Classical Particle Swarm Optimization Research Articles

Simulation-optimization model for estimating hydraulic conductivity: a numerical case study of the Lu Dila hydropower station in China

A simulation-optimization model (ADINA-MMRDE) has been developed using the commercial finite-element software ADINA coupled with an improved differential evolution algorithm (MMRDE) to estimate the hydraulic conductivity of the aquifer of the Lu Dila hydropower project in China. The ADINA-MMRDE model employed the sum of the weighted square difference between the simulated value and the observed value as the objective function. To evaluate the accuracy of the MMRDE, the ADINA software was also coupled with a classical differential evolution (DE) algorithm and particle swarm optimization (PSO). Before estimating the initial seepage field of the Lu Dila hydropower station, the performance of the ADINA-MMRDE model was first tested using a boundary-simplified model based on the initial seepage field model. The effects of different objective functions, measurement errors and population sizes on the performance of ADINA-MMRDE were investigated. Then the ADINA-MMRDE model was applied to the Lu Dila hydropower project by considering water levels in 20 boreholes. The results showed that the head calculated using the estimated parameters was very close to its observed value. The water levels of 13 monitoring boreholes were used for further confirmation of the parameters obtained by the inversion. The results revealed that the calculated values of the monitoring borehole water levels were generally well fitted to the measured values. This study demonstrated the effectiveness of the ADINA-MMRDE model for practical and complex parameter inversion tasks.

Parameters identification of PV solar cells and modules using flexible particle swarm optimization algorithm

The use of solar energy as a source of clean energy is increasing throughout the world. Therefore, designing higher-quality photovoltaic cells has attracted researches. Several equivalent circuits have been proposed for the photovoltaic cell, but it is necessary to note that in order to achieve maximum power point (MPP), finding appropriate circuit model parameters is required. Many methods for finding the optimal parameters have been proposed. In this paper, flexible particle swarm optimization (FPSO) algorithm is proposed to estimate the parameters of PV cell model. In this algorithm, an elimination phase is added to classic PSO. At the beginning of each phase, a certain number of worst particles are deleted and some new particles are replaced in the new search space. Also, the search space of the parameters in each particle is changed based on the value of these parameters. These modifications have enhanced the proposed algorithm performance by adding the ability of global search and also searching in a reasonable space. To highlight the superiority of the FPSO algorithm, this method is used to estimate the parameters of the single diode model, double diode model, and the photovoltaic module. In order to illustrate the proficiency of the proposed approach, it is compared to other well-known optimization methods. Furthermore, to ensure the practical use of the FPSO algorithm, it is validated by three different solar modules such as monocrystalline (SM55) and multi-crystalline (KC200GT) and polycrystalline (SW255). The simulation results show that the proposed algorithm has high performance in terms of accuracy and robustness.

Optimal day-ahead scheduling for commercial building-level consumers under TOU and demand pricing plan

In this paper, we solve the day-ahead scheduling programming problem for commercial building-level consumers with combined time-of-use ($/kWh) and demand ($/kW) pricing plans. Aiming at minimizing the monthly charge, the problem formulation is proposed as a generic algorithm that produces day-ahead building operation schedules. It considers the influences of daily peak load during on-peak hours and daily energy consumption on the monthly charge. The aggregation model of building-level space conditionings is built for scheduling demo. To obtain the near global optimum, a multi sub-swarms particle swarm optimization (MSPSO) is proposed by introducing the ideas of mutation operation, work hierarchy and iterative regrouping into particle swarm optimization (PSO). It improves the population diversity for enhancing the global searching ability and the ability of escaping from local optimum. The PSO comparison shows that MSPSO has better convergence performance with higher stability compared with some classical PSOs. Furthermore, a commercial office-style building with space conditionings is simulated. Using TOU and demand pricing plan from Duke Energy, numerical results demonstrate that the proposed day-ahead scheduling algorithm and the improved MSPSO can reduce the monthly charge by 30% and 17%, respectively.

