Multi-objective Particle Swarm Optimization Technique Research Articles

An optimized and hybrid gating scheme for the suppression of very low-frequency radios in transient electromagnetic systems

Abstract. One of the most widely used approaches for measuring the earth's subsurface resistivity is the transient electromagnetic (TEM) method. However, noise and interference from different sources, e.g., radio communication, the instrument, the atmosphere, and power lines, severely taint these types of signals. In particular, radio transmission in the very low-frequency (VLF) range between 3 and 30 kHz is one of the most prominent sources of noise. Transient electromagnetic signals are normally gated to increase the signal-to-noise ratio. A precise selection of gate shapes is required to suppress undesired noise while allowing the TEM signal to pass unaltered. We employ the multi-objective particle swarm optimization technique to choose optimal gate shapes and placements by minimizing an objective function composed of standard error bars, the covariance between gates, and the distortion of the gated signal. The proposed method is applied to both fully sampled synthetic TEM data and to boxcar-gated field data. The best output from the search space of gate shapes was found to be a hybrid combination of boxcar and Hamming gates. The effectiveness of hybrid gating over traditional boxcar and semi-tapered gating is confirmed by an analysis of covariance matrices and error bars. The results show that the developed method effectively suppresses VLF noise in the middle gates, which are gates with center times spanning 30 to 200 µs , and in the late gates, which are gates with center times spanning 200 to 1130 µs. The analysis shows that the average improvement in standard errors obtained for the hybrid gating scheme over boxcar gating is 1.719 and 1.717 for synthetic and field data, respectively.

Piezoelectric impedance-based high-accuracy damage identification using sparsity conscious multi-objective optimization inverse analysis

Two elements are essential in structural health monitoring utilizing dynamic responses: response measurement with high-frequency contents, i.e., small characteristic wavelengths, that can adequately reflect damage features, and effective inverse identification analysis that is however oftentimes under-determined. The advancement of smart structure integration has led to active interrogation through frequency-sweeping piezoelectric impedance measurement at high frequency range. In this research we develop a multi-objective optimization formulation for the identification of damage location and severity utilizing piezoelectric impedance. While one optimization objective is to match the response measurement with finite element model prediction in the damage parametric space, the other is the number of locations of damage, i.e., the sparsity of damage index as the solution vector, since damage usually occurs within a small number of locations. This multi-objective formulation fits well the under-determined nature of damage identification, as it naturally provides multiple solutions as basis for further elucidation. The challenge remaining is how to find a small solution set that can include the actual damage scenario. Here we develop a novel inverse identification framework utilizing the intelligent swarm optimizer which possesses flexibility for enhancement. We first embed a sparsity enforcement process into the population generation of the optimizer, which yields a solution repository intrinsically possessing sparsity. We then apply reinforcement learning so the agents can adaptively opt for local strategies with the aim of enriching the searching patterns to diversify the solutions. Through the incorporation of a Q-table, searching toward more promising directions will be rewarded. Our case analyses employing experimental data indicate that this sparsity-conscious multi-objective particle swarm optimization technique can lead to a small solution set which generally encompasses the true damage scenario. This effectively solves the structural damage identification problem with piezoelectric impedance measurement.

Multi-objective optimization of CMOS low noise amplifier through nature-inspired swarm intelligence

This paper presents the application of two swarm intelligence techniques, multi-objective artificial bee colony (MOABC) and multi-objective particle swarm optimization (MOPSO), to the optimal design of a complementary metal oxide semiconductor (CMOS) low noise amplifier (LNA) cascode with inductive source degeneration. The aim is to achieve a balanced trade-off between voltage gain and noise figure. The optimized LNA circuit operates at 2.4 GHz with a 1.8 V power supply and is implemented in a 180 nm CMOS process. Both optimization algorithms were implemented in MATLAB and evaluated using the ZDT1, ZDT2, and ZDT3 test functions. The optimized designs were then simulated using the advance design system (ADS) simulator. The results showed that the MOABC and MOPSO techniques are practical and effective in optimizing LNA design, resulting in better performance than previously published works, with a gain of 21.2 dB and a noise figure of 0.848 dB.

