Proposed Particle Swarm Optimization Research Articles

Employment of PSO algorithm to improve the neural network technique for radial distribution system state estimation

Abstract This study presented a new hybrid algorithm to improve the state estimation (SE) of radial distribution power systems (PSs). The proposed particle swarm optimization–neural network (PSO–NN) algorithm constructed an independent and fast monitoring system with high accuracy that can detect abnormal conditions or failures in a PS. In this study, PSO was adopted to determine the appropriate weights of the NN model. The speed and accuracy of PSO with the NN model were evaluated in the SE of power system networks (PSNs). The information obtained through SE was used to enhance the operations and customer service delivery in terms of energy consumption and power quality in PSNs. Capacitor banks were installed to reduce the losses and improve the voltage profiles. The PSO–NN algorithm was assessed on IEEE (9, 33, and 69) bus standards. Simulation results proved that the new technique can be tested on any distribution network because of its accurate and efficient SE. Results indicated that the PSO–NN algorithm had better performance than the phasor measurement units.

An Integrated Approach for Sustainable Supply Chain Management with Replenishment, Transportation, and Production Decisions

Sustainable supply chain management is important for most firms in today’s competitive environment. This study considers a supply chain environment under which the firm needs to make decisions regarding from which supplier and what quantity of parts should be purchased, which vehicle with a certain emissions amount and transportation capacity should be assigned, and what kind of production mode should be used. The integrated replenishment, transportation, and production problem is concerned with coordinating replenishment, transportation, and production operations to meet customer demand with the objective of minimizing the cost. The problem considered in this study involves heterogeneous vehicles with different emission costs, various materials with dissimilar emission costs, and distinct production modes, each with their own emission costs. In addition, multiple suppliers with different quantity discount schemes are considered, different kinds of vehicles with different loading capacities and traveling distance limits are present, and different production modes with different production capacities and production costs are included. A mixed integer programming model is proposed first to minimize the total cost, which includes the ordering cost, purchase cost, transportation cost, emission cost, production cost, inventory-holding cost, and backlogging cost, while satisfying various constraints in replenishment, transportation, and production. A particle swarm optimization model is constructed next to deal with large-scale problems that are too complicated to solve by the mixed integer programming. The main advantage of the proposed models lies in their ability to simultaneously coordinate the replenishment, transportation, and production operations in a planning horizon. The proposed particle swarm optimization model could further identify a near-optimal solution to the complex problem in a very short computational time. To the best of the authors’ knowledge, this is the first paper that considers the sustainable supply chain management problem with multiple suppliers, multiple vehicles, and multiple production modes simultaneously. Case studies are presented to examine the practicality of the mixed integer programming and the particle swarm optimization models. The proposed models can be adopted by the management to make relevant supply chain management decisions.

Probabilistic Approach Processing Scheme Based on BLAST for Improving Search Speed of Bioinformatics

As researchers on bioinformatics using heuristic algorithms have been increasingly studied, information management used in various bioinformatics fields (new drug development, medical diagnosis, agricultural product improvement, etc.) has been studied mainly on BLAST algorithm. However, many of the algorithms that are being used in the large genome database use a complete sorting procedure, which takes a lot of time to search the database for proteins or nucleic acid sequences, which causes many problems in processing large amounts of bio information. We propose a BLAST-based probabilistic access processing method that can manage, analyze and process a large amount of bio data distributed based on information communication infrastructure and IT technology. The proposed method aims to improve the accessibility of data by linking weighted bioinformatics information with probability factors to easily access large capacity bio data. In addition, the proposed scheme classifies the priority information allocated to the bioinformatics information by hierarchical grouping according to the degree of similarity, thereby ensuring high accuracy of the search results of the bioinformatics information, and at the same time, the goal is to obtain low processing time by classifying information (type, attribute, priority, etc.) into weights by property. Previous researchers have suggested clustering algorithms for fragmentation of genetic information to solve the problem of haplotype assembly in genetics, or proposed particle swarm optimization methods similar to existing genetic algorithms using heuristic clustering method based on MEC model. In the performance evaluation, the proposed method improved the accuracy by average 13.5% and the efficiency of the data retrieval by average 19.7% more than previous scheme. The overhead of Bioinformatics information processing was 8.8% lower and the processing time was average 13.5% lower.