Adaptive cooperation of multi-swarm particle swarm optimizer-based hidden Markov model

The classical PSO algorithm can be affected with premature convergence when it comes to more complex optimization problems; the resolution easily can be trapped into local optima. The primary concern is to accelerate the convergence speed and to prevent the local optima solutions. To defeat these weaknesses and to enhance the overall performances, a new technique is offered building a dynamic multi-swarm design with cooperative rules based on a machine-learning design, namely the hidden Markov classification model. In this approach, a new design with multiple processes implemented inside the PSO that are the control of parameters adaptively with the improvement in the topological structure by setting a multi-swarm layer. Another process of information exchange between swarms is also considered. According to an HMM classification, the entire swarm will be then divided into dynamic cooperating sub-swarms. The size of each sub-swarm is going to be also adjusted at each iteration to suit the search stage. All sub-swarms share information between each other in order to ensure the best exploration of the search space and most effective exploitation. Adaptiveness of both acceleration coefficient and inertia weight strategies is customized with the account of the multi-swarm dynamic evolution and the history of achievements. The approach is simulated and compared by experimental tests to the best-known state of the art.

Optimized metamaterial-loaded fractal antenna using modified hybrid BF-PSO algorithm

The paper proposes optimization of square split-ring resonator (SRR) metamaterial unit cell using modified hybrid bacterial foraging–particle swarm optimization (BF-PSO). Optimized metamaterial unit cells are loaded into novel designed square fractal antenna for its bandwidth enhancement. The presented research is alienated in three phases: Novel design of microstrip line-fed square fractal antenna with defected ground structure is proposed in the initial phase that provides dual band performance. In second phase, with the aim of bandwidth enhancement, quasi-static model of SRR unit cell is used to optimize its structural parameters so that optimized structure resonates at desired frequency region. Modifications are included in hybrid BF-PSO algorithm as per size constraints of SRR unit cell to be optimized and for improving the convergence behavior of algorithm. The performance of modified hybrid BF-PSO algorithm is assessed against four other evolutionary techniques named as classical BFO, chaos PSO, IWO and ABC. In later phase, optimized SRR unit cells are loaded into initially designed square fractal antenna that results in broadband performance suitable for upper S-band and lower C-band wireless applications. The designed square fractal antenna without and with SRR unit cells is fabricated and tested to verify the experimental results.

Twin-Timescale Artificial Intelligence Aided Mobility-Aware Edge Caching and Computing in Vehicular Networks

In this paper, we propose a joint communication, caching and computing strategy for achieving cost efficiency in vehicular networks. In particular, the resource allocation policy is specifically designed by considering the vehicle's mobility and the hard service deadline constraint. An artificial intelligence-based multi-timescale framework is proposed for tackling these challenges. To mitigate the complexity associated with the large action and search space in the sophisticated multi-timescale framework considered, we propose to maximize a carefully constructed mobility-aware reward function using the classic particle swarm optimization scheme at the associated large timescale level, while we employ deep reinforcement learning at the small timescale level of our sophisticated twin-timescale solution. Numerical results are presented to illustrate the theoretical findings and to quantify the performance gains attained.

Adaptive control of criticality infrastructure in automatic closed-loop supply chain considering uncertainty

There are many processes in the business world which are dependent on Critical Infrastructure Systems (CISs) to provide important services. Hence, undesirable behaviors of these processes can provide cascading disturbances and might cause high impacts on criticality infrastructure in Automatic Closed-Loop Supply Chain (A-CLSC). Thus, this level of vulnerability needs to monitor and control the disturbances related to uncertain parameters. In order to develop the controller component inward uncertain Business Process Management System (BPMS), the authors provided adaptive controller considering uncertainty, which could be tuned with an intelligent algorithm called Improved Particle Swarm Optimization (IPSO). In addition to multi-objective optimization, the novel controller has been used to control the two most important activities includes inventory and transportation evolving the criticality infrastructure in A-CLSC at the desired behavior as a simulation and practical case study. Furthermore, the proposed IPSO algorithm has been compared against legendary methods such as adaptive PSO (APSO) and classic PSO. Also, the paper has been provided some metrics to statistical evaluation of proposed method performance. The results indicated the better set response of the new controller to control the A-CLSC as well as effective control of CISs.

