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1

Toward the best combination of optimization with fuzzy systems to obtain the best solution for the GA and PSO algorithms using parallel processing

Valdez Fevrier, Kawano Yunkio, Melin Patricia

Journal of Automation Mobile Robotics and Intelligent Systems

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2020

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Vol. 14, No. 1

55--64

EN

In general, this paper focuses on finding the best configuration for PSO and GA, using the different migration blocks, as well as the different sets of the fuzzy systems rules. To achieve this goal, two optimization algorithms were configured in parallel to be able to integrate a migration block that allow us to generate diversity within the subpopulations used in each algorithm, which are: the particle swarm optimization (PSO) and the genetic algorithm (GA). Dynamic parameter adjustment was also performed with a fuzzy system for the parameters within the PSO algorithm, which are the following: cognitive, social and inertial weight parameter. In the GA case, only the crossover parameter was modified.

2

Particle swarm optimization and discrete artificial bee colony algorithms for solving production scheduling problems

Witkowski Tadeusz

Technical Sciences / University of Warmia and Mazury in Olsztyn

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2019

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nr 22(1)

61--74

EN

This paper shows the use of Discrete Artificial Bee Colony (DABC) and Particle Swarm Optimization (PSO) algorithm for solving the job shop scheduling problem (JSSP) with the objective of minimizing makespan. The Job Shop Scheduling Problem is one of the most difficult problems, as it is classified as an NP-complete one. Stochastic search techniques such as swarm and evolutionary algorithms are used to find a good solution. Our objective is to evaluate the efficiency of DABC and PSO swarm algorithms on many tests of JSSP problems. DABC and PSO algorithms have been developed for solving real production scheduling problem too. The experiment results indicate that this problem can be effectively solved by PSO and DABC algorithms.

3

Power loss minimization by voltage transformer turns ratio selection based on Particle Swarm Optimization

Manusov Vadim Zinovievich, Matrenin Pavel Victorovich, Khasanzoda Nasrullo

Przegląd Elektrotechniczny

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2019

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R. 95, nr 8

127--131

EN

The research considers the optimization of the taps position of voltage transformers to minimize power loss. The Particle Swarm Optimization algorithm is implemented to this optimization problem. The advantage of this algorithm is the ability to adapt to an optimization problem. It was found out that the Particle Swarm Optimization algorithm is more productive than the greedy heuristic algorithm based on the division of this optimization problem into subtasks. Also, the paper studied the influence of particle velocity restriction on the efficiency of the algorithm.

PL

W pracy analizowano metode optymalizacji strat transformatora przez dobór stosunku uzwojeń. Do tego celu wykorzystano algorytm genetyczny PSO. Porównano prace układu z innymi algorytmami adaptacyjnymi.

4

PI-controller tuning optimization via PSO-based technique

Romasevych Yuriy, Loveikin Viatcheslav, Usenko Sergii

Przegląd Elektrotechniczny

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2019

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R. 95, nr 7

33--37

EN

The technique of PI-controller tuning, which is based on a modification of the particle swarm optimization method, has been developed in the article. In order to take into account the most important quality indicators of plant controlling the complex criterion was developed. PI-controller tuning procedure has been reduced to the problem of criterion minimization. In the article, five benchmark transfer functions were used to estimate the technique. Comparative analysis with other well-known tuning techniques revealed the superiority of the proposed approach.

PL

W artykule przedtawiono metodę optymalizacji sterownika PI wykorzystującą algorytm rojowy. W artykule przedstawiono pięć rezultatów testów oraz porównanie tej metody z innymi powszechnie stosowanymi.

5

Optimization algorithm for number and wells placement

Łętkowski Piotr

Nafta-Gaz

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2019

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R. 75, nr 12

744--750

EN

Determination of the optimal number and placement of production wells is crucial for the effective depletion of the hydrocarbon reservoir. Due to the strongly non-linearity of the problem and the occurrence of multiple local minimums in the response function the non-gradient optimization methods in combination with reservoir simulations are most commonly used for its solution. However, it should be noted that most of the research works dedicated to this issue describe the process of placement optimization but not the number of drilling wells assuming that it was arbitrary set. This is partly due to the fact that known and used optimization methods operate on a fixed number of optimization parameters, therefore the number of production wells can not change during the optimization process. The paper is dedicated to the attempt to build an algorithm that allows simultaneous optimization of the number and position of production wells with respect to the discounted profit in a given period of operation. The basic optimization method in the presented algorithm is the Particle Swarm Optimization (PSO) – one of the most effective non-gradient optimization methods that belongs to the group of methods applying the swarm’s intelligence. Taking into account the number of drilling wells in the optimization process means that the algorithm operates on a variable number of parameters. The objective algorithm starts optimization from an arbitrarily set number of producers, reducing it gradually. Efficiency tests conducted on the sample reservoir PUNQ-S3 indicated a satisfactory convergence of the proposed method. The computing program created implements the mechanisms of convergence enhancement by improving the boundary conditions for the optimization method. The minimum separation distance control between production wells was also introduced at the initial stage of optimization process. Although the algorithm is characterized by satisfactory convergence it would be advisable to improve it by using a hybrid method to increase its effectiveness in the local optimization phase and to introduce minimum well spacing during the entire optimization process.

