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Obstacle avoidance for a swarm of unmanned aerial vehicles operating on particle swarm optimization: a swarm intelligence approach for search and rescue missions
(2022-02-01)
Kumar, Girish
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Anwar, Arham
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Dikshit, Abhinav
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Poddar, Abhirup
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Soni, Umang
An approach, based on a multi-plane system, is conceptualized in this work to solve the problem of collision avoidance for a swarm of unmanned aerial vehicles, being used for search and rescue to minimize affecting the searching algorithm. Relevant chronological advancements in the last two decades of the parent algorithm, particle swarm optimization, are summarized. As each optimization algorithm for search and rescue has its own niche area of application, various well-established algorithms such as particle swarm optimization and novel algorithms like layered search and rescue, spiral search and fish-inspired task allocation are compared with each other qualitatively. Simulations with 100 different cases were used to compare the original particle swarm optimization with the additional novel collision avoidance algorithm. The statistical z test was run based on which it was found that the proposed algorithm significantly reduces the number of collisions and does not put a toll on the iterations to convergence. Standardized residuals of all cases indicate minimal error difference in the optimum average fitness value calculated by the particle swarm optimization, with and without the conceptualized anti-collision algorithm.
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Optimum Operation and Cost Scenarios of a Hybrid Wind/PV/Battery in a Radial Network using Genetic Algorithm and Particle Swarm Optimization
(2022-01-01)
Abdelwareth, Mohamed Els S.
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Riawan, Dedet Candra
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Chompoo-Inwai, Chow
Hybrid generation systems took the attention of many researchers searching for the best energy source that can be optimum, reliable, and scalable instead of dependency on the traditional fossil fuels sources; researchers are developing Artificial Intelligence (AI) algorithms to optimize those systems. This paper will study the optimum generating power from a Wind turbine, PV, and Battery linked to the radial network in Tomia Island, considering the optimum cost using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).
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Hybrid learning of hand-crafted and deep-activated features using particle swarm optimization and optimized support vector machine for tuberculosis screening
(2020-09-01)
Win, Khin Yadanar
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Maneerat, Noppadol
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Hamamoto, Kazuhiko
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Sreng, Syna
Tuberculosis (TB) is a leading infectious killer, especially for people with Human Immunodeficiency Virus (HIV) and Acquired Immunodeficiency Syndrome (AIDS). Early diagnosis of TB is crucial for disease treatment and control. Radiology is a fundamental diagnostic tool used to screen or triage TB. Automated chest x-rays analysis can facilitate and expedite TB screening with fast and accurate reports of radiological findings and can rapidly screen large populations and alleviate a shortage of skilled experts in remote areas. We describe a hybrid feature-learning algorithm for automatic screening of TB in chest x-rays: it first segmented the lung regions using the DeepLabv3+ model. Then, six sets of hand-crafted features from statistical textures, local binary pattern, GIST, histogram of oriented gradients (HOG), pyramid histogram of oriented gradients and bags of visual words (BoVW), and nine sets of deep-activated features from AlexNet, GoogLeNet, InceptionV3, XceptionNet, ResNet-50, SqueezeNet, ShuffleNet, MobileNet, and DenseNet, were extracted. The dominant features of each feature set were selected using particle swarm optimization, and then separately input to an optimized support vector machine classifier to label 'normal' and 'TB' x-rays. GIST, HOG, BoVW from hand-crafted features, and MobileNet and DenseNet from deep-activated features performed better than the others. Finally, we combined these five best-performing feature sets to build a hybrid-learning algorithm. Using the Montgomery County (MC) and Shenzen datasets, we found that the hybrid features of GIST, HOG, BoVW, MobileNet and DenseNet, performed best, achieving an accuracy of 92.5% for the MC dataset and 95.5% for the Shenzen dataset.
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A perturbed particle swarm optimization using harmony search and mutation
(2020-07-01)
Phuchan, Wikrom
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Kruatrachue, Boontee
This paper applies the harmony search (HS) and mutation to lessens the stagnant of PSO. When a particle stops improving, it is mutated or replace by a position value created from harmony memory, which memorized improve locations of particles (Pbest) in search space. Another HS generates the best position among all particles (Gbest) to replace stagnant Gbest to sway the swarm from the trapping location. This HS has another harmony memory that contains improving Gbest. The results of the proposed algorithm are compared with related modified-PSO using 27 benchmark functions. The proposed algorithm locates optimum in more benchmark functions with faster execution.
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Enhancing of Particle Swarm Optimization Based Method for Multiple Motifs Detection in DNA Sequences Collections
(2020-05-01)
Som-In, Sarawoot
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Kimpan, Warangkhana
Genome sequence data consists of DNA sequences or input sequences. Each one includes nucleotides with chemical structures presented as characters: 'A ' 'C'' G''A','C','G', and 'T', and groups of motif sequences, called Transcription Factor Binding Sites (TFBSs), which are subsequences of DNA that lead to protein-synthesis. The detection of TFBSs is an important problem for bioinformatics research. With the similar patterns of motif sequences in TFBSs, computational algorithms for TFBSs detection have been improved to reduce resources used in laboratory setting. The metaheuristic algorithm is the important issue that has been continually improved to detect TFBSs with greater precision and recall. This paper proposes PSO_HD by applying Particle Swarm Optimization (PSO) as a pre-process and using Hamming distance to improve the efficiency of detecting TFBSs with more precision and recall. In order to measure its efficiency, the paper compares the TFBSs detection using PSO_HD algorithm with relevant algorithms in eight datasets. F-score is used as a measurement unit and compared to the related algorithms. The experimental results show that PSO_HD algorithm gives the highest average F-score, which can be indicated that the PSO_HD algorithm can improve the efficiency of detecting TFBSs with more precision and recall.
