Basic Particle Swarm Optimization Research Articles

Optimal energy management system for distribution systems using simultaneous integration of PV-based DG and DSTATCOM units

The energy management system (EMS) of an electrical distribution system (EDS), with the integration of distributed generation (DG) and distribution static compensator (DSTATCOM), provides numerous benefits and significantly differs from the existing EDSs. This paper presents an optimal integration of DG based on photovoltaic (PV) solar panels and DSTATCOM in EDS. A single objective function, based on maximizing the active power loss level (APLL) in EDS, is deployed to find the optimal size and location of photovoltaic DG and DSTATCOM simultaneously in different study cases using various particle swarm optimization (PSO) algorithms. These PSO algorithms are the basic PSO, adaptive acceleration coefficients PSO (AAC-PSO), autonomous particles groups for PSO (APG-PSO), nonlinear dynamic acceleration coefficients PSO (NDAC-PSO), sine cosine acceleration coefficients PSO (SCAC-PSO), and time-varying acceleration PSO (TVA-PSO). These algorithms are applied to the standard IEEE 33- and 69-bus EDSs, which are used as test systems to verify the effectiveness of the proposed algorithms. Simulation results prove that the TVA-PSO algorithm exhibits higher capability and efficiency in finding optimum solutions. Comparing the simulation results attained for different study cases leads to the conclusion that DG and DSTATCOM were optimally-allocated simultaneously, which resulted in a significant reduction of power losses and an enhancement of the voltage profile.

Integrated continuous berth allocation and quay crane assignment and scheduling problem with time-dependent physical constraints in container terminals

This paper presents an integer programming model for integrated continuous berth allocation, quay crane assignment and quay crane scheduling problem (BACASP) in container terminals where berthing possibility depends on water depth and tide conditions. The proposed model also considers the safe distance between quay cranes and the fact that they cannot cross each other on the rails. Given the NP-Hard complexity of the proposed model, a particle swarm optimization (PSO) based meta-heuristic called the random topology particle swarm optimization algorithm (RTPSO) is developed for solving its large-size instances. To evaluate the performance of the developed RTPSO, its results are compared with the results of the exact solution and the basic PSO. The results illustrate the better performance of the proposed random topology particle swarm optimization algorithm in terms of accuracy and computational time.

Multi-objective economic operation of modern power system considering weather variability using adaptive cuckoo search algorithm

Currently, most of the power systems are being integrated with flexible AC transmission system devices and renewable energy sources for operating with enhanced security margins and balancing the increasing demand cost-effectively. On the other side, the trend of increasing global warming and extremely changing weather conditions is continuing across the world. Under this scenario, it is essential to realize their effect on various power system components and its economic operation. In this paper, the parameters namely resistance of the transmission line/transformer, load and solar photovoltaic generation are modeled considering ambient temperature effect. Later, economic schedule under changing weather conditions is proposed for attaining multi-objectives simultaneously like total operating cost of conventional energy, real power loss, average voltage collapse point indicator index and average voltage deviation index. Also, the dispatchable problems in the transmission system and various practical operating constraints are handled via optimally setting the parameters of optimal unified power flow controller. The optimization problem is solved using adaptive cuckoo search algorithm (ACSA), in which a dynamically increasing switching parameter in a power of three is adopted for adjusting the random walk between local optima and global optima. The superiority of the proposed ACSA in solving the multiobjective, nonlinear complex optimization problem over basic CSA and particle swarm optimization, chicken swarm optimization and flower pollination algorithm is presented by illustrating various case studies on standard IEEE 14, 30 and 118–bus test systems.

The particle swarm optimization (PSO) algorithm application – A review

Particle Swarm Optimization (PSO) is one of the concepts of swarm intelligence inspired by studies in neurosciences, cognitive psychology, social ethology and behavioural sciences, introduced in the domain of computing and artificial intelligence as an innovative collective and distributed intelligent paradigm for solving problems, mostly in the domain of optimization, without centralized control or the provision of a global model. The PSO method has roots in genetic algorithms and evolution strategies and shares many similarities with evolutionary computing such as random generation of populations at system initialization or updating generations at optima search. This paper presents an extensive literature review on the concept of PSO, its application to different systems including electric power systems, modifications of the basic PSO to improve its premature convergence, and its combination with other intelligent algorithms to improve search capacity and reduce the time spent to come out of local optimums.

