Search

For distribution networks with fuzzy network structure or large scale, traditional reliability assessment is limited by data collection and lack of data samples. Compared with traditional methods, the distribution network reliability prediction method can use fewer data to calculate and obtain reliability results, and its operation is simpler and more practical. In this paper, a distribution network original parameters and reliability prediction method based on wavelet neural network (WNN) and quantum particle swarm optimization algorithm (QPSO) is proposed. Firstly, given the blindness of mother wavelet selection, this paper analyses the error and running time through example analysis and selects the most suitable mother wavelet for distribution network reliability prediction. According to the characteristics of premature convergence of QPSO, the evolutionary speed factor and aggregation factor are introduced to modify the scaling factor to control the convergence of the algorithm. The improved QPSO is used to optimize the initial values and thresholds of the WNN. It can reduce their influence on the prediction results. Finally, the analysis results of different examples show that the method has higher forecast accuracy, better generalization ability, and stability. This method also provides new scientific ideas for the reliability prediction of distribution networks. [ABSTRACT FROM AUTHOR]

The lock valve plays an important role during normal shift and power failure in heavy-dutyautomatic transmissions. A new kind of lock valve was designed in this paper, and the main purpose of this paper is to improve the working performance of the lock valve. Firstly, the AMESim simulation model of the lock valve was built based on the analysis of the working circuit of the lock valve, and the influence of several design parameters on the lock valve working performance were analysed, which provided a reliable theoretical basis for the subsequent optimisation research. Additionally, thecomplete second-orderresponse surface model between design parameters and output oil pressure response time and control oil pressure fluctuation is established based on ISE and ITAE criteria. Finally, the particle swarm optimisation(PSO) algorithm and Box-Behnkenresponse surface methodology were applied to the optimisation of the mathematical model. The optimisation results have shown that the two optimisation methods can significantly improve the output oil pressure response speed of the lock valve, but the optimisation results of Box-Behnkenresponse surface methodology are more stable than the PSO algorithm in the control oil pressure, and the growth fluctuation is smaller. [ABSTRACT FROM AUTHOR]

We have designed and implemented an optimized PID controller for an adaptive cruise control system in this paper. The mathematical model for a cruise control system has been developed, and it is observed that it is a nonlinear first-order model with dead time. The objective functions chosen for optimizing the PID controller are ITE, ITAE and ITSE. The design of the optimized PID controller is based on the particle swarm optimization technique and teacher learning-based optimization technique. The results are scientifically compared with the conventionally tuned PID and fuzzy-based controllers. The optimized Proportional Integral Derivative controller shows better performance than a conventional PID and fuzzy-based controller. The overshoot of the system has been reduced to 0% from 46%, and the rise time has been reduced to 0.6150 s. This is the new work in the literature that will be quite useful for the performance enhancement of the cruise control system. [ABSTRACT FROM AUTHOR]

In this paper, critical analysis of performance comparison of various optimization techniques for a MRPID (Multi Resolution Proportional Integral Derivative) thermal system controller is presented. This controller controls the uncertainty or mismatch between real and reference temperature. Wavelet coefficients of error between the two temperatures and their corresponding gains are added to produce a control signal for the thermal system. The main purpose of the proposed method is to optimize the tuning parameters of a wavelet-based MRPID controller to regulate the switching pulse of the thermal system. Modeling and simulations are carried out using MATLAB/Simulink@2015. Figures-of-merit such as disturbance rejection, system stability and transient response are also compared using various techniques like Particle swarm optimization (PSO)-MRPID, Genetic Algorithm (GA)-MRPID (existing techniques), Firefly-MRPID controllers and Elephant herding optimization (EHO) based Recurrent neural network (RNN)-MRPID algorithm. Firefly-MRPID and EHO with RNN-based MRPID are proposed for determining unique (best) operating conditions of the thermal system in this paper. [ABSTRACT FROM AUTHOR]

Panax notoginseng is a valuable herb with geographical indication, and the quality and price of P. notoginseng from different origins are very different. Therefore, this paper proposes a rapid and accurate method for identifying the origins of P. notoginseng by collecting the roots of P. notoginseng. This paper improves the whale optimization algorithm in terms of global convergence and convergence speed, introduces the Levy flight strategy and reconstructed whale synergy factor A, and applies it to the parameter optimization of support vector machines, to obtain a high-performance classification model. The improved whale optimization algorithm model identifies the origin of P. notoginseng by discriminating their terahertz spectra. Compared with the commonly used genetic algorithm and the original whale optimization algorithm, improvement in the whale optimization algorithm was able to avoid falling into local optimum solutions more effectively while having a high convergence rate. Accordingly, the improved whale optimization algorithm optimized support vector machine model obtained an overall accuracy of 98.44%, which was significantly higher than the 95.31% overall accuracy of the genetic algorithm optimized support vector machine model and the 96.88% overall accuracy of the whale optimization algorithm optimized support vector machine model. It was concluded that terahertz spectroscopy together with machine learning would be a promising technique for identifying the origins of P. notoginseng. [ABSTRACT FROM AUTHOR]

Today, the use of renewable energy is increasing rapidly due to the reduction of pollution and the optimal use of available energy. Microgrids (MGs) are used as systems to control and exploit various resources and loads. Due to the connection of different resources in MGs, control and operation of this system are complex and important. In this paper, the optimal utilization of MGs is presented by considering various parameters including production sources, different loads and energy storage. In this paper for energy management in systems with various MGs, we aim to apply the multi-objective optimization algorithm to obtain the optimal energy management in MG, while simultaneously satisfying pollution and economic objectives. The multi-objective particle swarm optimization (MOPSO) method and non-dominated sorting genetic algorithm (NSGA-II) are used to solve the problem and their results are compared. Various factors are considered in the cost function including operation cost, losses, pollution and other operation characteristics. Using multi-objective optimization functions considering cost functions and various constraints ensures optimal operation of the MG. Simulation results verify various models and effectiveness of the proposed methods. Simulation results show that using NSGA-II for scheduling DG resources gives more optimal results in terms of reducing operation cost and losses such that considering operation cost and pollution cost using NSGA-II reduces operation cost and losses by 10% and 4%, respectively. [ABSTRACT FROM AUTHOR]