Modeling and prediction of water quality parameters using a hybrid particle swarm optimization–neural fuzzy approach

This work focuses on the correlations among water quality parameters such as total hardness, total dissolved solids, electrical conductivity and total alkalinity with water parameters pH, temperature and the sum of mill equivalents of cations and anions in water reservoir in Kermanshah province (located in the middle of the western part of Iran). The data of water quality of monitoring sites were collected over year 2015. To predict and simulate water quality parameters, two data-driven models, i.e., adaptive neural fuzzy inference system and a hybrid adaptive neural fuzzy inference system structure trained by particle swarm optimization technique, were used. The main advantages of these methods are their high accuracy and very fast computational speed to predict unknown data. The results indicated that implementation of two models is highly satisfactory for predicting inorganic indicators of water quality. However, the flexibility of particle swarm optimization–adaptive neural fuzzy inference system method in modeling is better than adaptive neural fuzzy inference system approach. To prove this, the correlation coefficient, mean absolute error, root mean square error and t statistics were calculated as the error criterion. The overall (training and testing) mean relative error percentage, mean absolute error, root mean square error, correlation coefficient and t statistics obtained by the proposed particle swarm optimization–adaptive neural fuzzy inference system model are less than 3.50, 11.60, 18.90, 0.95 and 0.38%, respectively. The results provide a useful approach that uses water parameters to estimate water quality in water reservoir for water treatment and pollution management.

A simulation analysis method based on PSO-RBF model and its application

In order to improve the effectiveness of thermal and heat insulation property of building wall of light wood structure, a research method of heat insulation property of building wall of light wood structure based on PSO-RBF model is proposed. Firstly, the construction and parameter of the adopted wall in the research of the Thesis is given and the corresponding heat insulation physical model and problem are described; secondly, the thermal and heat insulation property of building wall of light wood structure is researched and analyzed by using the proposed particle swarm optimization BP network algorithm; finally, the effectiveness of the proposed algorithm is verified through simulation experiment.

An image contrast enhancement algorithm for grayscale images using particle swarm optimization

This paper addresses a contrast enhancement technique that combines classical contrast enhancement with an evolutionary approach. The central goal of this work is to increase the information content and enhance the details of an image using an adaptive gamma correction technique aided by particle swarm optimization. Gamma correction is a well established technique that preserves the mean brightness of an image that produces natural looking images by the choice of an optimal gamma value. Here, Swarm intelligence based particle swarm optimization is employed to estimate an optimal gamma value. In the proposed method, the edge and information content (entropy) are the parameters used to formulate the fitness function. The proposed method is compared with state-of-the-art of techniques in terms of Weighted Average Peak Signal to Noise Ratio (WPSNR), Contrast, Homogeneity, Contrast Noise Ratio (CNR), and Measure of Enhancement (EME). Simulation results demonstrate that the proposed particle swarm optimization based contrast enhancement method improves the overall image contrast and enriches the information present in the image. In comparison to other contrast enhancement techniques, the proposed method brings out the hidden details of an image and is more suitable for applications in satellite imaging and night vision.

Robust Optimization With Worst Case Sensitivity Analysis Applied to Array Synthesis and Antenna Designs

Tolerance analysis studies the impact of tolerances and uncertainties on design performance, which represents a key problem in practical implementation of electromagnetic designs. Building on a worst case error tolerance model, this paper proposes particle swarm optimization (PSO) as an approach of the worst case sensitivity analysis (WCSA). Several methods of WCSA have been compared, such as random search, vertex prediction, genetic algorithm, and PSO. PSO with the absorbing boundary condition is proven to be accurate and efficient by several benchmarks. This paper incorporates WCSA with optimization to offer a method which not only finds the optimized design, but also ensures 100% feasibility of the optimized design under the certain tolerance criteria. Linear and planar array synthesis and resonant antenna geometric design demonstrate that within the maximum tolerance given by the algorithm, the optimum solutions are 100% feasible.

Optimizing energy and throughput for MPSoCs: an integer particle swarm optimization approach

Most of recent research in multicore processor architectures has been shifted towards reconfigurable architectures due to increasing complexity of computing systems. These systems provide better application-specific energy and throughput balance with their reconfigurable behavior. They perform automatic run time resource allocation for an application as per its needs. But in terms of performance, current methodologies produce some unpredictable results because of the actual variety of the workloads. Therefore, we need optimization of the system resources usage by employing some optimization algorithms. Early research in the field of reconfigurable architecture using optimization algorithms has produced efficient results for energy consumption with the reconfiguration of cache sizes and associativity, number of cores and operating frequency. In this research, we propose particle swarm optimization (PSO) based algorithm, Integer PSO (IPSO) for design space exploration of reconfigurable computer architectures to have better energy and throughput balance. The results obtained by IPSO are evaluated by using various SPLASH-2 benchmark applications. Evaluation shows notable reduction in energy consumption without major effect on throughput. Simulation results also support the use of IPSO in design space exploration of multicore reconfigurable processor architectures.