Improved quantum evolutionary particle swarm optimization for band selection of hyperspectral image

ABSTRACT The large number of bands, the huge amount of information and the high band correlation in hyperspectral images bring great difficulties to the band selection of hyperspectral images. In the basic Particle Swarm Optimization (PSO), the learning factor and inertia factor are fixed, which limits the exploratory ability of the algorithm and cannot balance the global and local relations well. Therefore, a new algorithm-Improved Quantum Evolutionary Particle Swarm Optimization (IQEPSO) is proposed that the learning factor and inertia factor could be changed with the number of iterations. At the same time, mutation probability could be changed with the number of invariable fitness values, in order to enable particles to jump out of the local optimum. The proposed algorithm is applied to band selection of hyperspectral images. Experiments show that the proposed algorithm can improve the classification accuracy of ground objects. The disadvantage of falling into local optimum is overcome, the convergence speed is accelerated, and better classification accuracy is obtained.

Yield and energy optimization of the continuous catalytic regeneration reforming process based particle swarm optimization

Yield and energy optimization based particle swarm optimization of the reforming process was carried out to maximize the reformate and energy saving in order to perform the process improvement. Data from the existing process was extracted, and then a development of the mathematical model that represented the full process was investigated. The analysis showed two enormous opportunities for enhancing productivity via the improvement of the reactions network and separation process, and for energy saving at the heat exchangers network via pinch analysis approach. Improvements analyses of the existing process to achieve the optimum yield of reformate included modifications of inlet temperature, inlet pressure and molar ratio hydrogen to hydrocarbon according to enhancement in the process from 93.91% to 94.46%. The maximum energy savings of heat exchangers network indicated that reduction of utilities around 16.20%, which led to an addition to an incremental area according to the reduction in ΔT min from 80.3 K to 65 K to the existing heat exchangers. The final evaluation of yield-energy optimization of the modified process indicated an enhancement of productivity by 8700 t/y and the energy saving around 99.81 GJ/h, which led to a process profit increment of approximately USD$ 6.03 M/y. • Yield and energy optimization of the CCRRP were conducted as a new method. • Particle swarm optimization coupled with pinch analysis approach was implemented. • Maximum yield reached to 94.51% and maximum energy savings was around 16.20%. • Optimal modifications of pressure, temperature and molar ratio H/HC were estimated. • Evaluation of the CCRRP indicated the profit of approximately USD$ 6.03 million/year.

Assignment of duplicate storage locations in distribution centres to minimise walking distance in order picking

With the rapid development of e-commerce, the orders processed in B2C warehouses are characterised by heterogeneous and small volume. The traditional storage assignment strategies used in the picker-to-parts warehouses do not have advantage any more. In this case, the scattered storage strategy is a good alternative. In this paper, we study a new scattered storage strategy that allows the same product to be placed in multiple storage locations. The correlation between products which reflects how frequently any two products will be ordered together in the same order is considered. The problem is formulated as a 0-1 integer programming model to minimise the weighted sum of distances between the products, with weight being the elements of the correlation matrix. To solve large-scale problems, a GA and a basic PSO algorithm are developed. To improve solution quality, a new PSO algorithm based on the problem characteristic is designed and a hybrid algorithm combing it with GA is proposed. Experiments show that the solutions of these algorithms are close to the optimal solutions for the small-sale problems. For larger problems, the specially designed new PSO greatly improves solution quality as compared to the basic algorithms and the hybrid algorithm makes further improvement.

Application of PID optimization control strategy based on particle swarm optimization (PSO) for battery charging system

Abstract The battery charging process has nonlinear and hysteresis properties. PID (Proportion Integration Differentiation) control is a conventional control method used in the battery charging process. The control effect is determined by the PID control parameters ${K}_p$, ${K}_i$ and ${K}_d$. The traditional PID parameter setting method is difficult to give the appropriate parameters, which affects the battery charging efficiency. In this paper, the particle swarm optimization (PSO) is used to optimize the PID parameters. Aiming at the defects of basic PSO, such as slow convergence speed, low convergence precision and easy to be premature, a modified particle swarm optimization algorithm is proposed, and the optimized PID parameters are applied to the battery charging control system. Also, the experimental results show that the battery charging process possesses better dynamic performance and the charging efficiency of the battery has increased from 86.44% to 91.47%, and the charging temperature rise has dropped by 1°C.