Economic emission dispatch using an advanced particle swarm optimization technique

Purpose This study aims to present a novel optimization technique to solve the combined economic emission dispatch (CEED) problem considering transmission losses, valve-point loading effects, ramp rate limits and prohibited operating zones. This is one of the most complex optimization problems concerning power systems. Design/methodology/approach The proposed algorithm has been called advanced particle swarm optimization (APSO) and was created by applying several innovative modifications to the classic PSO algorithm. APSO performance was tested on four test systems having 14, 40, 54 and 120 generators. Findings The suggested modifications have improved the accuracy, convergence rate, robustness and effectiveness of the algorithm, which has produced high-quality solutions for the CEED problem. Originality/value The results obtained by APSO were compared with those of several other techniques, and the effectiveness and superiority of the proposed algorithm was demonstrated. Also, because of its superlative characteristics, APSO can be applied to many other engineering optimization problems. Moreover, the suggested modifications can be easily used in other population-based optimization algorithms to improve their performance.

A novel discrete particle swarm optimization algorithm for solving bayesian network structures learning problem

ABSTRACTBayesian 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.

Hybridization of Constriction Coefficient Based Particle Swarm Optimization and Gravitational Search Algorithm for Function Optimization

The Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) are stochastic optimization techniques. The PSO is inspired by the simulated behavior of bird flocking whereas GSA is the physics-based heuristic technique that is inspired by the law of mass interaction. In this paper, a new hybridization algorithm CPSOGSA i.e. constriction coefficient based particle swarm optimization and gravitational search algorithm have been proposed. It combines the exploitation and exploration capabilities of PSO and GSA, respectively in order to obtain the best result. The experimental results on 23 standard unimodal and multimodal test functions confirm the better performance of CPSOGSA as compared with classical Particle swarm optimization and Gravitational search algorithm. The efficiency of the hybrid CPSOGSA has been demonstrated through faster intensification rate and avoidance from local minima.

HYBRID IMPROVED BACTERIAL SWARM OPTIMIZATION ALGORITHM FOR HAND-BASED MULTIMODAL BIOMETRIC AUTHENTICATION SYSTEM

This paper proposes a Hybrid Improved Bacterial Swarm (HIBS) optimization algorithm for the minimization of Equal Error Rate (EER) as a performance measure in a hand-based multimodal biometric authentication system. The hybridization of the algorithm was conducted by incorporating Bacterial Foraging Optimization (BFO) and Particle Swarm Optimization (PSO) algorithm to mitigate weaknesses in slow and premature convergence. In the proposed HIBS algorithm, the slow convergence of BFO algorithm was mitigated by using the random walk procedure of Firefly algorithm as an adaptive varying step size instead of using fixed step size. Concurrently, the local optima trap (i.e. premature convergence) of PSO algorithm was averted by using mutation operator. The HIBS algorithm was tested using benchmark functions and compared against classical BFO, PSO and other hybrid algorithms like Genetic Algorithm-Bacterial Foraging Optimization (GA-BFO), Genetic Algorithm-Particle Swarm Optimization (GA-PSO) and other BFO-PSO algorithms to prove its exploration and exploitation ability. It was observed from the experimental results that the EER values, after the influence of the proposed HIBS algorithm, dropped to 0.0070% and 0.0049% from 1.56% and 0.86% for the right and left hand images of the Bosphorus database, respectively. The results indicated the ability of the proposed HIBS in optimization problem where it optimized relevant weights in an authentication system.

An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method

Abstract Properties prediction of crude oil remains an essential issue for refineries. In this communication, an exhaustive and extendable support vector machine (SVM) intelligent prediction process has been proposed to solve this problem. A novel hybrid genetic algorithm-particle swarm optimization (GA-PSO) method was applied to optimize the SVM model. The optimization process and result demonstrated that the newly proposed GA-PSO-SVM method was more accurate and time-saving than the classical GA or PSO method. Compared with the classical Grid-search SVM, the combined GA-PSO-SVM model appeared to be more applicable for the properties prediction task. The TBP distillation curve fitting was exampled to evaluate the performance of the developed model. The regression result demonstrated the high accuracy and efficiency of the proposed process. The model can be applied in the Industrial Internet as a plug-in, and the adaptability and flexibility is demonstrated by the implement of crude oil molecular reconstruction employing the intelligent prediction process.