Multi-objective optimization design of coupled wall structure with hybrid coupling beams using hybrid machine learning algorithms

A new hybrid coupling beam incorporating buckling restrained energy dissipaters and viscous dampers was previously recommended. The beam can effectively reduce the floor acceleration and interstory drift ratio of coupled wall structure, and has great application prospects. However, the optimal design of a structure with hybrid coupling beams needs to consider multiple objectives, and these objectives often conflict with each other. At the same time, multiple parameters need to be optimized, resulting in a corresponding increase in the time-cost. This usually brings challenges to structural design. To address this issue, this study presents a hybrid intelligent optimization framework based on multi-objective particle swarm optimization (MOPSO) and machine learning techniques. The developed framework uses machine learning technology to quickly predict the seismic performance of structures with hybrid coupling beams, and obtain a design solution set through multi-objective particle swarm optimization. The proposed framework is demonstrated by considering a case study of a 12-story coupled wall structure with hybrid coupling beams, and it is shown that compared with the initial stage of optimization, the seismic performance of optimized structure is simultaneously improved at close cost. The floor acceleration and interstory drift ratio of the optimized structure are reduced by 11.5% and 38.7%, respectively. Finally, a formula is proposed to quantify the optimization result of pareto solution, so as to make an optimal structural design scheme. In practice, the proposed framework can provide guidance for realizing rapid and accurate about multi-objective optimization of the coupled wall structure with hybrid coupling beams.

Optimal Sizing of Battery and Super-Capacitor Based on the MOPSO Technique via a New FC-HEV Application

In light of the energy and environment issues, fuel cell vehicles have many advantages, including high efficiency, low-temperature operation, and zero greenhouse gas emissions, making them an excellent choice for urban environments where air pollution is a significant problem. The dynamics of fuel cells, on the other hand, are relatively slow, owing principally to the dynamics of the air compressor and the dynamics of manifold filling. Because these dynamics can limit the overall performance of fuel cell vehicles, two key technologies that have emerged as critical components of electric vehicle powertrains are batteries and supercapacitors. However, choosing the best hybrid energy storage system that combines a battery and a supercapacitor is a critical task nowadays. An electric vehicle simulated application by MATLAB Code is modeled in this article using the multi-objective particle swarm optimization technique (MOPSO) to determine the appropriate type of batteries and supercapacitors in the SFTP-SC03 drive cycle. This application optimized both component sizing and power management at the same time. Batteries of five distinct types (Lithium, Li-ion, Li-S, Ni-Nicl2, and Ni-MH) and supercapacitors of two different types (Maxwell BCAP0003 and ESHSR-3000CO) were used. Each storage component is distinguished by its weight, capacity, and cost. As a consequence, using a Li-ion battery with the Maxwell BCAP0003 represented the optimal form of hybrid storage in our driving conditions, reducing fuel consumption by approximately 0.43% when compared to the ESHSR-3000CO.

Investigation of Distributed Generation Penetration Limits in Distribution Networks Using Multi-Objective Particle Swarm Optimization Technique

Investigation of Distributed Generation Penetration Limits in Distribution Networks Using Multi-Objective Particle Swarm Optimization Technique

An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

A Mobile Ad hoc Network (MANET) is a group of low-power consumption of wireless mobile nodes that configure a wireless network without the assistance of any existing infrastructure/centralized organization. The primary aim of MANETs is to extend flexibility into the self-directed, mobile, and wireless domain, in which a cluster of autonomous nodes forms a MANET routing system. An Intrusion Detection System (IDS) is a tool that examines a network for malicious behavior/policy violations. A network monitoring system is often used to report/gather any suspicious attacks/violations. An IDS is a software program or hardware system that monitors network/security traffic for malicious attacks, sending out alerts whenever it detects malicious nodes. The impact of Dynamic Source Routing (DSR) in MANETs challenging blackhole attack is investigated in this research article. The Cluster Trust Adaptive Acknowledgement (CTAA) method is used to identify unauthorised and malfunctioning nodes in a MANET environment. MANET system is active and provides successful delivery of a data packet, which implements Kalman Filters (KF) to anticipate node trustworthiness. Furthermore, KF is used to eliminate synchronisation errors that arise during the sending and receiving data. In order to provide an energy-efficient solution and to minimize network traffic, route optimization in MANET by using Multi-Objective Particle Swarm Optimization (MOPSO) technique to determine the optimal number of clustered MANET along with energy dissipation in nodes. According to the research findings, the proposed CTAA-MPSO achieves a Packet Delivery Ratio (PDR) of 3.3%. In MANET, the PDR of CTAA-MPSO improves CTAA-PSO by 3.5% at 30% malware.