Application of particle swarm optimization to water management: an introduction and overview.

Particle swarm optimization (PSO) is a stochastic population-based optimization algorithm inspired by the interactions of individuals in a social world. This algorithm is widely applied in different fields of water resources problems. This paper presents a comprehensive overview of the basic PSO algorithm search strategy and PSO's applications and performance analysis in water resources engineering optimization problems. Our literature review revealed 22 different varieties of the PSO algorithm. The characteristics of each PSO variety together with their applications in different fields of water resources engineering (e.g., reservoir operation, rainfall-runoff modeling, water quality modeling, and groundwater modeling) are highlighted. The performances of different PSO variants were compared with other evolutionary algorithms (EAs) and mathematical optimization methods. The review evaluates the capability and comparative performance of PSO variants over conventional EAs (e.g., simulated annealing, differential evolution, genetic algorithm, and shark algorithm) and mathematical methods (e.g., support vector machine and differential dynamic programming) in terms of proper convergence to optimal Pareto fronts, faster convergence rate, and diversity of computed solutions.

Optimizing the layout of onshore wind farms to minimize noise

As wind farm numbers and areas increase worldwide, it has become increasingly important to examine the impact of wind energy on the surrounding environment. One effect in some scenarios is noise, which depends on the type and age of the wind turbines and the distances between them and the residential buildings. Previous research on wind farm layout optimization has been generally aimed at achieving the minimum investment cost or maximum captured energy. This approach does not entirely align with minimizing noise. This paper focuses on an optimal layout for a wind farm considering its noise, without sacrificing power production. By optimizing the wind farm layout, the minimum noise is set as the basic objective, and both the wake effect and distances among wind turbines are considered. The basic particle swarm optimization algorithm and its evolutionary version are adopted and compared for better performance of calculation cost. Two strategies are presented to address the problems in various scenarios and to demonstrate the applicability of the proposed method and its effectiveness in designing layouts that minimize noise. Compared to a reference layout, a stringent noise control strategy could reduce the noise by 11%, even if minor, and increase the power production by 3.1%. A flexible strategy could reduce the noise by 5.7% and increase the power production by 3.1%.

An immune-based response particle swarm optimizer for knapsack problems in dynamic environments

This paper proposes a novel binary particle swarm optimization algorithm (called IRBPSO) to address high-dimensional knapsack problems in dynamic environments (DKPs). The IRBPSO integrates an immune-based response strategy into the basic binary particle swarm optimization algorithm for improving the quantity of evolutional particles in high-dimensional decision space. In order to enhance the convergence speed of the IRBPSO in the current environment, the particles with high fitness values are cloned and mutated. In addition, an external archive is designed to store the elite from the current generation. To maintain the diversity of elites in the external archive, the elite of current generation will replace the worst one in the external archive if and only if it differs from any of the existing particles in the external archive based on the Hamming distance measurement when the archive is due to update. In this way, the external archive can store diversiform elites for previous environments as much as possible, and so as to the stored elites are utilized to transfer historical information to new environment for assisting to solve the new optimization problem. Moreover, the environmental reaction scheme is also investigated in order to improve the ability of adapting to different kinds of dynamic environments. Experimental results on a series of DKPs with different randomly generated data sets indicate that the IRBPSO can faster track the changing environments and manifest superior statistical performance, when compared with peer optimization algorithms.

Reaction Control System Optimization for Maneuverable Reentry Vehicles Based on Particle Swarm Optimization

This paper presents a new parametric optimization design to solve a class of reaction control system (RCS) problem with discrete switching state, flexible working time, and finite-energy control for maneuverable reentry vehicles. Based on basic particle swarm optimization (PSO) method, an exponentially decreasing inertia weight function is introduced to improve convergence performance of the PSO algorithm. Considering the PSO algorithm spends long calculation time, a suboptimal control and guidance scheme is developed for online practical design. By tuning the control parameters, we try to acquire efficacy as close as possible to that of the PSO-based solution which provides a reference. Finally, comparative simulations are conducted to verify the proposed optimization approach. The results indicate that the proposed optimization and control algorithm has good performance for such RCS of maneuverable reentry vehicles.

