Single-objective Particle Swarm Optimization Research Articles

A Bi-level optimization dispatch for hybrid shipboard microgrid considering electricity-gas-heat coupling

With the increasingly severe problem of air pollution and energy crisis, new energy power generation technology in ship has quickly become the focus of attention. Compared with traditional ships, hybrid shipboard microgrid systems can achieve pollution-free, renewable and high use value. However, the integration of electricity-gas-heat in hybrid energy shipboard microgrid system also poses challenges to current optimization methods. Therefore, this paper develops a bi-level optimization dispatch model for hybrid shipboard microgrid system based on multi-objective particle swarm optimization algorithm. Taking the diesel generators, photovoltaic generation system, energy storage system (ESS) and thermal energy storage equipment into account, a hybrid shipboard microgrid system model considering electricity-gas-heat coupling is constructed. Based on this, a bi-level optimization dispatch model is established to reduce total cost, GHG (GHG) emissions and lifespan loss of ESS. The upper-level model achieves the optimization dispatch of power generation equipment and loads; lower-level optimization model with the goal of reducing the lifespan loss of ESS is constructed. The improved multi-objective and single-objective particle swarm optimization algorithms are introduced to find the optimal dispatch solutions for bi-level optimization dispatch model. Finally, simulation results show that the proposed optimization method can not only reduce the cost and GHG emissions by 8.7% and 10.9%, but also improve the cycle life of ESS by 9.2%.

Configuration size optimization of gas-electric hybrid power systems on ships considering energy density and engine load response

System component sizes and their energy densities impact the long-term ship energy efficiency and economics, whereas power source load response influences system transient behavior, posing challenges to system design and energy management when clean fuels are used onboard. In this paper, a novel component sizing method is proposed for integrating reciprocating gas engines and energy storage systems (ESS) on ships, considering energy density and load response simultaneously. By specifying gas engine powers and ESS capacities as normalized optimization variables by ship scales, this method optimizes them based on the A. J. Klee multi-index evaluation and single-objective particle swarm optimization (PSO). Energy densities of engines and ESS were introduced to demonstrate the trade-off between the power-splitting and increased system mass and hull resistance. The engine load response was incorporated into the rule-based energy management strategy (EMS) to limit power ramps. This method was examined in the case study by taking four cruise ships as examples. It can be found that implementing the proposed evaluation-based optimization can improve cruise ship energy efficiency while maintaining reliability over the voyage. It is possible to reduce fuel consumption and carbon emissions by up to 17.1% and 14.8%, respectively. The minimum system reliability can be increased from 0.025 to 0.475 during transients. Furthermore, ship life-cycle costs can be reduced compared to the original design method, and the payback period for gas engines, ESS, and fuel tanks can be shortened to less than six years. Among the various cases, the definition of normalized optimization variables broadens the applicability of this method away from a specific ship, allowing it to be applied to ships of various tonnages and types.

Coarse and Fine Two-Stage Calibration Method for Enhancing the Accuracy of Inverse Finite Element Method.

The inverse finite element method (iFEM) is a novel method for reconstructing the full-field displacement of structures by discrete measurement strain. In practical engineering applications, the accuracy of iFEM is reduced due to the positional offset of strain sensors during installation and errors in structural installation. Therefore, a coarse and fine two-stage calibration (CFTSC) method is proposed to enhance the accuracy of the reconstruction of structures. Firstly, the coarse calibration is based on a single-objective particle swarm optimization algorithm (SOPSO) to optimize the displacement-strain transformation matrix related to the sensor position. Secondly, as selecting different training data can affect the training effect of self-constructed fuzzy networks (SCFN), this paper proposes to screen the appropriate training data based on residual analysis. Finally, the experiments of the wing-integrated antenna structure verify the efficiency of the method on the reconstruction accuracy of the structural body displacement field.