Biomass possessing toward an efficient arrangement using a novel framework of waste-to-useful products: MOPSO optimization and comprehensive thermodynamic and cost analyses

The maximum use of the potential of biomass-fed power plants through an efficient waste-to-useful products framework ameliorates the performance resulting in better applicability. The current study suggests a novel multi-heat recovery for multiple productions aimed at simultaneous power, liquefied hydrogen, natural gas for urban usage, and freshwater generation. Meanwhile, the main system is made of a biomass processing unit using a gasifier, a Brayton cycle, a carbon dioxide power cycle, a Kalina cycle, a multi-effect desalination, and a Claude cycle attributed to hydrogen liquefaction, and a liquefied natural gas cold energy recovery-based power cycle. The system is examined from the viewpoints of thermodynamic and economic profitability and is optimized in four different scenarios employing a MOPSO (multi-objective particle swarm optimization) technique. Additionally, the sensitivity of the profitability measured by the NPV (net present value) is also evaluated at different prices of input fuel and products. Moreover, the scenarios of the MOPSO include exergyefficiency(εcycle)/NPVtotal, εcycle/freshwaterproductivity(m˙fw), NPVtotal/m˙fw, and εcycle/NPVtotal/m˙fw. Regarding the highest sensitivity impact of the inlet air compressor's pressure ratio on the performance variables and hydrogen liquefaction price on the NPVtotal, the last optimum case stated εcycle=37.1%,NPVtotal=1.635M$,andm˙fw=29.9kg/s.

Bi-level energy management model for the smart grid considering customer behavior in the wireless sensor network platform

Today, smart grids (SG) and its relationship to wireless sensor network (WSN) provides a suitable platform for establishing the two-way communication of the energy management system and network users. This issue can accurately plan the smart grid in real time with minimum cost. This paper proposes a bi-level energy management model for smart grids in uncertainty and demand-side management (DSM) in the Internet of things (IoT) platform based on WSN. In the upper level, for accurate planning in real-time and relationship with energy management system, the customers are modelled in wireless sensor network platform and clustered by fuzzy C-Means algorithm. Then, the energy consumption of the network sensors in the IoT platform is optimized by the genetic algorithm (GA). Moreover, the customer behavior optimization was performed utilizing the price-based demand response (PBDR) model and transferring demand load from the peak to the valley load times. The objective functions in the lower layer minimized the emission pollution, operation costs and energy not supplied (ENS) index using the multi-objective Moth–Flame optimization (MOMFO) algorithm. The Monte Carlo technique is used for modeling renewable energy sources (RES)'s uncertain output. Finally, the suggested approach is confirmed through the analysis of a case study. The proposed model's capability in planning a uniform smart grid operation is demonstrated by simulation study and comparison with multi-objective particle swarm optimization (MOPSO), epsilon-constraint and benders techniques.

Multiobjective Control Design for Human–Machine Systems With Safety Performance Constraints

This article studies the problem of multiobjective control design for a class of human–machine systems (HMSs) with safety performance constraints containing state constraints and input constraints. The HMSs under consideration not only monitor the human but also need to take proper actions to both the human and machine. A model of controlled hidden Markov jump system (CHMJS) is applied to represent the HMSs. Based on the CHMJS model, a sufficient condition for the practical mean square stability of the unconstrained HMSs is first derived using a stochastic Lyapunov functional. A sufficient condition for ensuring the safety performance constraints of the HMSs is also deduced by employing reachability analysis and set invariance theory. Subsequently, a bilinear matrix inequality-based control design is presented to guarantee both the practical mean square stability and safety performance constraints of the HMSs. A multiobjective optimization problem (MOP) is then formulated to determine a feedback controller for the human and a human-assistance controller for the machine such that both the practical mean square stability and safety performance constraints as well as the less human intervention can be satisfied. An algorithm that mixes the multiobjective particle swarm optimization and linear matrix inequality technique is developed to solve this MOP. Finally, a lane departure example is given to illustrate its effectiveness.