Differential mutation and novel social learning particle swarm optimization algorithm

Social Learning Particle Swarm Optimization (SLPSO) is an improved Particle Swarm Optimization (PSO) algorithm, which greatly improves the optimization performance of PSO. However, SLPSO still has some deficiency, such as poor balance between exploration and exploitation and low search efficiency, so that it cannot yet do well in solving many complex optimization problems. Thus, this paper proposes an improved SLPSO algorithm, that is, Differential mutation and novel Social learning PSO (DSPSO). Firstly, in order to balance exploration and exploitation better, a dynamic inertia weight is introduced to replace the random inertia weight of SLPSO, and a single-example learning approach and an example-mean learning one are proposed to replace the imitation component and the social influence component of SLPSO respectively. Secondly, the dimension-based velocity updating equation of SLPSO is divided into two particle-based updating equations with the two approaches, and the two are executed alternately to form a novel social learning PSO (NSLPSO), which enhance the exploitation of SLPSO. Finally, a dynamic differential mutation strategy is used in NSLPSO to update the three best particles to enhance the exploration to obtain DSPSO. Experimental results on the complex functions from CEC2013 reveal that DSPSO outperforms SLPSO and quite a few state-of-the-art and classic PSO variants.

Optimization of Battery Energy Storage to Improve Power System Oscillation Damping

A placement problem for multiple Battery Energy Storage System (BESS) units is formulated towards power system transient voltage stability enhancement in this paper. The problem is solved by the Cross-Entropy (CE) optimization method. A simulation-based approach is adopted to incorporate higher-order dynamics and nonlinearities of generators and loads. The objective is to maximize the voltage stability index, which is setup based on certain grid-codes. Formulations of the optimization problem are then discussed. Finally, the proposed approach is implemented in MATLAB/DIgSILENT and tested on the New England 39-Bus system. Results indicate that installing BESS units at the optimized location can alleviate transient voltage instability issue compared with the original system with no BESS. The CE placement algorithm is also compared with the classic PSO (Particle Swarm Optimization) method, and its superiority is demonstrated in terms of a faster convergence rate with matched solution qualities.

A collaboration-based particle swarm optimizer with history-guided estimation for optimization in dynamic environments

Optimization problems widely exist in many expert and intelligent systems, e.g., greenhouse intelligent control systems in agriculture, energy management systems for hybrid electric vehicle, and job shop scheduling systems in manufacture. For the optimization problems in these systems, the objective functions may change over time. This kind of problem is usually called as dynamic optimization problems (DOPs) or optimizing in dynamic environments. The optimization algorithm plays an important role in designing an expert and intelligent system. In this paper, we present a novel particle swarm optimizer for optimization in dynamic environments. We introduce two schemes to improve performance of particle swarm optimization in dynamic environments. Firstly, the classical particle swarm optimization is enhanced by a collaborative mechanism, in which a target particle learns from another randomly selected particle and the global best one in the swarm. Instead of moving to the new position directly, a worst replacement operator is used to update the swarm, whereby the worst particle in the swarm moves to the better newly generated position. During optimizing, the best solution in each generation is stored. When an environmental change is detected, the historical solutions are retrieved to collaborate with some newly generated solutions to adapt to the new environment. The performance of the proposed algorithm is compared with several reported algorithms over the benchmark problems. Experimental results indicate that the proposed algorithm offers superior performance compared with the competitors.

TRACKING MAXIMUM POWER POINT FOR PHOTOVOLTAIC SYSTEM USING A NOVEL DIFFERENTIAL PARTICLE SWARM OPTIMIZATION

In this paper, a new differential particle swarm optimization (DPSO) method is investigated for tracking the maximum power point (MPP) for the photovoltaic (PV) system in order to enhance the operating efficiency of the PV system. The DPSO-based MPPT method is not only able to tract the MPP using few iterations, but also avoids the steady-state oscillation phenomenon. Moreover, the proposed method is capable for extracting the optimization power under varyingtemperature condition as well as large fluctuations of irradiation. To demonstrate the effectiveness of the proposed, the obtained results are compared to those obtained using the conventional perturb and observation (P&O), incremental conductance (IncCond), and classical particle swarm optimization (PSO). Furthermore, a boost converter supplied by a solar array simulator is done to check the stability of the circuit. Meanwhile, the DPSO-MPPT algorithm is embedded in the PV system simulated by using Simulink software and MATLAB Toolbox. The simulating results show the superiority of the proposed approach in improving the efficiency of the photovoltaic system.