Voltage instability is a major challenge facing power system (PS) that has affected some organizations in achieving their desired goals. Therefore, voltage instability is the incapability of the PS to maintain the voltage standard under no disturbance and after subjecting to disruption. This paper describes the voltage instability phenomena; voltage stability indices include Line Stability Index (LP), Line voltage stability index, Fast Voltage Stability Index (FVSI), line stability factor (LQP), Bus voltage collapse prediction index (BVCPI), L index, voltage stability index (VCP-1), and so on. This review focuses on some stability indices that could identify the weak bus in the electrical PS network. The application of particle swarm optimization (PSO) to minimize losses that cause voltage instability is discussed. It started a detailed understanding of the power blackouts and the detrimental effects on the global economy. This was followed by a thorough understanding of the voltage instability/stability phenomenon, classification in power systems, and the corresponding formulations. The study presents an overview of voltage assessment techniques prior to applying PSO in discrete and multi-objective optimization and the corresponding advantages over others. These are followed by the progress and advances in voltage stability using PSO involving single and hybrid optimization methods. Lastly, to bridge the research gaps, the present study highlighted challenges and future prospects to foster further advancement in the field. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) generates countless amounts of data, much of which is processed in cloud data centers. When data is transferred to the cloud over longer distances, there is a long latency in IoT services. Therefore, in order to increase the speed of service provision, resources should be placed close to the user (i.e., at the edge of the network). To address this challenge, a new paradigm called Fog Computing was introduced and added as a layer in the IoT architecture. Fog computing is a decentralized computing infrastructure in which provides storage and computing in the vicinity of IoT devices instead of sending to the cloud. Hence, fog computing can provide less latency and better Quality of Service (QoS) for real-time applications than cloud computing. In general, the theoretical foundations of fog computing have already been presented, but the problem of IoT services placement to fog nodes is still challenging and has attracted much attention from researchers. In this paper, a conceptual computing framework based on fog-cloud control middleware is proposed to optimally IoT services placement. Here, this problem is formulated as an automated planning model for managing service requests due to some limitations that take into account the heterogeneity of applications and resources. To solve the problem of IoT services placement, an automated evolutionary approach based on Particle Swarm Optimization (PSO) has been proposed with the aim of making maximize the utilization of fog resources and improving QoS. Experimental studies on a synthetic environment have been evaluated based on various metrics including services performed, waiting time, failed services, services cost, services remaining, and runtime. The results of the comparisons showed that the proposed framework based on PSO performs better than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, a non-linear parameter identification method for structures is presented, whereby the instantaneous power flow balance of the substructure of interest is enforced. The time-domain power flow into the non-linear substructure is balanced against the power transmitted to adjacent structures, damping and kinetic and strain energies. Enforcing this condition of matching the net power balance to zero, a numerical model is iteratively updated as an inverse problem. Here, the identification is carried out using a non-classical optimization search tool, particle swarm optimization algorithm. A cubic non-linearity in spring and a quadratic non-linearity in damper are modelled for non-linear parameter estimation using power flow concept and is a preliminary stage of non-linear joint parameter identification. Important numerical simulations are presented in this paper, which cover different load cases, measurement points and different combination of non-linearities under noise-free and noisy conditions. The identified results show significant improvements in non-linear identification accuracy compared to previous literature related to non-linear identification. [ABSTRACT FROM AUTHOR]

This paper presents a bi-objective model for integrated production and distribution planning in a multi-period multi-product green supply chain. This paper considers many near-real-world factors, such as: normal and overtime production shifts, limited storage capacity of the finished product and raw materials, different vehicle types, direct and indirect shipping along with considering different production methods that influence the production costs and sale prices, simultaneously. Mixed integer programming model is proposed in which the profit maximization and the CO2 emission minimization are formulated. A particle swarm optimization algorithm based on Pareto archive that utilizes the genetic algorithm operators is proposed to solve the model. [ABSTRACT FROM AUTHOR]

Substrate Integrated Waveguide (SIW) technology has recently drawn attention to its microwave benefits, such as integration into planar microwave circuits, low manufacturing cost and high-quality factor compared to other voluminous technologies. In this paper, a cavity-backed slot antenna structure has been designed and optimised by the Particle Swarm Optimization (PSO) algorithm and manufactured for X-band applications. Indeed, the starting cavity-backed slot antenna has three pairs of resonators placed on the lower wall of the cavity, and the PSO algorithm seeks the optimal antenna by automating HFSS using MATLAB script. Two antennas have been chosen from the last PSO generation. The selected antennas operate respectively on the bands [9.17 GHz. 10.63 GHz] and [9.09 GHz, 10.6 GHz] with a bandwidth of 15%, and can achieve a maximum gain of 8.8 dBi. The total size of the proposed design is 0.46 λ 0 × 1.95 λ 0 . The gain, reflection coefficient and radiation partners are measured and compared to the EM simulation results and a very good agreement is obtained. [ABSTRACT FROM AUTHOR]

