Hybrid Particle Swarm Research Articles

Prototype and monitoring system of phasor measurement unit based on the internet of things

This research resulting the method to reduce phasor measurement unit (PMU) amount and optimization of PMU replacement using a combination of Integer linear k-means. The first step of modeling is using a lot of PMUs that are optimized at Bendul Merisi network using integer linear k-means clustering for achieving an optimum solution of amount and replacement of PMU to be installed. The second step is estimating the uninstalled bus's power and voltage. PMU is using modified adaptive neuro-fuzzy inference system (ANFIS) of hybrid particle swarm optimization (PSO)-genetic algorithm (GA). The third step is to test and simulate the hardware design of the research for offline and online data. Research also tested network transmission of Java–Bali 500 kV. Designed simulation can calculate the active and reactive power of each bus in clusters so the total active and reactive power of each cluster can be known. Device tests to transmit data using internet of things (IoT) from a laboratory scale during 7 days have an average of 2.8 seconds while the field test required an average of 10.416 seconds during 24 hours.

A new hybrid Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithm for the design of castellated beams

A new hybrid Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithm for the design of castellated beams

A new hybrid particle swarm optimizationalgorithm for optimal tasks scheduling in distributed computing system

Tasks allocation problem in heterogeneous and distributed-multiprocessing computing environments is a nonlinear multi-objective NP-hard problem and from a research perspective, it is considereda major issue. The underlying objective of any allocation mechanism to execute the specified tasks set and enumerating processors is to minimize the overall cost. To solve the tasks allocation problems numerous meta-heuristic techniques have been tried and tested but still, plenty of scopes are there for optimal strategies. In the presented article, a comprehensive task assignment model based on particle swarm optimization (PSO) is developed, which optimizes response time, flowtime, and cost of the distributed computing system. In the present technique, the ‘n’ number of cluster centroids of ‘r’ tasks, is updated by the PSO technique to form the ‘n’ task clusters to minimize the communication costs then their allocation is made by the newly proposed heuristic method. Considering the fact, in the given model objective functions conflict with each other and the good thing about the PSO algorithm are its accuracy and speed. And to come up with the most favorable solution, authors have developed PSO based algorithm.The PSO-based technique that is proposed in this article to solve a given assignment problem in a distributed computing system is able to give better results in a manner of convergence rate as thePSO method integrates local and global search methods in an attempt to strike a balance between research and utilization. To examine the functioning of the developed technique, the well-demonstrated scheduling policies based on different techniques have been compared and obtained finer outcomes. The developed mechanism is acceptable for an erratic number of tasks and processors.

Personalized Route Recommendation with Hybrid Tabu Search Algorithm Based on Crowdsensing

In the postmodern era of tourism, tourists’ behavior has undergone a substantial change and the demand of customized experience dominates the tourism market. The traditional single-objective travel route recommendation method fails to meet the multiobjective needs of users. To handle this problem, a multiobjective hybrid tabu search algorithm for urban travel route recommendations is proposed in this paper. First, the rating and level of attractions, as well as the corresponding information of hotels and restaurants within a certain radius, are considered. Then, based on this information, a crowdsensing scoring method is established. Second, the hybrid particle swarm genetic optimization algorithm is exploited to generate a single-object route, and the fast nondominated Pareto sorting algorithm is exploited to find the optimized solution. Then, the hybrid tabu algorithm is used to optimize the personalized route chosen according to multiple objects set by users. This algorithm combines the global search ability of the genetic algorithm and the neighborhood search ability of the tabu algorithm to prevent convergence from falling into a local optimum. Finally, the experiments are conducted on the real-world data collected from the Dianping and Ctrip web sites. The comparison with baseline algorithms indicates that the algorithm proposed in this paper provides accurate and reasonable route recommendations for users.