WEDM process parameters optimization by preference-based CS & PSO algorithm for LCP

ABSTRACT Locking Compression Plates (LCP) are typically made of Ti6Al4V alloy. LCP is used to heal fractures of the bone. This article uses Wire Cut Electric Discharge Machining (WEDM) to machine Ti6Al4V alloy. Responses are optimized by two metaheuristic algorithms, the cuckoo search algorithm (CS) and particle swarm optimization (PSO). The cuckoo search algorithm shows that the maximum MRR is 59.4312 mg/min & the maximum cutting speed is 5.0625 mm/min. The single-objective PSO shows a maximum MRR of 59.3632 mg/min & the maximum cutting speed is 5.0155 mm/min. The GRA revealed that the optimum weight for MRR and cutting speed is 48.868% and 51.132% respectively. Scanning electron microscope analysis indicates proper biocompatibility after machining. This article compares CS and PSO with similar calculations for the t-test and found that the p-values for MRR and Cutting speed are 0.9432 and 0.9919.

A Multi-Objective Demand/Generation Scheduling Model-Based Microgrid Energy Management System

In recent years, microgrids (MGs) have been developed to improve the overall management of the power network. This paper examines how a smart MG’s generation and demand sides are managed to improve the MG’s performance in order to minimize operating costs and emissions. A binary orientation search algorithm (BOSA)-based optimal demand side management (DSM) program using the load-shifting technique has been proposed, resulting in significant electricity cost savings. The proposed optimal DSM-based energy management strategy considers the MG’s economic and environmental indices to be the key objective functions. Single-objective particle swarm optimization (SOPSO) and multi-objective particle swarm optimization (MOPSO) were adopted in order to optimize MG performance in the presence of renewable energy resources (RERs) with a randomized natural behavior. A PSO algorithm was adopted due to the nonlinearity and complexity of the proposed problem. In addition, fuzzy-based mechanisms and a nonlinear sorting system were used to discover the optimal compromise given the collection of Pareto-front space solutions. To test the proposed method in a more realistic setting, the stochastic behavior of renewable units was also factored in. The simulation findings indicate that the proposed BOSA algorithm-based DSM had the lowest peak demand (88.4 kWh) compared to unscheduled demand (105 kWh); additionally, the operating costs were reduced by 23%, from 660 USD to 508 USD, and the emissions decreased from 840 kg to 725 kg, saving 13.7%.

Chattering Analysis of an Optimized Sliding Mode Controller for an Electro-Hydraulic Actuator System

Wear and tear are usually caused by various factors, which reduce the life span of a mechanical part. In the control engineering of an Electrohydraulic actuator system, the wear and tear can be caused by the system or the controller itself. This article examines the chattering effect that occurs during the sliding mode controller (SMC) design, and its effect on the nonlinear electrohydraulic actuator (EHA) system. To examine the chattering phenomenon, signum function is first applied on the switching function of the SMC. Then, parameters of the controller are obtained using single objective particle swarm optimization (PSO) method. These parameters are then applied to the switching function with hyperbolic tangent function. Lastly, the performance of both functions is analysed and compared based on graph and numerical data. From the output data, chattering phenomenon generated on the signum function is greatly eliminated by using hyperbolic tangent function.

Bulk-driven current conveyor optimization using simulation-based method

This paper presents the optimization of a novel low-voltage (LV) and low-power (LP) bulk driven current conveyor (CCII). For the bulk-driven circuits, the transistors are biased at the subthreshold regime for LV and LP operation. In contrast, the input transistors of the differential stage are controlled from the bulk terminals to achieve a rail-to-rail input voltage range. A simulation-based technique is adapted for the optimal design of LV-LP bulk driven CCII, using the single-objective particle swarm optimization (PSO) algorithm. It is designed using CMOS 0.18 μm technology to operate at a voltage of 0.3 V and have a power consumption of 37 nW. Optimization results and also process/temperature corner results confirm the correct operation of the designed circuit.

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.