In this paper, 1-Degree of Freedom (1-DOF) and 2-Degree of Freedom (2-DOF) Integer Order (IO) and Fractional Order (FO) Proportional–Integral–Derivative (PID) Controller has been designed for the Magnetic Levitation (Maglev) system. Maglev is one of the most versatile research-oriented laboratory instruments in the field of control systems engineering. The controller for the Maglev system is designed in such a way that a ferromagnetic ball can precisely levitate in a controlled electromagnetic field with particular design specifications. The controller parameters can be appropriately identified by optimizing the objective function with various popular artificial intelligence based evolutionary optimization algorithms. The 2-DOF controller structure takes care of both reference signal tracking and disturbance rejection through the feedback path. The 1 and 2-DOF controller structures with both Integer as well as Fractional Order approaches for different nonlinear systems were already discussed in various papers. However, the application of the Fractional Order control technique on a highly nonlinear Maglev system using the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Hybrid Particle Swarm Optimization (Hybrid PSO), and recently developed Jaya Algorithm is of our interest. Observation of the final analysis based on different simulation results states that as the controller structure moves from 1-DOF to 2-DOF, the performance of the system and the control signal gets improved. The robustness analysis has also been incorporated in this study which shows that the 1 and 2-DOF IOPID and FOPID controllers designed using different optimization algorithms for the Maglev plant are robust in nature. [ABSTRACT FROM AUTHOR]

This paper proposes a novel particle swarm optimization (PSO) algorithm with population reduction, which is called modified new self-organizing hierarchical PSO with jumping time-varying acceleration coefficients (MNHPSO-JTVAC). The proposed method is used for solving well-known benchmark functions, as well as non-convex and non-smooth dynamic economic dispatch (DED) problems for a 24 h time interval in two different test systems. Operational constraints including the prohibited operating zones (POZs), the transmission losses, the ramp-rate limits and the valve-point effects are considered in solving the DED problem. The obtained numerical results show that the MNHPSO-JTVAC algorithm is very suitable and competitive compared to other algorithms and have the capacity to obtain better optimal solutions in solving the non-convex and non-smooth DED problems compared to the other variants of PSO and the state of the art optimization algorithms proposed in recent literature. The source codes of the HPSO-TVAC algorithms and supplementary data for this paper are publicly available at . [ABSTRACT FROM AUTHOR]

In recent decades, traditional energy generation and consumption have undergone a transformation to cleaner energy sources. The transition to a sustainable energy system based on efficiency and renewable energies will thus require the replacement of a complex and heavily implemented energy system with one based on concepts such as microgrids. A microgrid is a controlled small-scale power system and the elements that make up it are: distributed generation systems; energy storage systems; load management techniques; monitoring systems, etc. Consequently, since ac devices are used, synchronization criteria must be satisfied to switch operation between them. The synchronization criteria consist of making the values of the phase-angle difference, slip frequency, and voltage difference as small as possible. This is very important to ensure the correct operation of the grid. In the other hand, the use of Particle Swarm Optimization (PSO) algorithm has been increasing, because of their simplicity and efficiency in engineering optimization problems. Therefore, this paper proposes an active synchronizing control scheme that allows to synchronize a generator set to the grid and a particle swarm optimization algorithm, which makes the synchronizing control as efficient as possible. With the implemented control and with the help of the synchronization algorithm, improvements in the reduction of settling time (from 9 seconds to 3 seconds) are obtained in comparison to other controls in the literature. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new family of continuous-time optimisation algorithms based on discontinuous second-order gradient optimisation flows, with finite-time convergence guarantees to local optima, for locally strongly convex (time-varying) cost functions. To analyse our flows, we first extend a well-know Lyapunov inequality condition for finite-time stability, to the case of (time-varying) differential inclusions. We then prove the convergence of these second-order flows in finite-time. In some particular cases, we can show that the finite-time convergence can be pre-defined by the user. We propose a robustification of the flows to bounded additive uncertainties and extend some of the results to the case of constrained optimisation. We show the performance of these flows on well-known optimisation benchmarks, namely, the Rosenbrock function, and the Rastringin function. [ABSTRACT FROM AUTHOR]

Finding the best structure of ANN to minimize errors, the processing and the search time is one of the main objectives in the AI field. In order to achieve prediction with a high degree of accuracy in a short time, an enhanced PSO-based selection technique to determine the optimal configuration for the artificial neural network has been proposed in this paper. To design the neural network to minimize processing time, search time and maximize the accuracy of prediction, it is necessary to identify hyperparameter values with precision. PSO with 2-D search space has been employed to select the best hyperparameters in order to construct the best neural network where PSO is used as a decision-making model and ANN is used as a learning model. The suggested technique was used to select the optimal number of the hidden layer and the number of units per hidden layer. The proposed technique was evaluated using a chemical dataset. The result of testing the proposed technique displayed high prediction accuracy with MSE equal to 3.9% and the relative error between the expected output and actual target is less than 1.6%. The results of the comparison of the proposed technique with the ANN showed thatthe proposed approach could predict output with an infinitesimal error, outperforming the existing ANN model in terms of error ratio. [ABSTRACT FROM AUTHOR]

This paper proposes a stochastic multistage expansion planning method in order to solve the mid-term and long-term optimal feeder routing problem. Pseudo dynamic behaviour of the network parameters and geographical constraints besides the associated uncertainties of future load demand and market price are incorporated into the method. The proposed method is solved from the DisCo viewpoints using particle swarm optimization algorithm which finally converges to a solution with minimum costs. The proposed cost function is the sum of feeder's installation costs, power losses cost, cost of active purchased power from power market, and reliability costs. Meanwhile, the final solution must satisfy all the operation aspects of power system in acceptable levels. On the other hand, the implementation of the final strategy obtained from the proposed method of this paper, can increase the responsibility of the power system in lower costs. In this regard, distribution system can power its customers with higher power quality in acceptable reliability level and also in lower costs. The proposed method is applied on a large-scale distribution network and its practicability and also its effectiveness are analysed due to the simulation results. [ABSTRACT FROM AUTHOR]