Cold-Chain Logistics Distribution Routing Optimization Based on PSO

Recently, cold chain which aims to keep agricultural product fresh before arrive to the designated customers has been greatly developed. The distribution routing optimization problem of cold chain has been an attractive research hotspot. In this paper, we introduce the basic PSO method and it’s advantaged about good robustness, global convergence, and intelligent search. Then, we put forward a method for distribution path optimization based on PSO. Taking an example, the evaluation results show that our proposed method can achieve the optimal solution effectively.

Matching and Recognition Method of Architectural Decoration Elements Based on Hybrid Particle Swarm Optimization Algorithm

TImage matching and recognition of architectural decoration elements is the basis of architectural decoration design. The traditional matching and recognition methods have low precision and high complexity, so they are not suitable for the image matching and recognition of architectural decoration elements under complex conditions. Based on the analysis of the basic particle swarm optimization algorithm, the nonlinear asynchronous strategy is adjusted to change the fixed constant mode of the learning factor to balance the local and global search ability of the algorithm in the iterative process. At the same time, the vitality factor was introduced to mutate the inactivated particles to improve the population diversity. The experimental results show that compared with the traditional algorithm, this method improves the image matching and recognition accuracy of architectural decoration elements.

A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid

The inherent variability of large-scale renewable energy generation leads to significant difficulties in microgrid energy management. Likewise, the effects of human behaviors in response to the changes in electricity tariffs as well as seasons result in changes in electricity consumption. Thus, proper scheduling and planning of power system operations require accurate load demand and renewable energy generation estimation studies, especially for short-term periods (hour-ahead, day-ahead). The time-sequence variation in aggregated electrical load and bulk photovoltaic power output are considered in this study to promote the supply-demand balance in the short-term optimal operational scheduling framework of a reconfigurable microgrid by integrating the forecasting results. A bi-directional long short-term memory units based deep recurrent neural network model, DRNN Bi-LSTM, is designed to provide accurate aggregated electrical load demand and the bulk photovoltaic power generation forecasting results. The real-world data set is utilized to test the proposed forecasting model, and based on the results, the DRNN Bi-LSTM model performs better in comparison with other methods in the surveyed literature. Meanwhile, the optimal operational scheduling framework is studied by simultaneously making a day-ahead optimal reconfiguration plan and optimal dispatching of controllable distributed generation units which are considered as optimal operation solutions. A combined approach of basic and selective particle swarm optimization methods, PSO&SPSO, is utilized for that combinatorial, non-linear, non-deterministic polynomial-time-hard (NP-hard), complex optimization study by aiming minimization of the aggregated real power losses of the microgrid subject to diverse equality and inequality constraints. A reconfigurable microgrid test system that includes photovoltaic power and diesel distributed generators is used for the optimal operational scheduling framework. As a whole, this study contributes to the optimal operational scheduling of reconfigurable microgrid with electrical energy demand and renewable energy forecasting by way of the developed DRNN Bi-LSTM model. The results indicate that optimal operational scheduling of reconfigurable microgrid with deep learning assisted approach could not only reduce real power losses but also improve system in an economic way.

K‐PSO: An improved PSO‐based container scheduling algorithm for big data applications

SummaryIn recent years, Docker container technology is being applied in the field of cloud computing at an explosive speed. The scheduling of Docker container resources has gradually become a research hotspot. Existing big data computing and storage platforms apply with traditional virtual machine technology, which often results in low resource utilization, a long time for flexible scaling and expanding clusters. In this paper, we propose an improved container scheduling algorithm for big data applications named Kubernetes‐based particle swarm optimization(K‐PSO). Experimental results show that the proposed K‐PSO algorithm converges faster than the basic PSO algorithm, and the running time of the algorithm is cut in about half. The K‐PSO container scheduling algorithm and algorithm experiment for big data applications are implemented in the Kubernetes container cloud system. Our experimental results show that the node resource utilization rate of the improved scheduling strategy based on K‐PSO algorithm is about 20% higher than that of the Kube‐scheduler default strategy, balanced QoS priority strategy, ESS strategy, and PSO strategy, while the average I/O performance and average computing performance of Hadoop cluster are not degraded.