Multi-objective Hybrid Particle Swarm Optimization and its Application to Analog and RF Circuit Optimization

Multi-objective Hybrid Particle Swarm Optimization and its Application to Analog and RF Circuit Optimization

A Hybrid Particle Swarm Optimization Algorithm with Dynamic Adjustment of Inertia Weight Based on a New Feature Selection Method to Optimize SVM Parameters

Support vector machine (SVM) is a widely used and effective classifier. Its efficiency and accuracy mainly depend on the exceptional feature subset and optimal parameters. In this paper, a new feature selection method and an improved particle swarm optimization algorithm are proposed to improve the efficiency and the classification accuracy of the SVM. The new feature selection method, named Feature Selection-score (FS-score), performs well on data sets. If a feature makes the class external sparse and the class internal compact, its FS-score value will be larger and the probability of being selected will be greater. An improved particle swarm optimization model with dynamic adjustment of inertia weight (DWPSO-SVM) is also proposed to optimize the parameters of the SVM. By improving the calculation method of the inertia weight of the particle swarm optimization (PSO), inertia weight can decrease nonlinearly with the number of iterations increasing. In particular, the introduction of random function brings the inertia weight diversity in the later stage of the algorithm and the global searching ability of the algorithm to avoid falling into local extremum. The experiment is performed on the standard UCI data sets whose features are selected by the FS-score method. Experiments demonstrate that our algorithm achieves better classification performance compared with other state-of-the-art algorithms.

Performance evaluation and optimization model for closed-loop production lines considering preventive maintenance and rework process

Closed-loop production lines with a constant number of carriers are widely encountered in today's production systems. In this paper, a production performance evaluation and optimization model is proposed for a closed-loop production line taking into account the degradation-level-dependent quality and rework process. Based on a two-machine-one-buffer decomposition method and the system state transition, a production performance evaluation method is presented. Due to the emphasis on quality management, preventive maintenance (PM) is used to ensure the reliability of the machines in the production line, increasing the effective output at minimum cost. A hybrid particle swarm optimization and estimation of distribution algorithm (PSO-EDA) is proposed to efficiently develop the optimal PM strategy. Finally, numerical experiments are performed to verify the effectiveness of the model. With the objective of maximizing the system profit, the optimization of the machines and pallets number is also explored.

Optimization of battery energy storage system size and power allocation strategy for fuel cell ship

AbstractThe fuel cell system (FCS) is commonly combined with an energy storage system (ESS) for enhancing the performance of the ship. Consequently, the battery ESS size and power allocation strategy are critical for the hybrid energy system. This paper focuses on designing a method to solve these two problems. First, a battery degradation model is employed to assess the ESS lifetime. Subsequently, the sizing problem and the optimal power allocation are integrated into a cost‐minimization problem, which is solved by a double‐loop optimization approach. The inside loop utilizes the battery degradation model to calculate ESS lifetime. In the outside loop, a power allocation strategy based on the hybrid Particle Swarm Optimization algorithm and Gray Wolf Optimization algorithm is presented. Finally, the power allocation strategy is extended to real‐time implementation by the equivalent consumption minimization strategy (ECMS) and an improved ECMS is proposed to make the FCS operates near the maximal efficiency point. Compared with ECMS, the operating cost reduces by 0.26%. The result indicates that the proposed method can optimize the ESS size efficiently, and the power allocation strategy can assure the stable operation of the fuel cell ship.

An Energy-Efficient Data Aggregation Clustering Algorithm for Wireless Sensor Networks Using Hybrid PSO

Extending the lifetime of wireless sensor networks (WSNs) and minimizing energy costs are the two most significant concerns for data transmission. Sensor nodes are powered by their own battery capacity, allowing them to perform critical tasks and interact with other nodes. The quantity of electricity saved from each sensor together in a WSN has been strongly linked to the network’s longevity. Clustering conserves the most power in wireless transmission, but the absence of a mechanism for selecting the most suitable cluster head (CH) node increases the complexity of data collection and the power usage of the sensor nodes. Additionally, the disparity in energy consumption can lead to the premature demise of nodes, reducing the network’s lifetime. Metaheuristics are used to solve non-deterministic polynomial (NP) lossy clustering problems. The primary purpose of this research is to enhance the energy efficiency and network endurance of WSNs. To address this issue, this work proposes a solution where hybrid particle swarm optimization (HPSO) is paired with improved low-energy adaptive clustering hierarchy (HPSO-ILEACH) for CH selection in cases of data aggregation in order to increase energy efficiency and maximize the network stability of the WSN. In this approach, HPSO determines the CH, the distance between the cluster’s member nodes, and the residual energy of the nodes. Then, ILEACH is used to minimize energy expenditure during the clustering process by adjusting the CH. Finally, the HPSO-ILEACH algorithm was successfully implemented for aggregating data and saving energy, and its performance was compared with three other algorithms: low energy-adaptive clustering hierarchy (LEACH), improved low energy adaptive clustering hierarchy (ILEACH), and enhanced PSO-LEACH (ESO-LEACH). The results of the simulation studies show that HPSO-ILEACH increased the network lifetime, with an average of 55% of nodes staying alive, while reducing energy consumption average by 28% compared to the other mentioned techniques.