In this paper, a long-term Distribution System Planning Model (DSPM) considering network reconfiguration and Distributed Generation (DG) integration, is presented. The DSPM objective functions are power loss reduction and voltage stability enhancement. The proposed model handles long-term load increase and seeks to define when and where the reinforcements, such as network reconfiguration (NR) and DG integration, are required to meet the load increase. A Binary Particle Swarm Optimization algorithm (BPSO) is used for network reconfiguration while optimal DG allocation and sizing are performed using the Voltage Stability Margin Index (VSMI) and curve fitting approximation. The planning horizon of each solution is determined through load forecasting using a Nonlinear Autoregressive neuronal network with exogenous variables (NARX) technique. The performance of the proposed model is evaluated considering several case studies on the IEEE 33-bus test system. Results demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid PPSOGSA algorithm based on the phasor particle swarm optimization (PPSO) and gravitational search algorithm (GSA) for solving the Volt/VAr/THDv control optimization problem in radial distribution systems with non-linear loads. The PPSOGSA's performance is analyzed and evaluated using the standard IEEE 30-bus radial distribution test system. The optimal solutions are obtained based on the following three optimization criteria: minimization of total active power losses, minimization of the voltage deviation, and a simultaneous minimization of total active power losses and voltage deviation. The voltage total harmonic distortion levels (THDv) are estimated using the decoupled harmonic power flow (DHPF) algorithm. Simulation results, obtained using the proposed PPSOGSA-based approach, are compared with those obtained using the other optimization algorithms such as PSO, PPSO, GSA, and PSOGSA. It is shown that the PPSOGSA-based approach has better performance in comparison to the other optimization techniques. [ABSTRACT FROM AUTHOR]

Land remote-sensing satellite (System, Landsat) 8 operational land imager (OLI) can provide land cover target mapping (LCTM) information. Due to the limitation of hardware and complexity of environment, Landsat 8 OLI image sometimes contains a large number of mixed pixels, which brings a challenge for LCTM. Super-resolution mapping (SM) handles the mixed pixels to obtain LCTM at subpixel scale. However, the scale information is single and the infrared information is not rich in the existing SM methods. In order to solve these issues, this paper proposes SM based on multiscale-infrared information (MII). There are two terms (i.e. multiscale and infrared terms) in MII. The multiscale term with multiscale information is produced through deep Laplacian pyramid network (DLPN), multiresolution segmentation, and extended random walker in turn. The infrared term with infrared information is derived by calculating the normalized difference target index (NDTI). The two terms are combined to generate a minimization term with multiscale-infrared information. Particle swarm optimization algorithm is applied to the minimization term to obtain the LCTM result. The experimental results on four Landsat 8 OLI images show that the MII provides better LCTM results than the traditional SM methods. [ABSTRACT FROM AUTHOR]

In this paper, a modified Competitive Mechanism Multi-Objective Particle Swarm Optimization (MCMOPSO) algorithm is presented for multi-objective optimization. The algorithm consists of an improved leader selection scheme called multi-competition leader selection. Under this scheme, particles move to the winner among the elite particles for the social cognitive by comparing the nearest angle or the farthest angle of several randomly selected elite particles. Besides, as the inertia weight plays an important role in controlling the previous velocity of each particle, the competitive mechanism is applied to the inertia weight in order to investigate for the most suitable balance between the exploration and exploitation abilities of the algorithm during the search process. The experimental results show that the proposed algorithm outperforms four other popular multi-objective particle swarm optimization algorithms most of the time on thirty-seven benchmarks in terms of inverted generational distance. Furthermore, the proposed algorithm is applied to the signalized traffic problem to optimize the effective green time of each phase, and the proposed algorithm performs better than other MOPSO algorithms for the traffic problem in terms of hypervolume. [ABSTRACT FROM AUTHOR]

The paper proposed a Personalize Parking Guidance Service(PPGS), in which a bi-level programming model was built to describe the relationship between the Personalized Parking Guidance Information System and drivers. The upper-level of the model aimed to achieve the efficetive and balanced utilization of parking resonurces during peak time, while the lower-level model was to minimize the driver's walking distance after parking.A nested Particel Swarm Optimization algorithm was used to solve the proposed model. The simulation results of the model show that the peak congestion time has been reduced remarkably under the guidance of the proposed model. The Mean value of Unocuupied Parking Difference Index(MUPDI) curves trend to decline during the overall process. It means that within the acceptable walking distance, the proposed parking lots allocation model can effectively balance the utilization of parking resources shared in the service area and minimize walking distance as well. [ABSTRACT FROM AUTHOR]

With the increasing global demands for energy, fuel supply management is a challenging task of today's industries in order to decrease the cost of energy and diminish its adverse environmental impacts. To have a more environmentally friendly fuel supply network, Liquefied Natural Gas (LNG) is suggested as one of the best choices for manufacturers. As the consumption rate of LNG is increasing dramatically in the world, many companies try to carry this product all around the world by themselves or outsource it to third-party companies. However, the challenge is that the transportation of LNG requires specific vessels and there are many clauses in related LNG transportation contracts which may reduce the revenue of these companies, it seems essential to find the best option for them. The aim of this paper is to propose a meta-heuristic Binary Particle Swarm Optimization (BPSO) algorithm to come with an optimized solution for ship routing and scheduling of LNG transportation. The application demonstrates what sellers need to do to reduce their costs and increase their profits by considering or removing some obligations. [ABSTRACT FROM AUTHOR]

The droop control approach is widely used in case of parallel operation of DC sources. The conventional droop control method is realized by linearly reducing the load voltage as the load current increases. This droop control is unable to achieve effective equal load sharing as well as the bus voltage regulation. The load sharing error is enhanced when cable line parameters of parallel-connected DC sources are not equal. The droop gain and nominal voltage reference of source converters are the key parameters to achieve the bus voltage deviation within the permissible limits and effective load sharing. In this paper, the droop parameters of the proposed distributed control scheme are optimized with the help of the Particle Swarm Optimization technique for the aforementioned control objectives of the DC microgrid for unequal cable line resistances. The performance of the proposed control scheme based on optimal droop parameters is verified through MATLAB/Simulink environment and validated by a hardware-in-the-loop real-time simulator based on the dSPACE 1202 platform. [ABSTRACT FROM AUTHOR]