Optimization of Electric Discharge Machining of Al/Al2O3 Metal Matrix Composites using MOPSO

In this article, Response Surface Methodology (RSM) and Multi-objective Particle Swarm Optimization (MOPSO) were used to optimize the output response of Material Removal Rate (MRR) and Surface Roughness(SR) of die-sinking Electrical discharge machining (EDM). An aluminum based metal matrix composites, reinforced with alumina, prepared by stir casting, was used for machining on EDM by Copper (Cu) and Titanium (Ti) tool. Box- Behnken Design (BBD) approach of RSM was used to design the experiment by considering four input factors at three levels. This developed model for multi-objective optimization by MOPSO and an RSM-based multi-objective optimization was also designed for input parameters. And it was found that the MOPSO technique was easy and valuable for parametric optimization of EDM. From MOPSO, optimized input parameters for machining of AMMC using Cu tool are current 4A, Voltage 60V, pulse on-time 100 µs, and duty factor 6. From MOPSO, optimized input parameters for machining of AMMC using Ti tool are current 4.241658A, Voltage 60V, pulse on-time 100 µs, and duty factor 4. The confirmatory test found that MRR and SR decreased by 63.86 % and 53.083% for the Cu tool, respectively, for MOPSO compared to RSM optimize value.

Techno-economic and environmental feasibility study with demand-side management of photovoltaic/wind/hydroelectricity/battery/diesel: A case study in Sub-Saharan Africa

This paper presents an optimal design and selection made on four energy systems or configurations with criteria Loss of Power Supply Probability, Cost of Energy, Total Emission and the Diesel Contribution Factor using a Multi-objective Particle Swarm Optimization technique; for some rural communities selected from five different countries in the Sub-Saharan Africa region. These communities include BOGO, BOGOMORO, DANTIKA, GARA-ALI and KASSOUALA. Energy conservation demand-side management strategy is also applied in this study. The results obtained reveal that considerable savings are made with the demand-side management strategy and that Configuration 1 with demand-side management is the best energy system for the communities of BOGO, GARA-ALI and KASSOUALA; Configuration 2 with demand-side management is the best energy system for the communities of DANTIKA and BOGOMORO. For the case of BOGO, the best configuration consists of 196 solar panels, 10 wind turbines, 1 micro hydro generator, 1.5 days of storage system autonomy and 1 diesel generator with Cost of Energy 0.096 $/kWh, 15080.47 kg of Total Emission and 0.0002% of Diesel Contribution Factor. In DANTIKA, the best configuration 174 solar panels, 9 wind turbines, 1 micro hydro generator, 1.3 days of storage system autonomy with Cost of Energy 0.088 $/kWh and 10524.474 kg of Total Emission. Moreover, the results of this study can provide a good framework in the development of Hybrid renewable energy systems to solve the problem of energy deficit in poor rural communities.

A Threshold-based Brain Tumour Segmentation from MR Images using Multi-Objective Particle Swarm Optimization

The Pareto optimal solution is unique in single objective Particle Swarm Optimization (SO-PSO) problems as the emphasis is on the variable space of the decision. A multi-objective-based optimization technique called Multi-Objective Particle Swarm Optimization (MO-PSO) is introduced in this paper for image segmentation. The multi-objective Particle Swarm Optimization (MO-PSO) technique extends the principle of optimization by facilitating simultaneous optimization of single objectives. It is used in solving various image processing problems like image segmentation, image enhancement, etc. This technique is used to detect the tumour of the human brain on MR images. To get the threshold, the suggested algorithm uses two fitness(objective) functions- Image entropy and Image variance. These two objective functions are distinct from each other and are simultaneously optimized to create a sequence of pareto-optimal solutions. The global best (Gbest) obtained from MO-PSO is treated as threshold. The MO-PSO technique tested on various MRI images provides its efficiency with experimental findings. In terms of “best, worst, mean, median, standard deviation” parameters, the MO-PSO technique is also contrasted with the existing Single-objective PSO (SO-PSO) technique. Experimental results show that Multi Objective-PSO is 28% advanced than SO-PSO for ‘best’ parameter with reference to image entropy function and 92% accuracy than Single Objective-PSO with reference to image variance function.

Multiobjective Test Case Prioritization Using Test Case Effectiveness: Multicriteria Scoring Method