Optimal operation of interconnected energy hubs by using decomposed hybrid particle swarm and interior-point approach

The Energy Hub has become an important concept for formally optimizing multi-carrier energy infrastructure to increase system flexibility and efficiency. The existence of energy storage within energy hubs enables the dynamic coordination of energy supply and demand against varying energy tariffs and local renewable generation to save energy cost. The battery lifetime cost may be included in the optimization objective function to better utilize battery for long term use. However, the operational optimization of an interconnected energy hub system with battery lifetime considered presents a highly constrained, multi-period, non-convex problem. This paper proposes Particle Swarm Optimization (PSO) hybridised with a numerical method, referred to collectively as the decomposition technique. It decouples the complicated optimization problem into sub-problems, namely the scheduling of storage and other elements in the energy hub system, and separately solves these by PSO and the numerical method ‘interior-point’. This approach thus overcomes the disadvantages of numerical methods and artificial intelligence algorithms that suffer from convergence only to a local minimum or prohibitive computation times, respectively. The new approach is applied to an example two-hub system and a three-hub system over a time horizon of 24h. It is also applied to a large eleven-hub system to test the performance of the approach and discuss the potential applications. The results demonstrate that the method is capable of achieving very near the global minimum, verified by an analytical approach, and is fast enough to allow an online, receding time horizon implementation.

A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization

In this paper, a novel hierarchical global path planning approach for mobile robots in a cluttered environment is proposed. This approach has a three-level structure to obtain a feasible, safe and optimal path. In the first level, the triangular decomposition method is used to quickly establish a geometric free configuration space of the robot. In the second level, Dijkstra's algorithm is applied to find a collision-free path used as input reference for the next level. Lastly, a proposed particle swarm optimization called constrained multi-objective particle swarm optimization with an accelerated update methodology based on Pareto dominance principle is employed to generate the global optimal path with the focus on minimizing the path length and maximizing the path smoothness. The contribution of this work consists in: (i) The development of a novel optimal hierarchical global path planning approach for mobile robots moving in a cluttered environment; (ii) The development of proposed particle swarm optimization with an accelerated update methodology based on Pareto dominance principle to solve robot path planning problems; (iii) Providing optimal global robot paths in terms of the path length and the path smoothness taking into account the physical robot system limitations with computational efficiency. Simulation results in various types of environments are conducted in order to illustrate the superiority of the hierarchical approach.

Multirobot task allocation based on an improved particle swarm optimization approach

Due to its complexity and non-deterministic polynomial-time hard characteristic, multirobot task allocation problem remains a challenging issue in the field of cooperative robotics. Thanks to its easy implementation and promising convergence speed, the particle swarm optimization method has recently aroused increasing research interest in the area of multirobot task allocation problem. However, the efficiency of the standard particle swarm optimization is hindered by several deficiencies such as the inefficient capabilities in balancing exploration and exploitation, as well as the high likelihood of plunging into stagnation. Aiming at enhancing the performance of particle swarm optimization via remedying these two drawbacks, this paper proposes an improved particle swarm optimization method, which integrates standard particle swarm optimization 2011 with evolutionary game theory. To prevent particles being locked into stagnation, particles in the proposed particle swarm optimization first adopt the updating rules of standard particle swarm optimization 2011 to undertake their movements. Subsequently, attempting to well trade off the exploration and exploitation capabilities of particles, a novel self-adaptive strategy, which is determined by the evolutionary stable strategies of evolutionary game theory and the iteration number of particle swarm optimization, is presented to adaptively adjust the main control parameters of particles in the proposed particle swarm optimization. Since the convergence of particle swarm optimization remains paramount and dramatically affects the performance of particle swarm optimization, this paper also analytically investigates the convergence of the proposed particle swarm optimization and provides a convergence-guaranteed parameter selection principle for the proposed method. Finally, leveraging the development of the proposed particle swarm optimization, this paper completes the design of a new particle swarm optimization–based multirobot task allocation method. The performance of the new particle swarm optimization–based multirobot task allocation method is tested through three different allocation cases against four well-known evolutionary methods. Experimental results confirm that the proposed method generally outperforms its contenders in terms of the solution quality. Moreover, the proposed method performs slightly better than the majority of its peers as far as the computation time is concerned.