This paper addresses the tracking control problem of a single-arm manipulator actuated by Pneumatic Artificial Muscle (PAM) subjected to perturbation in parameters. The super-twisting sliding-mode control (STSMC) has been designed for the PAM system and the stability analysis has been presented to prove the asymptotic convergence of tracking errors. A perturbation observer-based on sliding-mode methodology has been proposed to estimate the uncertainty in system parameters and it is combined with STSMC to establish a robust controller against variation in parameters and to improve the capability of SMC in terms of chattering reduction. The stability analysis based on Lyapunov showed that the estimation error could converge asymptotically and the closed-loop system of an observer-based controlled PAM manipulator is asymptotically stable. Also, to improve the performance of the observer-based controlled system, a modern optimization technique based on Particle Swarming Optimization (PSO) has been suggested and developed for tuning the design parameters of the super-twisting sliding mode-controlled PAM robot based on Sliding-Mode Perturbation Observer (SMPO). The computer simulation based on MATLAB programming format showed the effectiveness of the both proposed observer and controller. [ABSTRACT FROM AUTHOR]

The use of meta-heuristic methods for solving nonlinear engineering and optimization problems is one of the paramount topics that attracted the attention of the researchers. Particle swarm optimization (PSO) is an optimization algorithm which has inspired by birds flocking. However, like other methods, PSO has some disadvantages such as problems in finding the best global minimum or trapping in the local minima in some special problems. In some works, the initial particles are randomly set using uniform or Gaussian distributions. These particles sometimes fail to cover the search space completely. The main goal of this paper is to improve the mechanism of the initial population production stage in the first step to cover the feasible space properly. So, with the help of the scrambled Halton sequence and producing quasi-random numbers, the initial population has been generated in a mathematical way without making a lot of change in the original PSO algorithm and its structure. These particles cover the search space more efficiently. This new hybrid algorithm is named the Halton-PSO in this research. The results show that Halton-PSO improves the ability and efficiency of PSO. The performance and ability of the proposed Halton-PSO algorithm have been examined by 11 benchmark functions and 7 different nonlinear engineering problems. Both of the optimization results of test functions and the real problems demonstrate that the Halton-PSO method is more successful than the original PSO and the PSO family algorithms and the other methods for distinguishing the global best minimum. [ABSTRACT FROM AUTHOR]

In the research of energy internet, demand response has become a hot issue which has been widely concerned. Smart home energy management system, as a necessary means to realize the demand response, has become the focus of research. A smart home energy management system with multilayer structure is designed in this paper, which includes the interface layer, the control layer and the load layer. The interface layer is the human machine interface (HMI), the control layer is the central controller, and the load layer contains loads of various electrical equipment. Optimal scheduling model for smart home energy management system is constructed, which takes into account factors such as environmental change, electricity price, user habits, load fluctuation and so on. The fusion algorithm of harmony search algorithm and particle swarm optimization algorithm is used to solve the model, which got the program to meet the needs of users. The simulation results showed that the load curve was effectively improved, and the electricity cost was obviously reduced. [ABSTRACT FROM AUTHOR]

Bayesian network is an effective representation tool to describe the uncertainty of the knowledge in artificial intelligence. One important method to learning Bayesian network from data is to employ a search procedure to explore the space of networks and a scoring metric to evaluate each candidate structure. In this paper, a novel discrete particle swarm optimization algorithm has been designed to solve the problem of Bayesian network structures learning. The proposed algorithm not only maintains the search advantages of the classical particle swarm optimization but also matches the characteristics of Bayesian networks. Meanwhile, mutation and neighbor searching operators have been used to overcome the drawback of premature convergence and balance the exploration and exploitation abilities of the particle swarm optimization. The experimental results on benchmark networks illustrate the feasibility and effectiveness of the proposed algorithm, and the comparative experiments indicate that our algorithm is highly competitive compared to other algorithms. [ABSTRACT FROM AUTHOR]

This paper presents two inventory models for ameliorating items under crisp and interval environments. In these models, three-parameter Weibull distribution is considered to represent both the amelioration and deterioration rates. In crisp, an inventory model is formulated for ameliorating item with fixed values of different inventory parameters. Due to uncertainty, these parameters may not be fixed. In this context, another inventory model with interval valued parameters is developed. Also, demand is dependent on the selling price and advertisement frequency of the product. The corresponding profit maximization problem has been developed. For solving the problem, different variants of quantum behaved particle swarm optimization technique (QPSO) are applied. To validate the proposed models, two numerical examples are considered and solved. The results are compared for different variants of QPSO techniques. Finally, graphical sensitivity analyses are presented to study the impact of several system parameters on cycle length, initial stock level along with average profit for both the models. [ABSTRACT FROM AUTHOR]

A hybrid particle swarm optimization algorithm for determining extrinsic parameters of the camera, with the intrinsic parameters known, is presented in this paper. An adaptive inertia weighting strategy is designed, and improved crossover and mutation operation are performed. Taking the result of the particle swarm optimization algorithm as the initial value, the extrinsic parameters of the camera are optimized by Gauss–Newton method. The simulation results demonstrate that, when the standard deviation of image noise is 0.1 pixels, the mean relative error of rotation angle is less than 0.04844% and the mean relative error of translation vector is less than 0.05583%. The experimental results show that the mean reprojection error of the proposed algorithm is no more than 0.06045 pixels. The algorithm can achieve high precision and robustness in practical applications. Besides, the algorithm is computational efficient. [ABSTRACT FROM AUTHOR]