Modified source code validation is done by regression testing. In regression testing, the time and resources are limited, in which we have to select the minimal test cases from test suites to reduce execution time. The test case minimization process deals with the optimization of the regression testing by removing redundant test cases or prioritizing the test cases. This study proposed a test case prioritization approach based on multiobjective particle swarm optimization (MOPSO) by considering minimum execution time, maximum fault detection ability, and maximum code coverage. The MOPSO algorithm is used for the prioritization of test cases with parameters including execution time, fault detection ability, and code coverage. Three datasets are selected to evaluate the proposed MOPSO technique including TreeDataStructure, JodaTime, and Triangle. The proposed MOPSO is compared with the no ordering, reverse ordering, and random ordering technique for evaluating the effectiveness. The higher values of results represent the more effectiveness and the efficiency of the proposed MOPSO as compared to other approaches for TreeDataStructure, JodaTime, and Triangle datasets. The result is presented to 100-index mode relevant from low to high values; after that, test cases are prioritized. The experiment is conducted on three open-source java applications and evaluated using metrics inclusiveness, precision, and size reduction of a matrix of the test suite. The results revealed that all scenarios performed well in acceptable mode, and the technique is 17% to 86% more effective in terms of inclusiveness, 33% to 85% more effective in terms of precision, and 17% minimum to 86% maximum in size reduction of metrics.

Full-Field Optimization of Offshore Squeeze Campaigns in Total Gulf of Guinea Fields

SummaryScale inhibitor (SI) squeeze treatments are one of the most common techniques to prevent downhole scale formation. In this paper, we present the optimization of treatment design for multiple wells included in offshore campaigns. Two offshore fields with 8 and 12 production wells in west Africa were considered that are separately treated via yearly squeeze campaigns. The wells included in each campaign are treated in a single trip of the supply vessel. Based on the storage capacity of the vessel, the available volume of SI onboard should be optimally allocated to each of the wells (having different properties and water production rates), so that they are all protected from scaling for 1 year until the next campaign is carried out. A hybrid optimization methodology was applied to optimize the squeeze campaign design.The gradient descent (GD) algorithm was first applied to derive the squeeze “isolifetime proxies” related to each well. Each proxy includes all the possible squeeze designs that result in 365 days of squeeze lifetime in the well. Using these proxies, any combination of wells’ squeeze designs could be nominated as the campaign design, because that would result in treating all wells until the next campaign. The multiobjective particle swarm optimization (MOPSO) technique was implemented to optimize the campaign design by simultaneously minimizing the total SI volume and the total injection time for the whole campaign. Minimizing the total pumping time would consequently minimize the deferred oil volume and the total cost of squeezes in the field.Finally, the Pareto Front was identified for each field, showing the most optimum campaign designs. The Pareto Front was shown to be a valuable tool for the operator to make a trade-off between the size of the vessel and the injection time; that is, to use a bigger vessel to transport more inhibitor to the wells or to use a smaller one but for a longer time to inject more water during the squeeze treatments in the field. A cost analysis was performed to identify the most optimum deployment plan providing the most optimum inhibitor allocation strategy, including the optimum inhibitor volume and the optimum injection time for each campaign.

Optimal allocation of active and reactive power of dispatchable distributed generators in a droop controlled islanded microgrid considering renewable generation and load demand uncertainties

This paper proposes a novel sequential optimization strategy (SOS) for optimal allocation of both active and reactive power among dispatchable distributed generator (DDG) units present in a droop controlled islanded AC microgrid. Active power is optimally dispatched based on the simultaneous satisfaction of an economic objective of total operational cost (TOC) minimization, along with an environmental objective of total operational emission (TOE) minimization, as well as a network-related objective of total active power loss (Ploss) minimization. A fuzzy-embedded multi-objective particle swarm optimization (FMOPSO) technique is used to solve this optimization problem. Reactive power is optimally allocated depending on the objective of capacity based reactive power sharing, which is solved using the conventional particle swarm optimization (PSO) algorithm. Node voltage deviation index (NVDI) and total active power loss in the network (Ploss) are used as two main attributes to quantitatively measure the improvement in node voltage profile as a result of optimal sharing of reactive power among the dispatchable distributed generator units. The presence of local heat demand and uncertainties inherent in weather dependent load demand and renewable generation are taken into consideration. A novel mixed probabilistic–possibilistic scenario based approach (MPPSBA) is put forward to model the uncertainties associated with both the electrical and heat load demand and renewable distributed generator (RDG) output. The effectiveness of the proposed methods is demonstrated on a 33-node droop controlled islanded microgrid (DCIMG) test network.

Adaptive Trajectory Tracking for Quadrotor Systems in Unknown Wind Environments Using Particle Swarm Optimization-Based Strictly Negative Imaginary Controllers

Leveraging the benefits of the multiobjective particle swarm optimization (PSO) technique, we introduce a new concept of adaptive strictly negative imaginary (SNI) controllers. The proposed adaptive control systems are specifically designed to minimize a certain performance index, representing the objective of our control design, which is to obtain a stable, robust, and responsive 3-D tracking of the AR.Drone drone in the face of wind gusts. We compare the performance of our proposed adaptive controllers with respect to the performance of PSO-PID control systems and the traditional Ziegler-Nichols PID controllers, not only in simulated flights but also in real flight tests. We also present a stability analysis of the closed-loop control system using the SNI system theory.