PL

Określenie optymalnej liczby i położenia odwiertów eksploatacyjnych jest kluczowe dla efektywnej eksploatacji złoża węglowodorowego. Ze względu na silnie nieliniowy charakter problemu oraz występowanie w funkcji odpowiedzi wielokrotnych minimów lokalnych do jego rozwiązania najczęściej wykorzystywane są bezgradientowe metody optymalizacyjne w połączeniu z symulacjami złożowymi. Należy jednak zauważyć, że większość prac poświęconych temu zagadnieniu opisuje proces optymalizacji położenia, a nie liczby odwiertów, przyjmując, że jest ona dana arbitralnie. Wynika to po części z faktu, że znane i stosowane metody optymalizacyjne operują na stałej liczbie parametrów optymalizacyjnych, w związku z czym liczba odwiertów wydobywczych nie może zmieniać się w trakcie procesu optymalizacji. Artykuł jest poświęcony próbie zbudowania algorytmu umożliwiającego równoczesną optymalizację liczby i położenia odwiertów wydobywczych ze względu na zdyskontowany zysk w zadanym okresie eksploatacji. Podstawową metodą optymalizacyjną w prezentowanym algorytmie jest optymalizacja rojem cząstek (ang. PSO) – jedna z najbardziej efektywnych metod optymalizacji bezgradientowej, należąca do grupy metod wykorzystujących inteligencję roju. Próby efektywności metody przeprowadzone na przykładzie złoża testowego PUNQ-S3 wskazały na zadowalającą zbieżność zaproponowanej metody, dla której na początkowym etapie zastosowano kontrolę minimalnej odległości pomiędzy odwiertami. Jakkolwiek algorytm charakteryzuje się zadowalającą zbieżnością, to jednak wskazane byłoby jego udoskonalenie poprzez wykorzystanie metody hybrydowej w celu zwiększenia jego efektywności w fazie optymalizacji lokalnej oraz wprowadzenie kontroli odległości minimalnej w trakcie całego procesu optymalizacji.

6

Optimal configuration of standalone wind-solar-storage complementary generation system based on the GA-PSO algorithm

Sun Qian, Ma Jianwei, She Yanjie, Zhang Jingchao, Gu Bo, Zhang Zichao

Journal of Power Technologies

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2019

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Vol. 99, nr 4

231--236

EN

The capacity configuration of the standalone wind–solar–storage complementary power generation system (SWS system) is affected by environmental, climate condition, load and other stochastic factors. This makes the capacity configuration of the SWS system problematic when the capacity configuration method of traditional power generation is used. An optimal configuration method of the SWS system based on the hybrid genetic algorithm and particle swarm optimization (GA-PSO) algorithm is proposed in this study to improve the stability and economy of the SWS system. The constituent elements of investment, maintenance cost and various reliability constraints of the SWS system were also discussed. The optimal configuration of the SWS system based on GA-PSO was explored to achieve the optimization objective, which was to minimize investment and maintenance costs of the SWS system while maintaining power supply reliability. The investment and maintenance costs of the SWS system under different configuration methods were calculated and analyzed on the bases of the monthly mean wind speed, solar radiation and load data of Xiaoertai Village in Zhangbei County of Hebei Province in the last 10 years. Results show that the optimal configuration method based on the GA-PSO algorithm could effectively improve the economy of the system and meet the requirements of system stability. The proposed method shows great potential for guiding the optimal configuration of the SWS system in remote areas.

7

On the efficiency of population-based optimization in finding best parameters for RGB-D visual odometry

Kostusiak Aleksander, Skrzypczyński Piotr

Journal of Automation Mobile Robotics and Intelligent Systems

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2019

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Vol. 13, No. 2

5--14

EN

Visual odometry estimates the transformations between consecutive frames of a video stream in order to recover the camera’s trajectory. As this approach does not require to build a map of the observed environment, it is fast and simple to implement. In the last decade RGBD cameras proliferated in roboTIcs, being also the sensors of choice for many practical visual odometry systems. Although RGB-D cameras provide readily available depth images, that greatly simplify the frame-to-frame transformations computaTIon, the number of numerical parameters that have to be set properly in a visual odometry system to obtain an accurate trajectory estimate remains high. Whereas seƫng them by hand is certainly possible, it is a tedious try-and-error task. Therefore, in this article we make an assessment of two population-based approaches to parameter opTImizaTIon, that are for long time applied in various areas of robotics, as means to find best parameters of a simple RGB-D visual odometry system. The optimization algorithms investigated here are particle swarm optimization and an evolutionary algorithm variant. We focus on the optimization methods themselves, rather than on the visual odometry algorithm, seeking an efficient procedure to find parameters that minimize the estimated trajectory errors. From the experimental results we draw conclusions as to both the efficiency of the optimization methods, and the role of particular parameters in the visual odometry system.