Probabilistic optimal power dispatch in a droop controlled islanded microgrid in presence of renewable energy sources and PHEV load demand

This paper proposes a stochastic optimal power allocation strategy for a droop controlled islanded microgrid (DCIMG) with a medium X/R ratio in presence of combined heat and power-based distributed generators (DGs), renewable generators, switched capacitor banks, plug-in hybrid electric vehicle (PHEV) loads, voltage dependent electric loads and heat loads. Optimal values of stochastic power demand due to PHEV charging and discharging load are determined based on the goals of achieving target state-of-charge of PHEV batteries and effective peak shaving during peak load periods, respectively. Active power of dispatchable DGs is optimally allocated depending on the simultaneous fulfillment of an economic objective, an environmental objective along with three network related objectives. Hong's 2m+1 point estimate method (PEM) and Hong's 2m+1 PEM coupled with Nataf transformation (NT) are employed to handle the uncorrelated uncertainties accompanied with PHEV load demand and the correlated uncertainties associated with spatially correlated renewable generation and load demand. Active and reactive power static droop coefficients, and frequency and voltage reference settings of the dispatchable DG units are considered as the control variables for optimal dispatching of active power. A modified hybrid particle swarm and grey wolf optimizer embedded in fuzzy framework is used to solve the constrained multi-objective optimization problem. The efficacy of the proposed framework is validated on a 33-node droop controlled islanded microgrid test system.

A simulation optimization design of the filter stack spectrometer for laser-plasma interaction experiment

Filter stack spectrometers are widely employed in laser facilities for the spectrum measurement of bremsstrahlung photons. However, this method suffers from large uncertainty of unfolding due to its intrinsic limit resolution. For this, an optimization study on filter stack spectrometer is conducted. This procedure is implemented by a hybrid particle swarm optimization and genetic algorithm (PSO-GA). Monte-Carlo particle transport code Fluka is used for the simulation of the response matrix. Gravel algorithm, based on the least-square method, is used for the unfolding. For mono-energetic photons, this optimized filter stack spectrometer design provides a better energy resolution. For continuous distribution, this optimized filter stack spectrometer design yields a narrower unfolding solution space in the presence of measurement error.

Optimal fog node selection based on hybrid particle swarm optimization and firefly algorithm in dynamic fog computing services

Fog computing is a qualifiedly roseate technology introduced to support latency-sensitive and mission-critical applications by bringing resources closer to the end users. To exploit the full potentials of this auspicious technology, it is important to select optimal fog nodes for secured service provisioning. Therefore, in this work, an optimal fog node selection is formulated as a multi-objective optimization problem. To solve this problem, an efficient selection strategy based on improved particle swarm optimization (PSO) and modified firefly algorithm (FA) is developed. This study considers two important objectives that have not been optimized in the previous works: trust and rate of sojourn, in addition to remaining node capacity and energy consumption. The linear weighted-sum approach is used to aggregate the individual objective functions, after which the Best–worst method (BWM) is used to determine the weight vector of the aggregated function. The impact of the variation in the weight values of the most sensitive objective indicate that in the worst-case scenario, the proposed model is robust and insensitive with an improvement of about 42.16%–50.02% over the related methods. Moreover, we conduct an experimental simulation of the proposed selection strategy as well as comparative analysis with the state-of-the-art algorithms using six performance metrics. The results show that the proposed hybrid PSO-FA achieves a higher accuracy and faster convergence. The proposed solution also records an improvement of about 18.64%–69.45% in resource utilization, 10.94%–40.45% in energy consumption, 12.50%–75% in trust violation, 4.94%–31.12% in computation delay, and 24.71%–37.7% in makespan over the related advanced algorithms.