Blue–green colour cast effect and low contrast are common problems suffered by deep underwater images. This paper introduces a new method which consists of two major steps: red channel correction based on green and blue channels (RCCGB), and simultaneous contrast stretching and mean pixel enhancement (SCSMPE). The RCCGB is designed to minimize the effect of blue–green illumination. This step considers the differences between the red channel and other channels in terms of total pixel values. The second major step, SCSMPE is specifically designed to perform contrast stretching and improve the mean pixel value simultaneously through particle swarm optimization (PSO). Based on the visual observation, the proposed method significantly reduces the effect of the blue–green colour cast and improves the image contrast. Furthermore, the average quantitative values for 300 underwater images also demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid optimization algorithm developed through the novel combination of particle swarm optimization (PSO) oriented bacteria foraging optimization algorithm (BFOA) for finest tuning of proportional integral derivative (PID) controllers for load frequency control problem of hybrid power system comprising of photovoltaic (PV) system and thermal generators. In order to prevent the solution from sub-optimal condition and to accelerate the convergence time of the problem the PSO is merged with BFOA to solve the LFC problem of the hybrid system. To check the power of the proposed strategy initially the hybrid system is evaluated for step load change and the results are compared with some powerful optimization algorithms such as BFOA, PSO, and flower pollination algorithm (FPA) based PID in terms of computed gains and achieved value of error. The results obtained show the cost and control efficacy of the proposed design. The study is extended to investigate the performance of proposed design with dead-band of governor and generation rate constraint non-linearity. Finally, the robustness of the proposed design is evaluated for random load change, sinusoidal load change and for extensive parametric variations from the typical plant values and the application results are presented. [ABSTRACT FROM AUTHOR]

Atmospheric duct propagation has a significant influence on the performance of wireless communication and radar systems in the maritime environment that makes it essential to master the detailed knowledge of the atmospheric refractivity profile. Automatic identification system (AIS) is a maritime navigation safety communication system that operates in the very high frequency mobile band and the propagation path of AIS signals will be influenced by different atmospheric conditions. In this paper, a new refractivity estimation method based on the AIS signal level is proposed and the Levy flight quantum-behaved particle swarm optimization (LFQPSO) algorithm is presented and applied in the new estimation method to estimate the surface based duct parameters. Numerical simulations demonstrate that the LFQPSO algorithmhas good robustness and the new refractivity estimation method based on the AIS signal level can provide near-real-time estimation ofatmospheric refractivity. [ABSTRACT FROM AUTHOR]

There is a variety of fuzzy classifiers, one of which is Adaptive Neuro-Fuzzy Inference system (ANFIS) classifier. One of the main challenges in designing such data classifiers is selection of effective and appropriate type and location of membership functions and its training method to reduce the classification error. In this paper, a new technique (based on intelligent methods) is presented and implemented to select and locate the membership functions and simultaneous training using a new method based on Inclined Planes System Optimization (IPO) to minimize errors of an ANFIS classifier for the first time. The presented method is evaluated for classification of data sets with different reference classes and different length feature vectors, which have acceptable complexity. According to the results of the research, the presented method has a higher level of accuracy and efficiency in selecting the type and location of membership functions (based on intelligent methods) and simultaneous training with IPO, compared to other methods, such as particle swarm optimization, genetic algorithm, differential evolution, and ACOR algorithms. [ABSTRACT FROM AUTHOR]

An R2 indicator-based multi-objective particle swarm optimiser (R2-MOPSO) can obtain well-convergence and well-distributed solutions while solving two and three objectives optimisation problems. However, R2-MOPSO faces difficulty to tackle many-objective optimisation problems because balancing convergence and diversity is a key issue in high-dimensional objective space. In order to address this issue, this paper proposes a novel algorithm, named R2-MaPSO, which combines the R2 indicator and decomposition-based archiving pruning strategy into particle swarm optimiser for many-objective optimisation problems. The innovations of the proposed algorithm mainly contains three crucial factors: (1) A bi-level archiving maintenance approach based on the R2 indicator and objective space decomposition strategy is designed to balance convergence and diversity. (2) The global-best leader selection is based on the R2 indicator and the personal-best leader selection is based on the Pareto dominance. Meanwhile, the objective space decomposition leader selection adopts the feedback information from the bi-level archive. (3) A new velocity updated method is modified to enhance the exploration and exploitation ability. In addition, an elitist learning strategy and a smart Gaussian learning strategy are embedded into R2-MaPSO to help the algorithm jump out of the local optimal front. The performance of the proposed algorithm is validated and compared with some algorithms on a number of unconstraint benchmark problems, i.e. DTLZ1-DTLZ4, WFG test suites from 3 to 15 objectives. Experimental results have demonstrated a better performance of the proposed algorithm compared with several multi-objective particle swarm optimisers and multi-objective evolutionary algorithms for many-objective optimisation problems. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) specifically from solar energy is gaining much attention. Integration of solar photo-voltaic plant (SPP) on distribution grid increases flexibility of the system but lacking behind in terms of convention protection scheme. The impact of orientation of point of common coupling (PCC) of SPP on distribution grid changes the entire power flow computation and hence, coordination of protection devices. This paper presents the power flow computation on modified IEEE 4-bus system for different integrated positions of SPP. Further analysis is carried for the fault current level with different locations and number of SPP DG. It has been found that DG integration increases the magnitudes of fault current; hence, it will have impact on relay co-ordination. A MATLAB/Simulink model is proposed for relay mis-coordination and optimal co-ordination by implementing evolutionary optimization technique. The Proposed study on IEEE 4-bus system along with three units 100 kW capacity of SPP is used to derived optimal co-ordination solution which validates the obtained results on electrical transient's analysis programming platform (ETAP). [ABSTRACT FROM AUTHOR]

