad fields of applications (e.g. medical devices, tooling, automotive, etc.). That is why producing fully functional parts through optimal printing configuration is still a key issue to be addressed. This paper aims to present an entirely new framework for simultaneously reducing surface roughness (SR) while increasing the material processing rate in the SLM process of 316L stainless steel, keeping fundamental mechanical properties within their allowable range.Design/methodology/approachConsidering the nonlinear relationship between the printing parameters and features analyzed in the entire experimental space, machine learning and statistical modeling methods were defined to describe the behavior of the selected variables in the as-built conditions. First, the Box–Behnken design was adopted and corresponding experimental planning was conducted to measure the required variables. Second, the relationship between the laser power, scanning speed, hatch distance, layer thickness and selected responses was modeled using empirical methods. Subsequently, three heuristic algorithms (nonsorting genetic algorithm, multi-objective particle swarm optimization and cross-entropy method) were used and compared to search for the Pareto solutions of the formulated multi-objective problem.FindingsA minimum SR value of approximately 12.83 μm and a maximum material processing rate of 2.35 mm3/s were achieved. Finally, some verification experiments recommended by the decision-making system implemented strongly confirmed the reliability of the proposed optimization methodology by providing the ultimate part qualities and their mechanical properties nearly identical to those defined in the literature, with only approximately 10% of error at the maximum.Originality/valueTo the best of the authors’ knowledge, this is the first study dealing with an entirely different and more comprehensive approach for optimizing the 316 L SLM process, embedding it in a unique framework of mechanical and surface properties and material processing rate.

Multi-objective Optimal Scheduling Analysis of Power System Based on Improved Particle Swarm Algorithm

Economic Environmental Dispatching (EED) in power systems is a multi-variable, strongly constrained, non-convex, multi-objective optimization problem that is difficult to properly handle using traditional methods. However, the application of particle swarm optimization algorithms may result in insufficient population diversity and easy to fall into local optimization problems. Therefore, this paper proposes an adaptive backbone multi-objective particle swarm optimization (ABBMOPSO) method to solve the economic and environmental scheduling problems of power systems. This paper first analyzes the topology and computational flow of particle swarm optimization algorithms, and then constructs a multi-objective optimization research framework that integrates Pareto optimization principles for the scheduling of power generation units. The execution algorithm is the improved multi-objective particle swarm optimization algorithm (MOPSO). This paper establishes a mathematical model for the economic and environmental scheduling of power systems, which optimizes conflicting fuel cost functions and pollutant emission functions simultaneously, taking into account nonlinear constraints such as load balance constraints and unit operation constraints. The improved ABBMOPSO algorithm is used to optimize the solution to improve the global search ability of the EED model. The simulation data of seven units show that the ABBMOPSO algorithm has a minimum power generation cost of 588.1 $/h and a minimum pollutant emission of 0.192 t/h, which is significantly superior to other algorithms and reduces the number of iterations, with good feasibility.

Performance optimization of solar‐powered heat engine using a multiobjective accelerated algorithm

AbstractIn this study, a multiobjective optimization approach was used to conduct a thermodynamic investigation of a solar Brayton and endoreversible heat engine. The thermo‐economic performance capabilities of such machines with hybrid input power, solar‐fuel, are examined numerically. Throughout this study, three performance indicators of the cycle, including the power output, the thermo‐economic performance function, and the thermal efficiency are optimized concurrently employing a multiobjective steepest descent method, named the Accelerated Diagonal Steepest Descent algorithm. Furthermore, to properly analyze the error, three strategies are employed in the decision‐making step to identify the optimal compromise solution, and the deviation indices under these strategies are analyzed. The numerical experiments reveal that the present algorithm outperforms the two popular multiobjective algorithms: the multiobjective particle swarm optimization method and the elitist nondominated sorting genetic algorithm. The relevance of the presented algorithm with respect to the previous ones is examined by means of a deviation index. Finally, these experiments show the optimal design parameters which lead to the best performance of the heat engine.

Demand response-fuzzy inference system controller in the multi-objective optimization design of a photovoltaic/wind turbine/battery/supercapacitor and diesel system: Case of healthcare facility