A hybridization of PSO and VNS to solve the machinery allocation and scheduling problem under a machinery sharing arrangement

This paper presents the hybrid Particle Swarm Optimization and the Variable Neighborhood Search (PSO-VNS) to solve the machinery and equipment allocation and scheduling to help the growers expand the production level to meet the increased demands and growing interest, and to increase profitability. This problem can be formulated as the Machinery and Equipment Allocation and Scheduling Problem (MEASP) which is special type of a hybrid flow shop scheduling problem (HFS) with sequence dependent setup times, machine eligibility, machine grouping, blocking, tooling constraint, and time windows (HFS |SDST, rcrc, Grouping, blocking, Tool, Tw | Cmax). The objective of this research is to minimize the total completion time for sugarcane cultivation. A Mixed Integer Linear Programming (MILP) model was developed to handle small-scale problems. The hybrid Particle Swarm Optimization and the Variable Neighborhood Search (PSO-VNS) was used for large-scale problems. Two new velocity update formulae and a position update formula were incorporated into the Particle Swarm Optimization (PSO), and four types of neighborhood strategies were developed for the VNS. The experimental results show that all the PSO-VNS methods outperform the traditional PSO due to the effectiveness of the newly proposed velocity and position update formulae in finding the optimal solutions, the PSO-VNS-6 methods, on the average, can reduce the computational time by 58.43% from the original PSO and improve the solution quality by 11.71%.

Cement Transport Vehicle Routing with a Hybrid Sine Cosine Optimization Algorithm

This study will solve the classical vehicle routing problem, the goal is to generate k trips with the shortest distance for h customers with predetermined locations and needs.. The proposed solution to the classical vehicle routing problem is a hybrid sine cosine algorithm. The sine cosine algorithm is hybridized with the grey wolf optimizer, which is used in combination with the methods of tournament selection, opposition learning, and the mutation and crossover method to build the optimal routing plan for the means of transporting cement. To demonstrate the advantages of the developed hybrid sine cosine algorithm, this algorithm is evaluated and compared with modern algorithms such as sine cosine algorithm, dragonfly algorithm, grey wolf optimizer, ant lion optimizer, particle swarm optimization, modified hybrid particle swarm optimization, genetic algorithm, and the double-population genetic algorithm in case studies. The hybrid sine cos algorithm gives optimal results in these cases because it balances mining and exploration. Thus, the results of this study indicate that managers can use the developed hybrid sine cosine algorithm to create optimal vehicle routing plans to reduce transportation distances.

An online identification method for establishing a microgrid equivalent model based on the hybrid particle swarm optimization butterfly algorithm

AbstractThe frequency response model of a microgrid system is an indispensable tool for designing secondary frequency controllers and analyzing system frequency stability. Owing to the complexity of a microgrid system structure, time‐varying operation state, and difficulty in obtaining the inverter parameters, it is difficult to model the microgrid system. To address the abovementioned problems, this study constructs the equivalent model of a microgrid system based on mechanism analysis and then identifies the equivalent model parameters using an online identification method based on a hybrid particle swarm optimization butterfly algorithm according to the sampled operation data to obtain the specific equivalent model of a microgrid for the controller. This modeling method has strong adaptability and can yield accurate identification modeling when the operational structure of the microgrid changes.

Swarm Optimization of Fuzzy Systems for Mobile Robots with Remote Control

This paper is dedicated to the development and research of the advanced approach for optimization of fuzzy control systems (FCS) for mobile robots (MR) with remote control based on bioinspired swarm techniques. The proposed approach makes it possible to create effective intelligent control systems for MRs based on the principles of hierarchical multi-level control, remote IoT-based control, fuzzy logic control, and intelligent optimization of fuzzy control devices. The applied hybrid particle swarm optimization (PSO) techniques with elite strategy allow effectively optimizing various parameters of FCSs, finding the optimal solution to the problem, and, at the same time, have a higher convergence rate compared with the basic PSO algorithms. To evaluate the effectiveness of the obtained advanced approach based on hybrid swarm techniques, the optimization process of the FCS for the speed control of the multi-purpose caterpillar MR, which can move on inclined and vertical ferromagnetic surfaces, is carried out. The presented research results fully confirm the high efficiency of the proposed approach, as well as the expediency of its application for the optimization of fuzzy control systems for various remotely controlled mobile robots.