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Safety path planning with obstacle avoidance using particle swarm optimization for agv in manufacturing layout
(2019-02-01)
Praserttaweelap, Rawinun
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Kaitwanidvilai, Somyot
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Aoyama, Hisayuki
In robotic systems, path planning is the one of important processes for robot motion. The best path planning is required for shortest path searching that can make fast movement of robot. However, the real environment is not only the path from point to point but it has obstacles which are the one of constraints for best path searching. The obstacle avoidance is concerned to avoid the crashing between robot and obstacle under environment. In Hard Disk Drive manufacturing, the first priority is safety constraint for non-collision and second priority is shortest path for processing time saving. This research designed the algorithm for path planning and obstacle avoidance for AGV in Hard Disk Drive Manufacturing of Seagate Technology (Thailand) Ltd by using particle swarm optimization. The fitness function on particle swarm optimization process for particle searching has been integrated with obstacle avoidance function to find the best path for robot without collision and total distance to find the shortest path. This algorithm is applied to verifying the model performance. The simulation results of this research are done by MATLAB 2016b and illustrate the good performance on different cases with controlled parameter.
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NexusPSO: A novel algorithm to detect transcription factor binding sites
(2018-08-28)
Som, Sarawoot
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Kimpan, Warangkhana
The detection of transcription factor binding sites is a major problem in research in Biology. Methods and computer algorithms can be applied to reduce time complexity and cost of detecting transcription factor binding sites in laboratory experiments. One of the well-known methods commonly used is swarm intelligence. However, errors in detection of transcription factor binding sites can be caused by different binding sites in the same genome sequence. The purpose of this research is to improve the effectiveness and accuracy in the detection of transcription factor binding sites by applying the newly developed pre-processing procedure, Nexus, to Particle Swarm Optimization algorithm (NexusPSO). The accuracy of the NexusPSO algorithm was measured in comparison with other algorithms, using information content (IC) as an indicator, with Escherichia coli data. This study found that NexusPSO is the most accurate method being tested. NexusPSO was then tested using consensus sequences on Saccharomyces cerevisiae and Homo sapiens. NexusPSO showed nearly identical results when compared to DNA footprinting methods.
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A modified multi-swarm optimization with interchange GBEST and particle redistribution
(2017-10-19)
Chengkhuntod, Kanokporn
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Kruatrachue, Boontee
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Siriboon, Kritawan
The Particle Swarm Optimization (PSO) is an optimization algorithm using multiples particle to search solution space for an optimize solution. Each particle of PSO moves toward the best solution within its group. For this behavior, PSO often traps in local optimum. Many researchers proposed splitting a swarm into multiple swarms so that they may move to different local optimum. Besides, the mutation operation technique, the natural selection technique and the crossover operation technique are added to normal PSO process. These proposed techniques are called Selective Crossover base on Fitness in Multi-Swarm Optimization (SFMPSO) and Fast Multi-swarm Optimization (FMPSO). However, both techniques used too many evaluation calls dues to crossover and the mutation operation. This paper proposes setting the best position (GBEST) of a trapped swarm to GBEST of the other swarm. Then, the swarm's particle is redistributed in solution space before restart the trapped swarm. This proposed technique is evaluated on a set of twenty-six benchmark test functions. The experimental results show that the results are better than those of PSO, FMPSO and SFMPSO.
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Simultaneous control of frequency fluctuation and battery SOC in a smart grid using LFC and EV controllers based on optimal MIMO-MPC
(2017-03-01)
Pahasa, Jonglak
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Ngamroo, Issarachai
This paper proposes a simultaneous control of frequency deviation and electric vehicles (EVs) battery state of charge (SOC) using load frequency control (LFC) and EV controllers. In order to provide both frequency stabilization and SOC schedule near optimal performance within the whole operating regions, a multiple-input multiple-output model predictive control (MIMO-MPC) is employed for the coordination of LFC and EV controllers. The MIMO-MPC is an effective model- based prediction which calculates future control signals by an optimization of quadratic programming based on the plant model, past manipulate, measured disturbance, and control signals. By optimizing the input and output weights of the MIMO-MPC using particle swarm optimization (PSO), the optimal MIMO-MPC for simultaneous control of the LFC and EVs, is able to stabilize the frequency fluctuation and maintain the desired battery SOC at the certain time, effectively. Simulation study in a two-area interconnected power system with wind farms shows the effectiveness of the proposed MIMO-MPC over the proportional integral (PI) controller and the decentralized vehicle to grid control (DVC) controller.
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Particle swarm optimization based specified order robust-gap H∞ loop shaping controller design
(2017-01-01)
Konghuayrob, Poom
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Kaitwanividvilai, Somyot
The demand of data storage capacity in hard disk drive is expected to increase significantly which the areal density will achieve 10 Tbit/in<sup>2</sup> in the near future. An increase of high areal density leads to the reduction of distance per data tracks. Due to the narrow track pitch, hard disk drive system is easily sensitive to the disturbance and noise. This is the benchmark problem for controlling the high precision servo mechanism. The alternative robust loop shaping based v-gap metric is proposed to synthesize the optimal controller for stabilizing a voice coil motor in hard disk drive servo system. Additionally, the designed loop shaping is evaluated by the Riccati procedure with regard to the system robustness. This paper applies the potential particle swarm optimization (PSO) to minimize the close loop gap between the loop shaping of the plant with weighting function and the plant with proposed controller. Moreover, the structure of proposed controller can be specified as the 2<sup>nd</sup> order controller which is uncomplicated to implement in the actual application. The simulation illustrates similar results in terms of the performance tracking and disturbance rejection of proposed controller against the H<inf>∞</inf> loop shaping. Furthermore, the system stability index called stability margin with 0.434 emphasizes the robustness of the proposed controller.

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