One of the most common causes of power outages in developing countries is a global mismatch between supply and demand. The effects of this phenomenon are especially devastating in the healthcare sector. This paper describes the management of the loads' operation using Demand Response-Fuzzy Inference System Controller (DR-FIS) for the sizing optimization of photovoltaic/wind turbine/battery/supercapacitor and photovoltaic/wind turbine/battery/diesel generator systems operating autonomously in a health center in northern Cameroon using multi-objective particle swarm optimization (MOPSO) and multi-objective genetic algorithm (MOGA) methods. The assessment criteria for this optimization are Loss of Power Supply Probability (LPSP), Net Present Cost (NPC), Cost of Energy (COE), Total Greenhouse gases Emission (TGE), Wasted Energy (WE), and Renewable Generation (REG). Implementing a Demand Response-Fuzzy Inference System controller (DR-FIS) has allowed significant energy savings (15.4130% reduction in energy demand) and increased worldwide supply–demand adequacy. This study highlights the techno-economic and environmental significance of using a supercapacitor (SC) as a backup in contrast to a diesel generator (DG), as well as the validation of its compatibility with storage batteries because of the provision of a robust energy management approach. Finally, in this study, MOGA results in better results than MOPSO after evaluating the outcomes of the various multi-objective optimization methods. This strategy enabled the determination of the ideal configuration for the studied Healthcare Center's power supply. This configuration includes the Demand Response-Fuzzy Inference System. It consists of 20 solar panels (PV), 02 wind turbines (WT), 04 batteries (BT), and 07 supercapacitors (SC) for a COE of 0.1691 $/kWh, a NPC of 1.1808e + 03 $, a TGE of 439.7901 Kg, a WE of 4.0066e + 03 Kwh, 100% REG and unfortunately 0.9858 % LPSP.

Variable-Length Multiobjective Social Class Optimization for Trust-Aware Data Gathering in Wireless Sensor Networks

Data gathering in wireless sensor networks (WSNs) is vital for deploying and enabling WSNs with the Internet of Things (IoTs). In various applications, the network is deployed in a large-scale area, which affects the efficiency of the data collection, and the network is subject to multiple attacks that impact the reliability of the collected data. Hence, data collection should consider trust in sources and routing nodes. This makes trust an additional optimization objective of the data gathering in addition to energy consumption, traveling time, and cost. Joint optimization of the goals requires conducting multiobjective optimization. This article proposes a modified social class multiobjective particle swarm optimization (SC-MOPSO) method. The modified SC-MOPSO method is featured by application-dependent operators named interclass operators. In addition, it includes solution generation, adding and deleting rendezvous points, and moving to the upper and lower class. Considering that SC-MOPSO provides a set of nondominated solutions as a Pareto front, we employed one of the multicriteria decision-making (MCDM) methods, i.e., simple additive sum (SAW), for selecting one of the solutions from the Pareto front. The results show that both SC-MOPSO and SAW are superior in terms of domination. The set coverage of SC-MOPSO is 0.06 dominant over NSGA-II compared with only a mastery of 0.04 of NSGA-II over SC-MOPSO. At the same time, it showed competitive performance with NSGA-III.

Global-guidance chaotic multi-objective particle swarm optimization method for pneumatic suspension handling and ride quality enhancement on the basis of a thermodynamic model of a full vehicle

In this paper, the parameters of the validated suspension system model of a full-vehicle are tuned through design sensitivity analyses, and then Multi-Objective Particle Swarm Optimization (MOPSO) is used to enhance vehicle ride comfort, which is the vertical whole-body vibrations, and handling features, that is, roll motion and road holding, simultaneously. The parameters of this thermodynamic-based pneumatic suspension system model are comprised of the air spring reservoir volume, orifice resistance, initial volume, and pressure of the pneumatic springs. To enhance the convergence rate, computational times, and diversity of the swarm particles, we have incorporated chaotic dynamics into the MOPSO using the Logistic Map chaotic method to initialize the population and also employed the leader-based global guidance techniques to conduct the potential solutions in each iteration. The analysis of the proposed modeling and optimization results show that the suspension system has been reasonably boosted in terms of vehicle handling and ride comfort. Quantitatively, the RMS acceleration and pitch angle has been reduced by about 71% and 57%, respectively, showing a substantial improvement in passenger comfort. Furthermore, the proposed approach caused an increase in tire road-holding force by about 148% and a reduction of roll angle by 33% which results in an enhancement in vehicle handling, boosting vehicle driving safety.

Endmember Bundle Extraction Based on Improved Multiobjective Particle Swarm Optimization

Endmember extraction (EE) is a key task for hyperspectral image unmixing. The majority of EE algorithms extract only one pure spectrum for each class of material, which may lead to a large unmixing error in cases of spectral variability. Endmember bundle extraction (EBE), which identifies a set of endmembers representing the spectral variability within each class, has been developed to solve the spectral variability problem. To date, only a small number of EBE methods have been developed; moreover, these approaches mainly employ traditional convex-geometry-based EE methods to extract endmember bundles from subset data, which may result in the loss of some endmembers and an incomplete endmember bundle set. This paper proposes an improved multi-objective particle swarm optimization method for the identification of multiple endmember bundles, named IMPSO-EBE. The proposed approach follows the framework of the spatial-spectral EBE (SSEBE) method, which extracts endmember candidates first and then applies post-processing to remove redundant endmembers. However, unlike the SSEBE method, which uses the traditional pixel purity index (PPI) method to obtain candidate endmembers from single feature space, IMPSO-EBE proposes to cooperate across multiple dataspaces to obtain candidate endmembers based on multi-objective particle swarm optimization, making it able to extract candidate endmembers that cannot be identified in single feature space. We compare the performance of the proposed method with that of the single dataspace-based endmember bundle extraction methods using two real hyperspectral datasets. Results show that the endmember bundles identified by the proposed method are more complete than those of the comparison method.