Performance Improvement of Active Distribution Systems Using Adaptive and Exponential PSO Algorithms

This paper presents heuristic-based approaches to enhance the voltage profile and reduce the power losses of Radial Distribution Systems (RDS) equipped with Distributed Generation (DG) sources. These approaches determine the size and location of the DG using modified versions of the Particle Swarm Optimization (PSO) algorithm. Standard, adaptive, and exponential PSO algorithms are derived and compared to each other. It is found that exponential PSO converges faster than adaptive PSO, although they give nearly the same final results. These algorithms are applied and used for single-objective PSO and Multi-Objective PSO (MOPSO) problems to minimize the losses, enhance the voltage stability, and reduce the total voltage deviation of the studied distribution systems. Different methods, including aggregated sum and Pareto-optimal set, are used for MOPSO with a fuzzy-based function to determine the best compromise solution. The proposed algorithms are applied to the balanced IEEE 33- and unbalanced 37-bus radial distribution systems. The obtained results are compared with other algorithms, and it is found the effectiveness of the proposed methodologies.

Vibration Control of Dynamically Similar Buildings Optimally Connected by Viscoelastic Dampers

Recently, the construction of similar high-rise buildings has increased due to the increasing population. Moreover, due to space constraints, these structures are being built next to each other. When such tall buildings are located in high seismic zones, earthquake protection becomes essential. There have been many studies on vibration control by connecting dampers to adjacent dissimilar buildings. However, reviews with specific recommendations on damper connections to adjacent similar buildings are found to be scarce. This study aims to control the building vibration that is dynamically similar and adjacent to each other through damper connections. In this study, two adjacent ten-storied, dynamically similar RC buildings are considered. The buildings are modeled with shear frame and lumped mass for efficient yet straightforward analysis. The idealized shear buildings were connected with viscoelastic dampers using different damper configurations and then subjected to seven seismic ground motions. A numerical integration technique is used to obtain the seismic response, and a single objective particle swarm optimization technique is employed to optimize the position of dampers. The viscoelastic dampers provided at their optimal locations improved the seismic performance of coupled buildings in an economical way.

Multi-objective metaheuristic optimization of combined flash-binary geothermal and humidification dehumidification desalination systems

This paper proposes two configurations of combined flash-binary geothermal systems for simultaneous power production and water purification. The binary cycles which are combined together in “Series” and “Parallel” configurations are Organic Rankin Cycle (ORC) and Humidification Dehumidification (HDH) desalination system. Some zeotropic mixtures are also utilized as the working fluids of the ORC unit. Based on the thermodynamic analysis, a single-objective Particle Swarm Optimization (PSO), and then a multi-objective Particle Swarm Optimization (MOPSO) are performed on the systems whose two objectives are total output work, Ẇtot, and produced freshwater, ṁpw. The single-objective optimization results show that the utilization of the zeotropic mixtures in both of the configurations gives higher output work. However, although the use of zeotropic mixtures does not affect the Parallel system's water purification, it leads to lower freshwater production in the Series system. The maximum amounts of Ẇtot and ṁpw in the single-objective optimization are 5.72 (MW) and 3.93 (kg/s), which are obtained from the Series and the Parallel configurations respectively. The multi-objective optimization, however, gives more reliable outcomes. According to the results of the MOPSO algorithm, the maximum amount of the 1st optimization objective, Ẇtot, for both systems, is 5.87 (MW) and also the maximum value of the 2nd objective, ṁpw, for both configurations is 11.42 (kg/s). On the other hand, the multi-objective optimization demonstrates that the Series configuration has a better performance than the Parallel system.

Particle Swarm Optimization for Cooperative Multi-Robot Task Allocation: A Multi-Objective Approach

This letter presents a new Multi-Objective Particle Swarm Optimization (MOPSO) approach to a Cooperative Multi-Robot Task Allocation (CMRTA) problem, where the robots have to minimize the total team cost and, additionally, balance their workloads. We formulate the CMRTA problem as a more complex variant of multiple Travelling Salesman Problems (mTSP) and, in particular, address how to minimize the total travel distance of the entire robot team, as well as how to minimize the highest travel distance of an individual robot. The proposed approach extends the standard single-objective Particle Swarm Optimization (PSO) to cope with the multiple objectives, and its novel feature lies in a Pareto front refinement strategy and a probability-based leader selection strategy. To validate the proposed approach, we first use three benchmark functions to evaluate the performance of finding the true Pareto fronts in comparison with four existing well-known algorithms in continuous spaces. Afterwards, we use six datasets to investigate the task allocation mechanisms in dealing with the CMRTA problem in discrete spaces.