Optimal Location of SSSC Based on PSO to Improve Voltage Profile and Reduce Iraqi Grid System Losses

This search aims to study the effects of (SSSC)”static synchronous series compensator– one of the FACTS devices” – on reducing the power losses and improving voltage profile of Iraqi national grid system. Proposed particle swarm optimization (“PSO”) to determine the optimum location of “SSSC” devices based objective function that depends on the power and voltage as fitness. The proposed algorithm is checked on the IEEE- 9bus. Then is applied on the Iraqi national grid. The results show the ability of the proposed method to determine the optimum location of static synchronous series compensator (SSSc) that reduced losses of active power and improve the bus “voltage profile”. The proposed algorithms are implemented by using MATLAB package version 7.10

Particle Swarm Optimization-Based Band Selection for Hyperspectral Target Detection

This letter proposes particle swarm optimization (PSO)-based band selection (BS) approach for hyperspectral target detection. Due to lack of training samples in a detection problem, it is more difficult than classification-purposed BS. The objective function, called maximum-submaximum-ratio (MSR) gauging target-background separation, is proposed for target detection during PSO searching. Typical target detectors such as target-constrained interference-minimized filter and adaptive coherence estimator are studied. Experimental results demonstrate that the proposed MSR-based objective function in conjunction with PSO-based searching can select a small band set while yielding similar or even better detection performance than using all the original bands, sequential forward search-based BS, or BS relying on detection map similarity assessment.

Predicting Minimized Settling Time Corresponding Piezo Localization Displacement and Velocity

The significance intension of this research paper is to predict optimal input parameters such as piezolocation, displacement and velocity to retrieve minimized settling time. Our previous research work comprised of framing mathematical modelling, that model is utilized as an objective function to get the solution of minimized settling time. This previous developed mathematical model is subjected with three different optimization techniques such as cuckoo search (CS), artificial bee colony (ABC) and particle swarm optimization algorithms (PSO). Among, all three applied optimization techniques the proposed particle swarm optimization technique reveals better result (i.e.) achieving minimized settling time with optimal input parameters.

Dynamic Stochastic Resonance Based Diffusion-Weighted Magnetic Resonance Image Enhancement Using Multi-Objective Particle Swarm Optimization

Diffusion weighted (DW) magnetic resonance (MR) imaging maps the diffusion process of water in the tissues. DW-MR image is useful to probe the tissue microstructure, but suffers from inherent low signal to noise ratio and poor contrast. Dynamic stochastic resonance (DSR) utilizes the noise to enhance the low contrast image where the quality of the processed image depends on the bistability parameters of DSR and the number of iterations. This paper presents an approach that optimally finds the bistability parameters and number of iterations for the maximization of competitive image quality indices: contrast enhancement factor and mean opinion score using multi-objective particle swarm optimization. The proposed Particle Swarm Optimization optimized DSR algorithm has been tested on 40 DW-MR brain images of different subjects. The quantified results show average contrast enhancement factor, 1.603 and average perceptual quality measure, 9.508. These values are significantly higher than image quality indices of original image, the images that are produced by conventional enhancement methods and filtering followed by enhancement methods.

Optimal Antenna Deployment for Multiuser MIMO Systems Based on Random Matrix Theory

This paper investigates the issues on optimal antenna deployment at a base station to maximize downlink ergodic sum-rate capacity. We analyze the ergodic sum-rate capacity of a multiuser multiple-input–multiple-output (MIMO) system using random matrix theory, and the analysis reveals that such a capacity depends largely on the deployed antenna structure. Moreover, we have found an optimal antenna configuration based on the proposed particle swarm optimization (PSO) algorithm to maximize the ergodic sum-rate capacity. The theoretical results on downlink ergodic sum-rate capacity match simulation results very well, validating the accuracy of the theoretical analysis. The simulation results show that the proposed optimal antenna configuration offers the largest downlink sum-rate capacity with the best bit error rate (BER) performance compared with uniform linear arrays (ULAs) and uniform circular arrays (UCAs). It is concluded that the optimal antenna coordinates can be predetermined before deploying the antennas in a given base station.