This paper represents a new methodology to improve the performance of population-based optimization algorithms designed for corrective and preventive control actions enhancing power system dynamic security. Unlike many adaptive approaches employing the parameters such as population size, crossover or mutation rates, the proposed method for the performance improvement is based on the reduction of the search space size. In the method, optimization algorithms run consecutively, while the size of the search space is reduced according to the objective function values attained during the optimization process. In this study, generation rescheduling combined with load curtailment is applied as a preventive control action, whereas load shedding is selected as a corrective control. Each of these control actions is determined through the formulation of a security constrained optimization problem and its solution via population-based optimization algorithms. The proposed methodology is successfully applied to differential evolution, particle swarm optimization, artificial bee colony optimization, and big bang big crunch optimization methods for solving the optimization problems in a 16-generator-68-bus system and the Turkish Transmission System with 750 generators and 2600 buses. It is demonstrated that the proposed method provides better solutions with lesser computational complexity than the ones obtained by using fixed search space sizes. [ABSTRACT FROM AUTHOR]

This paper proposes an effective intrusion detection method, named I-AHSDT, which is the combination of the Adaptive Dynamic Directive Operative Fractional Lion clustering (ADDOFL) and Hyperbolic Secantbased Decision Tree classifier (HSDT). The proposed HSDT classifier is based on the inverse hyperbolic secant function and it performs the two level classification to detect the intrusion, which offers robust classification performance. The experimentation is performed using the KDD Cup 1999 data, and the HCR Lab data set, and the experimental results prove that the proposed method outperforms the existing system in terms of the accuracy, which is 0.95. [ABSTRACT FROM AUTHOR]

This paper proposes a new variant of Particle Swarm Optimization (PSO), dubbed CentroidPSO, to tackle data classification problem in high dimensional domains. It is inspired by the center-based sampling theory, which states that the center region of a search space contains points with higher probability to be closer to the optimal solution. The experimental results show striking performance of the CentroidPSO as compared to the standard PSO, four closely related PSO variants, and three recent evolutionary computation approaches. Moreover, a comparison with three machine learning approaches indicate that the CentroidPSO is a very competitive and promising classifier. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective optimization methodology that applies the Non-dominated Sorting Genetic Algorithm-II(NSGA-II) to propeller design, and realizes Fluid-Structure Interaction (FSI) weak-coupling based on Panel Method (PM) and the Finite Element Method (FEM). The FSI iterative process and the convergent pressure coefficient distribution and pressure fluctuation of HSP (a propeller installed on a Japanese bulk freighter - Seiun-Maru) are numerical calculated. The FSI results turn out to have higher precision than those without FSI. The appropriate optimization parameters are chosen after studying five cases. The Sobol method, a global Sensitivity Analysis (SA) algorithm, is used to quantify the dependence of objectives and constraints on the input parameters. In the multi-objective optimization methodology, efficiency, unsteady force, and mass are chosen as optimum objectives under certain constraints. Effectiveness and robustness of the methodology are validated by running the program starting from four different random values, which all improve the objectives and converge to the similar results. The proposed multi-objective optimization methodology could be a promising tool for propeller design to help improve design efficiency and ability in the future. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing (HU) is an important technique for extracting materials and their abundance in hyperspectral remote sensing imagery. The presence of nonlinear mixing of light on the ground poses a difficult problem when estimating abundance fractions of all pixels. This problem makes the foundation of algorithms that can adapt all types of nonlinear mixing on the ground more complex and challenged. In this paper, a new bionic intelligent algorithm named crossover double particle swarms optimization (CDPSO) has been presented to estimate abundance for hyperspectral remote sensing imagery. The reconstruction error is used as the objective function for HU based on multilinear mixing model, and the nonlinear unmixing is transformed into an optimization problem. By improving the optimization performance of PSO for HU, we embed two types of new strategies, including double particle swarms crossover and swarm re-initialization, respectively. Our experiments, conducted using both synthetic and real hyperspectral data, demonstrate that the proposed CDPSO algorithm can outperform other state-of-the-art unmixing methods. [ABSTRACT FROM AUTHOR]

The particle swarm optimisation (PSO) is a stochastic, optimisation technique based on the movement and intelligence of swarms. In this paper, three new effective optimisation algorithms BPSO, HPSO and WPSO, by incorporating some decision criteria into PSO, have been proposed and analysed both in terms of their efficiency, resistance to the problem of premature convergence and the ability to avoid local optima. In the new algorithms, for each particle except position, two sets of velocities are generated and the profit matrix is constructed. Using the decision criteria the best strategy is selected. Simulations for benchmark test nonlinear function show that the algorithms in which the decision criteria have been applied, are beneficial over classical PSO in terms of their performance and efficiency. [ABSTRACT FROM AUTHOR]

High frequency (HF) transformer at the isolation stage of SST plays a vital role in deciding the power conversion efficiency of the SST system. This paper proposes a new design procedure for optimization of HF transformer used in SST applications such that it fulfils all the requirements pertaining to its operating conditions. The novel design procedure proposed is based on optimizing the core geometry (in 5th power of cm), which has direct influence on regulation and copper loss. An isolated half-bridge DC-DC converter topology is chosen for implementing the proposed design. Four bio-inspired algorithms, namely Particle swarm optimization (PSO), Whale Optimization algorithm (WOA), Dragonfly Algorithm (DA), and Ant Lion Optimizer (ALO) are used to solve the optimization problem and the results are compared with Genetic algorithm (GA). Finally, the optimization results are validated through PowerEsim, a web-based testing platform with huge database of real-time components from leading manufacturers across the globe. [ABSTRACT FROM AUTHOR]