Reliability Support of Undependable Grid Using Green Energy Systems: Economic Study

Developing countries’ energy sector faces a multitude of challenges, ranging from inadequate generation to unstable grids. Power outages are among the most common issues, particularly in remote areas. Utilizing grid-tied green energy resources to address this issue and to cover for power outages from local grids. This article presents a cost-effective design of a grid-tied, hybrid green energy system (GES) consisting of wind, PV, and batteries considering the influence of the grid availability. A multi-objective, optimal techno-economic design, optimized by multi-objective particle swarm optimization technique is presented for the grid-tied GES linked to a small hamlet in the north of Egypt. The multi-objective function introduced in this work includes three objective functions which are, the Loss of Power Supply Probability (LPSP), the Cost of Energy (COE), and the System Surplus Energy Rates (SSER) considering the grid availability. The grid availability (GA) of 100% was considered as a base case and it was reduced to 70% with a step of 5%. The simulation consequences had cleared that the lowest and largest percentage values of SSER were obtained at GA of 85 %, and 70 %, respectively. When the value of SSER equal to 0.33%, the system design for solving the grid unavailability consists of 12 PVs, one WT, and 1420 batteries with COE of 0.145$/kWh and TNPC of 3,699,800 ($).

Parameter Settings Optimization in Map Reduce Big Data processing using the MOPSO Algorithm

Big data is a commodity that is highly valued in the entire globe. It is not just regarded as data but in the world of experts, we can derive intelligence from it. Because of its characteristics which are Variety, Value, Volume, Velocity, and the growing need of how it can be handled, Organizations are facing difficulties in ensuring optimal as well as affordable processing and storage of large datasets. One of the already existing models used for rapid processing together with storage in big data is known as Hadoop MapReduce. MapReduce is used for large-scale data processing in a parallel and distributed computing environment, while Hadoop is used for running applications and storing data in clusters of commodity hardware Furthermore, the Hadoop MapReduce framework needs to tune more than 190 configuration parameters which are mostly done manually. Due to complex interactions and large spaces between parameters, manual tuning is not effective. Even worse, these parameters must be tuned every time Hadoop MapReduce applications are run. The main goal of this research is to create an algorithm that will improve efficiency by automatically optimizing parameter settings when MapReduce jobs are running. The algorithm employs the Multi-Objective Particle Swarm Optimization (MOPSO) technique, which uses two objective functions to look for a Pareto optimal solution while optimizing the parameters. The results of the experiments have shown that the algorithm has remarkably improved MapReduce job performance in comparison to the use of default settings.

A multi-objective optimization model for fast electric vehicle charging stations with wind, PV power and energy storage

The construction of fast electric vehicle (EV) charging stations is critical for the development of EV industry. The integration of renewable energy into the EV charging stations comprises both threats and chances. A successful and reasonable capacity configuration and scheduling strategy is beneficial and significant. This paper studies the optimal design for fast EV charging stations with wind, PV power and energy storage system (FEVCS-WPE), which determines the capacity configuration of components and the power scheduling strategy. Firstly, an EV charging load simulation model considering demand response is built, which dynamically modified charging expectation under time-of-use electricity price. Secondly, based on the system design, a multi-objective optimization model is proposed with minimum objectives of cost of electricity and pollution emissions. Then, this model is solved by a hybrid optimization algorithm which combines multi-objective particle swarm optimization (MOPSO) algorithm and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Finally, the proposed optimization framework is applied to a case in Inner Mongolia, China. A scenario analysis is conducted and concludes that the renewable energy supplies, the connection with utility grid and demand response can help improve the performance on optimization objectives. A sensitivity analysis is also performed to verity the model’s effectiveness. In addition, the proposed method is compared with simulated annealing and genetic algorithm to show its faster computation speed and higher solution quality. • A fast EV charging station with renewable energies and ESS is proposed. • An EV charging load simulation model based on demand response is built. • An optimization design model for FEVCS-WPE is proposed with two objectives. • A hybrid optimization algorithm combining MOPSO and TOPSIS method is used.