PSO Research on Cutting Parameters in AWJM Process for Aluminum 6061 Alloy

In recent years there is a rapid growth in the improvement of complexity, difficult and harder to machine metals and alloys. AWJM is one of the hybrid, nontraditional machining process in machining several hard-to-cut materials these days. Machining parameters play the lead role in determining the machine economics and quality of machining. In this study Particle Swarm Optimization soft computing technique is executed to estimate the optimal process parameters which leads to a least value of machining performance and compared with the machining performance value of experimental data. The approaches suggested in this study involve three components, viz., experimental observation, multi regression modeling and single objective Particle Swarm Optimization. The consequence of Pressure, Abrasive flow rate, Orifice diameter, Focusing nozzle diameter and Stand off distance AWJM process parameters on MRR and SR of Aluminium 6061 alloy which is machined by AWJM was experimentally performed and analyzed. According to Response Surface Methodology design, different experiments were conducted with the combination of input parameters on this alloy. The outcome of this study revealed that the PSO soft computing technique obtains the optimal solution of AWJM process parameters for Aluminium 6061 alloy.

Multi-Swarm Single-Objective Particle Swarm Optimization to Extract Multiple-Choice Tests

This paper proposes the use of multi-swarm method in particle swarm optimization (PSO) algorithm to generate multiple-choice tests based on assumed objective levels of difficulty. The method extracts an abundance of tests at the same time with the same levels of difficulty and approximates the difficulty-level requirement given by the users. The experimental results show that the proposed method can generate many tests from question banks satisfying predefined levels of difficulty. Additionally, the proposed method is also shown to be effective in terms of many criteria when compared with other methods such as manually extracted tests, random methods and PSO-based methods in terms of execution time, standard deviation, the number of particles per swarm and the number of swarms.

A two-dimensional discrete fractional Fourier transform-based pansharpening scheme

ABSTRACTIn this paper, a new approach for fusion of multi-spectral (MS) and panchromatic (Pan) images based on 2D-discrete fractional Fourier transform (2D-DFRFT) is proposed. The proposed technique is closer in approach to the other filtering-based pansharpening schemes existing in the literature. In the proposed method histogram equalized Pan image is transformed using the 2D-DFRFT and further used to generate the pansharpened image using appropriate pansharpening rule. The angle parameters associated with the 2D-DFRFT provide additional degrees of freedom which are optimized by single-objective particle swarm optimization (PSO) algorithm for finding better pansharpening results. Simulation results of the proposed technique carried out in MATLAB are presented for IKONOS and GeoEye-1 satellite images and compared with existing fusion methods in terms of both visual perception and objective metrics such as Q-index (Q4), Spectral Angle Mapper (SAM), relative dimensionless global error (ERGAS) and quality with-no reference (QNR). It is observed that the proposed pansharpening scheme provides improved spectral and spatial quality as compared with the existing schemes. The effects of aliasing and mis-registration errors on the proposed method are also investigated and compared with existing pansharpening methods. It is seen that the proposed method is robust against aliasing and mis-registration errors.

Optimal configuration and operating condition of counter flow cooling towers using particle swarm optimization algorithm

Cooling towers are broadly utilized in industrial activities and consume substantial energy, resource and expenditure. Optimization of cooling tower configurations and operating conditions is imperative for reducing consumptions. In this paper, the counter flow cooling tower optimization problems are solved utilizing the particle swarm optimization (PSO) algorithm, which is able to handle both single-objective and deeper multi-objective optimization problems. Based on heat and mass transfer balance equations, the mathematical model of the counter flow cooling tower is established. Validation of the mathematical model shows satisfactory agreement with the experimental data. The single-objective particle swarm optimization (SOPSO) is explored through the proposed mathematical model. With appropriate population size and iteration step number, six cases pursuing minimum total annual cost are investigated by deciding multiple optimal decision variables. Since SOPSO only considers one objective, which may be restricted under conditions with diverse requirements, the multi-objective particle swarm optimization (MOPSO) is also introduced. The case for MOPSO involves four objectives, namely the range, tower characteristic ratio, effectiveness and water evaporation rate, while flow rates of air and water are the decision variables. The results of SOPSO and MOPSO are both compared with previous literatures and satisfactory performance of PSO algorithm is revealed.