A hybrid moth–flame algorithm with particle swarm optimization with application in power transmission and distribution

The transmission lines are used for power distribution across large distances. Different parameters affect the power transmission efficiency, and the quality of service. Furthermore, the transmission system parameter estimation is crucial for power flow analysis, electric power system expansion planning, stability, dispatch, and economic analysis. This task is created by utilizing system identification techniques, and with the analytical method being the most commonly utilized methodology for acquiring transmission line data. However, to address an issue that simplifies the estimation these techniques have significant downsides — such as non-recursive parameter estimation and the accessibility of an appropriately transposed line. This paper presents a hybrid moth-flame optimization (MFO) and particle swarm optimization (PSO) for estimating transmission line parameters based on various scenarios and mathematical validation on different benchmark functions. The concepts of MFO and PSO are rationally integrated into this algorithm to overcome their limitations and improve their global search ability. Regarding solution quality and convergence speed the results show that the proposed hybrid algorithm performs better than the conventional PSO and original MFO.

Hybrid Particle Swarm Optimization Algorithm Based on the Theory of Reinforcement Learning in Psychology

To more effectively solve the complex optimization problems that exist in nonlinear, high-dimensional, large-sample and complex systems, many intelligent optimization methods have been proposed. Among these algorithms, the particle swarm optimization (PSO) algorithm has attracted scholars’ attention. However, the traditional PSO can easily become an individual optimal solution, leading to the transition of the optimization process from global exploration to local development. To solve this problem, in this paper, we propose a Hybrid Reinforcement Learning Particle Swarm Algorithm (HRLPSO) based on the theory of reinforcement learning in psychology. First, we used the reinforcement learning strategy to optimize the initial population in the population initialization stage; then, chaotic adaptive weights and adaptive learning factors were used to balance the global exploration and local development process, and the individual optimal solution and the global optimal solution were obtained using dimension learning. Finally, the improved reinforcement learning strategy and mutation strategy were applied to the traditional PSO to improve the quality of the individual optimal solution and the global optimal solution. The HRLPSO algorithm was tested by optimizing the solution of 12 benchmarks as well as the CEC2013 test suite, and the results show it can balance the individual learning ability and social learning ability, verifying its effectiveness.

A fast and accurate global maximum power point tracking controller for photovoltaic systems under complex partial shadings

<span lang="EN-US">The operating conditions of partially shaded photovoltaic (PV) generators created a need to develop highly efficient global maximum power point tracking (GMPPT) methods to increase the PV system performance. This paper proposes a simple, efficient, and fast GMPPT based on fuzzy logic control to reach the point of global maximum power. The approach measures the PV generator current in the areas where it is almost constant to estimate the local maximums powers and extracts the highest among them. The performance of this method is evaluated firstly by simulation versus four well-known recent methods, namely the hybrid particle swarm optimization, modified cuckoo search, scrutinization fast algorithm, and shade-tolerant maximum power point tracking (MPPT) based on current-mode control. Then, experimental verification is conducted to verify the simulation findings. The results confirm that the proposed method exhibits high performance for complex partial irradiances and can be implemented in <br /> low-cost calculators.</span>

Research on simplification of branches method of accident sequences based on expert knowledge and heuristic optimization algorithm

The simplification of branches method of accident sequences based on expert knowledge and heuristic optimization algorithm is proposed in this paper, to increase the effectiveness of nuclear power plant safety margin analysis. First, the classification criteria for accident sequence and expert knowledge are used to screen out the deterministic failure sequences and deterministic safety sequences that are obviously. Secondly, a heuristic optimization method is used to study the variation in the accident sequences for those accident sequences that are challenging to evaluate with expert knowledge. Moreover, a hybrid particle swarm optimization (HPSO) is proposed to increase the accuracy of key safety parameters. This enables efficient accident sequence type identification, by comparing and evaluating key safety parameters and safety limits. Finally, the accident sequences are simplified using the cut-off frequency. To verify the effectiveness of the method, the small break loss of coolant (SBLOCA) accident in the nuclear power plant is analyzed in this paper. The numerical results show that the proposed HPSO improved the accuracy of solving the key safety parameters of accident sequences compared with the traditional method. By simplifying the accident sequences, the number of accident sequences requiring further analysis was reduced from 64 to 5.