The job shop scheduling problem (JSSP) is an important NP-hard practical scheduling problem that has various applications in the fields of optimization and production engineering. In this paper an effective scheduling method based on particle swarm optimization (PSO) for the minimum makespan problem of the JSSP is proposed. New variants of the standard PSO operators are introduced to adapt the velocity and position update rules to the discrete solution space of the JSSP. The proposed algorithm is improved by incorporating two neighborhood-based operators to improve population diversity and to avoid early convergence to local optima. First, the diversity enhancement operator tends to improve the population diversity by relocating neighboring particles to avoid premature clustering and to achieve broader exploration of the solution space. This is achieved by enforcing a circular neighboring area around each particle if the population diversity falls beneath the adaptable diversity threshold. The adaptive threshold is utilized to regulate the population diversity throughout the different stages of the search process. Second, the local search operator based on critical path analysis is used to perform local exploitation in the neighboring area of the best particles. Variants of the genetic well-known operators “selection” and “crossover” are incorporated to evolve stagnated particles in the swarm. The proposed method is evaluated using a collection of 123 well-studied benchmarks. Experimental results validate the effectiveness of the proposed method in producing excellent solutions that are robust and competitive to recent state-of-the-art heuristic-based algorithms reported in literature for nearly all of the tested instances. [ABSTRACT FROM AUTHOR]

This paper introduces a Markov model in Phase II profile monitoring with autocorrelated binary response variable. In the proposed approach, a logistic regression model is extended to describe the within-profile autocorrelation. The likelihood function is constructed and then a particle swarm optimization algorithm (PSO) is tuned and utilized to estimate the model parameters. Furthermore, two control charts are extended in which the covariance matrix is derived based on the Fisher information matrix. Simulation studies are conducted to evaluate the detecting capability of the proposed control charts. A numerical example is also given to illustrate the application of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, optimal design of single-ring and multi-ring circular array, hexagonal array, and elliptical array of isotropic antenna has been carried out using Simplex Particle Swarm Optimization (Simplex-PSO). The synthesis of the array geometry is first formulated as an optimization problem with the goal of side lobe level reduction and is then solved using Simplex-PSO algorithm for optimum current excitations. To overcome the premature convergence characteristic of PSO, Simplex-PSO is considered while preserving the fast converging property of PSO. Two design examples are presented which illustrate the effectiveness of Simplex-PSO algorithm, and the optimization goal for each example is easily achieved. The design results obtained using Simplex-PSO are superior to those obtained using popular algorithms like PSO and its variant craziness-based PSO (CRPSO) and PSO with aging leader and challengers (ALC-PSO) in a statistically significant way. [ABSTRACT FROM AUTHOR]

In recent years sampling approaches have been used more widely than optimization algorithms to find parameters of conceptual rainfall–runoff models, but the difficulty of calibration of such models remains in dispute. The problem of finding a set of optimal parameters for conceptual rainfall–runoff models is interpreted differently in various studies, ranging from simple to relatively complex and difficult. In many papers, it is claimed that novel calibration approaches, so-called metaheuristics, outperform the older ones when applied to this task, but contradictory opinions are also plentiful. The present study aims at calibration of two simple lumped conceptual hydrological models, HBV and GR4J, by means of a large number of metaheuristic algorithms. The tests are performed on four catchments located in regions with relatively similar climatic conditions, but on different continents. The comparison shows that, although parameters found may somehow differ, the performance criteria achieved with simple lumped models calibrated by various metaheuristics are very similar and differences are insignificant from the hydrological point of view. However, occasionally some algorithms find slightly better solutions than those found by the vast majority of methods. This means that the problem of calibration of simple lumped HBV or GR4J models may be deceptive from the optimization perspective, as the vast majority of algorithms that follow a common evolutionary principle of survival of the fittest lead to sub-optimal solutions. [ABSTRACT FROM PUBLISHER]

The Gbest-guided artificial bee colony (GABC) algorithm is a latest swarm intelligence-based approach to solve optimisation problem. In GABC, the individuals update their respective positions by drawing inspiration from the global best solution available in the current swarm. The GABC is a popular variant of the artificial bee colony (ABC) algorithm and is proved to be an efficient algorithm in terms of convergence speed. But, in this strategy, each individual is simply influenced by the global best solution, which may lead to trap in local optima. Therefore, in this paper, a new search strategy, namely “Fully Informed Learning” is incorporated in the onlooker bee phase of the ABC algorithm. The developed algorithm is named as fully informed artificial bee colony (FABC) algorithm. To validate the performance of FABC, it is tested on 20 well-known benchmark optimisation problems of different complexities. The results are compared with those of GABC and some more recent variants of ABC. The results are very promising and show that the proposed algorithm is a competitive algorithm in the field of swarm intelligence-based algorithms. [ABSTRACT FROM PUBLISHER]

Derivative-free optimization is an area of long history which has so many applications in different fields. It has lately received considerable attention within the engineering community. This paper describes a random derivative-free algorithm for solving unconstrained or bound constrained continuously differentiable non-linear problems. This method is a combination of particle swarm and directional direct search algorithms. The key difference in direct search methods is in the way of generating positive bases. At first glance, a simple way of generating positive bases has been introduced for solving continuously differentiable problems. Then, it has been shown that using the particle swarm algorithm with a direct search algorithm can solve non-linear optimization problems efficiently. Some standard examples have been presented to demonstrate the ability and effectiveness of this approach. [ABSTRACT FROM AUTHOR]

Many industrial applications need less than 6 degrees of freedom (DOF) to manipulate. On one hand, the parallel manipulators (PMs) with lower DOF have some advantages such as low manufacturing and control cost. On the other hand, they have more complicated kinematics. Among PMs with lower DOF, the 5-DOF PMs have particularly many industrial applications. Within the group of 5-DOF PMs, the three translational and two rotational (3T2R) type has more industrial applications than three rotational and two translational type. In this paper, we analyze and optimize the 5-RPUR PM which is a 3T2R type. The kinematic analysis is studied using the screw theory. The boundary and volume of the workspace is obtained using a geometrical method. In addition, singularity analysis is studied. Particle swarm optimization is utilized to optimize the workspace and the accuracy simultaneously, and achieve a roughly homogeneous accuracy index over the workspace. The boundary of the optimized workspace is depicted and we show that the optimized workspace is singularity free. The accuracy index of the manipulator is calculated and depicted in several cross-sections of the workspace. [ABSTRACT FROM AUTHOR]