Particle swarm optimization procedure in determining parameters in Chaboche kinematic hardening model to assess ratcheting under uniaxial and biaxial loading cycles

AbstractAs parameters in Chaboche model are difficult to be determined from experimental data, a single objective particle swarm optimization procedure was employed to obtain them. Hysteresis loop and uniaxial and biaxial ratcheting simulations were conducted to validate the determined models. Chaboche models determined by particle swarm optimization give more accurate simulation of ratcheting compared with the model determined by trial and error method. Chaboche models containing different backstress components were studied. Models determined considering uniaxial ratcheting can only predict uniaxial ratcheting precisely, while giving very bad simulation of biaxial ratcheting. The linear hardening rule in the N3L1 model clearly decreases the rate of the accumulation of ratcheting strain, and the N3L1 model gives the best simulations. For biaxial ratcheting, the fourth backstress component can decrease the rate of the accumulation apparently, while it has a little influence on prediction of uniaxial ratcheting.

Pansharpening approach using Hilbert vibration decomposition

In this study, a new approach for pansharpening of multispectral and panchromatic (PAN) images is proposed. The proposed technique is based on recently developed signal decomposition technique known as Hilbert vibration decomposition (HVD). In the proposed method, the histogram equalised PAN image is decomposed into many instantaneous amplitude (IA) and frequency components in the decreasing order of energy using the HVD. The IA of the first component (having highest energy) in the decomposition of the PAN image is used to generate the pansharpened image using appropriate pansharpening model. The tuning factor associated with the pansharpening model is optimised by single-objective particle swarm optimization algorithm. This method is also extended for the hyperspectral images. Experimental results of the proposed technique are compared with existing pansharpening methods in terms of both visual perception and objective metrics. It is observed that the proposed pansharpening scheme has improved spectral and spatial qualities as compared with the existing schemes. The effects of aliasing and misregistration errors in the proposed method are also investigated and it is observed that the proposed method is robust against aliasing and misregistration errors as compared with other existing methods.

From single to many-objective PID controller design using particle swarm optimization

Proportional, integrative and derivative (PID) controllers are among the most used in industrial control applications. Classical PID controller design methodologies can be significantly improved by incorporating recent computational intelligence techniques. Two techniques based on particle swarm optimization (PSO) algorithms are proposed to design PI-PID controllers. Both control design methodologies are directed to optimize PI-PID controller gains using two degrees-of-freedom control configurations, subjected to frequency domain robustness constraints. The first technique proposes a single-objective PSO algorithm, to sequentially design a two degrees-of-freedom control structure, considering the optimization of load disturbance rejection followed by set-point tracking optimization. The second technique proposes a many-objective PSO algorithm, to design a two degrees-of-freedom control structure, considering simultaneously, the optimization of four different design criteria. In the many-objective case, the control engineer may select the most adequate solution among the resulting optimal Pareto set. Simulation results are presented showing the effectiveness of the proposed PI-PID design techniques, in comparison with both classic and optimization based methods.

MOPSO-based predictive control strategy for efficient operation of sensorless vector-controlled fuel cell electric vehicle induction motor drives

This paper introduces an optimal control strategy of model-based predictive control (MPC) based on multiobjective particle swarm optimization (MOPSO) for a sensorless vector control induction motor, which is used in a fuel cell electric vehicle drive system. The proposed MPC-MOPSO algorithm is implemented to tune the weighting parameters of the MPC controller to tackle all the conflicting objective functions. The paper handles the following fitness functions: minimizing the speed error, minimizing the torque ripple, minimizing the DC-link voltage ripple, and minimizing machine flux ripple. Computer simulations studies have been completed utilizing MATLAB/Simulink with a specific end goal of assessing the dynamic performance of the proposed MPC-MOPSO optimal controller and comparing it with single-objective particle swarm optimization and traditional PI controllers. The simulation results demonstrate the good dynamic response of the proposed MPC-MOPSO optimal tuning strategy over the traditional PI controllers for more accurate tracking performance through the whole speed range, especially at starting conditions and load change disturbances.