Hexagonal Descriptor Particle Swarm Optimization with Knowledge-Crowding for Face Recognition

Automatic identification of various facial expressions with high recognition value is important for human computer interaction as the facial behavior of a human can be treated as an important factor for information representation as well as communication. A number of basic factors such as cluttered background, occlusion, and camera movement and illumination variations degrade the image quality resulting in poor performance for identifying different facial expressions. Moreover the identification of the automatic feature detection in facial behavior requires high degree of correlation between the training and test images. Face recognition is done by minimizing the objective function which leads to selection of optimal set of fiducial points. The method preserves the local information from different facial views for mapping neighboring input to its corresponding output, resulting in low dimensional representation for encoding the relationships of the data. The proposed method Hexagonal Descriptor Particle Swarm Optimization with Knowledge-Crowding (HDPSO-KC) overcomes from local optima and improves global search process and collaborative work. The method also covers the problem of eliminating the particles in denser regions in Pareto front distribution. The proposed methodology is validated with benchmark datasets for analyzing the performance over other methods.

Intelligent probabilistic broadcasting in mobile ad hoc network: a PSO approach

MANETs are collection of independent nodes, which communicate with each other to perform a task. Broadcasting methods are widely used in this infrastructureless networks. Although broadcasting is easy to implement and a method to perform routing and safety functions, in a wide and high mobility MANET it is a difficult and expensive task to achieve. It is required that the underlying algorithm used for communication must consider parameters such as neighborhood density, the size and shape of the network, and the efficient use of channel. Probabilistic strategies are frequently used, as they do not introduce additional latency. Several researchers have proposed using various parameter instances which are managed dynamically, for instance, the change in the number of neighbor nodes and corresponding change in retransmission probability. But the authors did not optimize the parameters for specific environments. The proposed work in this research article suggests and determines the most efficient strategy for each node to decide the retransmission probability according to its neighborhood density, available bandwidth and remaining energy of a node. It describes a tool combining a network simulator (ns-2) and a particle swarm optimization algorithm. Then, it is applied to the MANET broadcasting problem. The simulation results show that the proposed particle swarm optimization probabilistic broadcasting (PSOPB) scheme is reliable and efficient in comparison with the other artificial intelligence broadcasting schemes such as elitist simulated binary evolutionary algorithm (ESBEA), multi-objective problems with Pareto front solution (MOP_PF) and efficient fuzzy logic-based probabilistic broadcasting (EFPB).

A Hybrid Particle Swarm Optimization with Affine Transformation Approach for Cloud Free Multi-Temporal Image Registration

An image registration is the major part of the image categorization and cluster formation in multi temporal image processing. The images are affected by the different factors such as cloud shadow, water level, building shadows etc. In this paper, an enhanced registration process and the cloud removal technique is proposed for image enhancement. The Daemons, Combined Registration and Segmentation (CRS) approach, Markov Random Field (MRF) approach and Mutual Information (MI) based approaches results in more computational complexity, minimum edge preservation measure (QAB/F) and Mutual Information in image registration. In order to maximize the quality of edge preservation measure and MI with minimum computational time, this paper proposes Particle Swarm Optimization (PSO) based affine transformation technique. The proposed techniques measure and compare the computation time against the number of pixels of an image with the existing methods of CRS and MRF for the number of images. The comparative analysis of QAB/F and MI with the traditional methods of Clock Point –Least Square (CP-LS) and the Multi-Focus Image Fusion (MFIF) and Discrete Wavelet Transform (DWT) is presented to confirm the effective performance. The simulation results of the proposed transformation for registration process confirms the effective image registration in the multi-temporal image processing.

Discrete particle swarm optimization method for the large-scale discrete time–cost trade-off problem

Despite many research studies have concentrated on designing heuristic and meta-heuristic methods for the discrete time–cost trade-off problem (DTCTP), very little success has been achieved in solving large-scale instances. This paper presents a discrete particle swarm optimization (DPSO) to achieve an effective method for the large-scale DTCTP. The proposed DPSO is based on the novel principles for representation, initialization and position-updating of the particles, and brings several benefits for solving the DTCTP, such as an adequate representation of the discrete search space, and enhanced optimization capabilities due to improved quality of the initial swarm. The computational experiment results reveal that the new method outperforms the state-of-the-art methods, both in terms of the solution quality and computation time, especially for medium and large-scale problems. High quality solutions with minor deviations from the global optima are achieved within seconds, for the first time for instances including up to 630 activities. The main contribution of the proposed particle swarm optimization method is that it provides high quality solutions for the time–cost optimization of large size projects within seconds, and enables optimal planning of real-life-size projects.