Improved Particle Swarm Optimization Geomagnetic Matching Algorithm Based on Simulated Annealing

As a new assistant navigation technology using geophysical field for navigation, geomagnetic matching navigation can effectively alleviate the problems such as the unavailability of satellite and the easy divergence of position data of inertial navigation system in the process of navigation. It can also carry out real-time assistant navigation with high concealment, all-around area and all-weather. According to the principle of geomagnetic matching and the geomagnetic affine model, considering that the basic particle swarm optimization algorithm is easy to fall into local extremum, this paper introduces particle swarm optimization geomagnetic matching algorithm based on simulated annealing(SAPSO) for limitations of traditional matching algorithm. What’s more, the SAPSO is improved from three parts: constraints, parameters and function of fitness. Finally, the simulation analysis is carried out from five aspects to verify the effectiveness and accuracy of the improved SAPSO.

An Adaptive Droop Control Strategy for Islanded Microgrid Based on Improved Particle Swarm Optimization

In an islanded microgrid with multiple distributed generations (DGs), the difference in line impedance may cause local voltage deviation, which leads to a series of problems such as lower power allocation accuracy and bus voltage drop under traditional droop control. In this respect, a method for optimizing the droop control using an improved particle swarm optimization (IPSO) is proposed. Firstly, the microgrid structure and influence of line parameters on traditional droop control strategy is analyzed. Then, an improved particle swarm optimization is proposed. Based on the basic particle swarm optimization (PSO) algorithm, a fuzzy inference system (FIS) is introduced to dynamically adjust the particle swarm optimization, which can effectively improve the global search ability and local search ability of the algorithm. After that, the improved algorithm is applied to the droop controller, simultaneously, the range of stable operation of the system is determined by small signal analysis. Finally, the simulation and experiment results show that the proposed improved droop control strategy can achieve accurate allocation of active and reactive power effectively while maintaining bus voltage and system frequency stability, enhance the dynamic performance and transient stability of the microgrid system.

Online PSO-tuned phase shift angle controller for dual active bridge DC–DC converter

The deep concern towards efficient and green energy has brought to the development of intermediate energy storage system. Electric vehicles (EVs), for example, is an application where the energy storage devices are vital. In EV, the bidirectional DC–DC converter is high in demand due to its nature EV of having two or more separate DC sources. A recent technology called the dual active bridge (DAB) converter has quickly become popular in DC–DC converter thanks to its inherent galvanic isolation via a high-frequency transformer. Furthermore, DAB topology allows for high-efficiency conversion and soft switching adaption. This paper presents the optimization of the phase shift angle controller of DAB using Particle Swarm Optimization (PSO). From the preliminary study, it was found out that the optimization performs well until there are abrupt changes in the system. The system is not able to respond to the reference voltage step change as the traditional one-time execution PSO have local optima entrapped in react to the changes. The paper proposes a reset function to allow PSO to identify that its current optimized value is not optimal anymore. This drives PSO to look for a new optimal value to improve DAB’s overall dynamic performance. Simulation of the modified PSO is done on a 200 kW DAB system with 20 kHz switching frequency is developed in MATLAB/Simulink environment. Simulation has been carried out with the objective to minimize the steady-state error as well as to improve the dynamic performance of the DAB. The DAB performance with the proposed solution is evaluated in terms of steady-state error and transient response by testing the system under various reference voltage and step-change input voltage. The self-excited re-exploration PSO also have been presented as to respond to the reference voltage step change as the basic PSO have local optima entrapped in react to the changes. In order to validate the simulation results, a hardware-in-the-loop (HIL) experimental circuit is built in Typhoon HIL-402 to verify the dynamic response and the steady state performance of the system.

An enhanced particle swarm optimization algorithm

<p>In this paper, an enhanced stochastic optimization algorithm based on the basic Particle Swarm Optimization (PSO) algorithm is proposed. The basic PSO algorithm is built on the activities of the social feeding of some animals. Its parameters may influence the solution considerably. Moreover, it has a couple of weaknesses, for example, convergence speed and premature convergence. As a way out of the shortcomings of the basic PSO, several enhanced methods for updating the velocity such as Exponential Decay Inertia Weight (EDIW) are proposed in this work to construct an Enhanced PSO (EPSO) algorithm. The suggested algorithm is numerically simulated established on five benchmark functions with regards to the basic PSO approaches. The performance of the EPSO algorithm is analyzed and discussed based on the test results.</p>