Local-global iterative linear embedding optimization-based positioning algorithm in sensor network

ABSTRACT In the distributed iterative positioning algorithm of wireless sensor networks, there will be positioning errors due to the existence of noise and selection of reference nodes; the errors will be further propagated when the nodes selected are taken as the descendant reference nodes. Currently, error propagation is not considered in the distributed iterative algorithm; to reduce the propagation errors, the local linear embedding optimization-based positioning algorithm is proposed in this paper. This algorithm follows the essential criteria of the beacon nodes and takes the mean of the positioned node location and the same node location of the previous generation as the current node location; the particle swarm optimization algorithm of population classification and dynamic learning factor is adopted to accelerate the positioning speed. According to the simulation result, compared with the classical PSO algorithm, the algorithm proposed in this paper has better real-time and effectively improves the positioning accuracy for it has not only saved the positioning time but also effectively reduced the influences of propagation errors on the positioning accuracy.

An implementation of differential evolution algorithm for a single product and single period multi-echelon supply chain network model

In this paper, five variants of Differential Evolution (DE) algorithms are proposed to solve the multi-echelon supply chain network optimization problem. Supply chain network composed of different stages which involves products, services and information flow between suppliers and customers, is a value-added chain that provides customers products with the quickest delivery and the most competitive price. Hence, there is a need to optimize the supply chain by finding the optimum configuration of the network in order to get a good compromise between several objectives. The supply chain problem utilized in this study is taken from literature which incorporates demand, capacity, raw-material availability, and sequencing constraints in order to maximize total profitability. The performance of DE variants has been investigated by solving three stage multi-echelon supply chain network optimization problems for twenty demand scenarios with each supply chain settings. The objective is to find the optimal alignment of procurement, production, and distribution while aiming towards maximizing profit. The results show that the proposed DE algorithm is able to achieve better performance on a set of supply chain problem with different scenarios those obtained by well-known classical GA and PSO.

TrAdaBoost Based on Improved Particle Swarm Optimization for Cross-Domain Scene Classification With Limited Samples

Scene classification is usually based on supervised learning, but collecting instances is expensive and time-consuming. TrAdaBoost has achieved great success in transferring source instances to target images, but it has problems, such as the excessive focus on instances harder to classify, the rapid convergence speed of the source instances, and the weight mismatch caused by the big gap between the number of source and target instances, leading to decreased classification accuracy. In this paper, in order to address these problems, classical particle swarm optimization (PSO) is modified to select the optimal feature subspace for classifying the “harder” and “easier” instances by reducing unimportant dimensions. A modified correction factor is proposed by considering the classification accuracy of the instances from both domains, to decrease the convergence speed. Iterative selective TrAdaBoost is also proposed to reduce the weight mismatch by removing the indiscriminate source instances. The experimental results obtained with three benchmark data sets confirm that the proposed method outperforms most of the previous methods of scene classification with limited target samples. It is also proved that modified PSO for optimal feature subspace selection, the modified correction factor, and iterative selective TrAdaBoost are all effective in improving the classification accuracy, giving improvements of 3.6%, 4.3%, and 2.7%, and these three contributions together increase the classification accuracy by about 8% in total.

Route planning algorithm for autonomous underwater vehicles based on the hybrid of particle swarm optimization algorithm and radial basis function

The mission route plays an essential role for the mission security and reliability of an unmanned system. This paper gives a route planning method for autonomous underwater vehicles (AUVs) based on the hybrid of particle swarm optimization (PSO) algorithm and radial basis function (RBF). In the improved PSO algorithm, metropolis criterion is used to prevent the improved PSO algorithm from falling into local optimum and RBF is used to smooth the path planned by PSO algorithm. Compared with classic PSO algorithm, the hybrid algorithm of PSO and RBF can avoid falling into the local optimum effectively and plan an anti-collision route. Moreover, based on the simulation results, it can be seen that the approach presented here is more efficient in convergence performance, and the planned route requires lower performance of AUVs.