8

Collision-free autonomous robot navigation in unknown environments utilizing PSO for path planning

Krell Evan, Sheta Alaa, Balasubramanian Arun Prassanth Ramaswamy, King Scott A.

Journal of Artificial Intelligence and Soft Computing Research

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2019

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Vol. 9, No. 4

267--282

EN

The autonomous navigation of robots in unknown environments is a challenge since it needs the integration of a several subsystems to implement different functionality. It needs drawing a map of the environment, robot map localization, motion planning or path following, implementing the path in real-world, and many others; all have to be implemented simultaneously. Thus, the development of autonomous robot navigation (ARN) problem is essential for the growth of the robotics field of research. In this paper, we present a simulation of a swarm intelligence method is known as Particle Swarm Optimization (PSO) to develop an ARN system that can navigate in an unknown environment, reaching a pre-defined goal and become collision-free. The proposed system is built such that each subsystem manipulates a specific task which integrated to achieve the robot mission. PSO is used to optimize the robot path by providing several waypoints that minimize the robot traveling distance. The Gazebo simulator was used to test the response of the system under various envirvector representing a solution to the optimization problem.onmental conditions. The proposed ARN system maintained robust navigation and avoided the obstacles in different unknown environments. vector representing a solution to the optimization problem.

9

Particle swarm-optimized support vector machines and pre-processing techniques for remaining useful life estimation of bearings

Souto Maior Caio Bezerra, das Chagas Moura Márcio, Lins Isis Didier

Eksploatacja i Niezawodność

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2019

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Vol. 21, no. 4

610--618

EN

The useful life time of equipment is an important variable related to system prognosis, and its accurate estimation leads to several competitive advantage in industry. In this paper, Remaining Useful Lifetime (RUL) prediction is estimated by Particle Swarm optimized Support Vector Machines (PSO+SVM) considering two possible pre-processing techniques to improve input quality: Empirical Mode Decomposition (EMD) and Wavelet Transforms (WT). Here, EMD and WT coupled with SVM are used to predict RUL of bearing from the IEEE PHM Challenge 2012 big dataset. Specifically, two cases were analyzed: considering the complete vibration dataset and considering truncated vibration dataset. Finally, predictions provided from models applying both pre-processing techniques are compared against results obtained from PSO+SVM without any pre-processing approach. As conclusion, EMD+SVM presented more accurate predictions and outperformed the other models.

PL

Okres użytkowania sprzętu jest ważną zmienną związaną z prognozowaniem pracy systemu, a możliwość jego dokładnej oceny daje zakładom przemysłowym znaczną przewagę konkurencyjną. W tym artykule pozostały czas pracy (Remaining Useful Life, RUL) szacowano za pomocą maszyn wektorów nośnych zoptymalizowanych rojem cząstek (SVM+PSO) z uwzględnieniem dwóch technik przetwarzania wstępnego pozwalających na poprawę jakości danych wejściowych: empirycznej dekompozycji sygnału (Empirical Mode Decomposition, EMD) oraz transformat falkowych (Wavelet Transforms, WT). W niniejszej pracy, EMD i falki w połączeniu z SVM wykorzystano do prognozowania RUL łożyska ze zbioru danych IEEE PHM Challenge 2012 Big Dataset. W szczególności, przeanalizowano dwa przypadki: uwzględniający kompletny zestaw danych o drganiach oraz drugi, biorący pod uwagę okrojoną wersję tego zbioru. Prognozy otrzymane na podstawie modeli, w których zastosowano obie techniki przetwarzania wstępnego porównano z wynikami uzyskanymi za pomocą PSO + SVM bez wstępnego przetwarzania danych. Wyniki pokazały, że model EMD + SVM generował dokładniejsze prognozy i tym samym przewyższał pozostałe badane modele.

10

Early prediction of remaining discharge time for lithium-ion batteries considering parameter correlation between discharge stages

Yu Jinsong, Yang Jie, Tang Diyin, Dai Jing

Eksploatacja i Niezawodność

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2019

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Vol. 21, no. 1

81--89

EN

In this paper, we propose a method for making early predictions of remaining discharge time (RDT) that considers information about future battery discharge process. Instead of analyzing the entire degradation process of a battery, as in the existing literature, we obtain the information about future battery condition by decomposing the discharge model into three stages, according to level of voltage loss. Correlation between model parameters at the first and last stages of discharge process allows the values of model parameters in the future to be used to predict the value of parameters at early stages of discharge. The particle swarm optimization (PSO) and particle filter (PF) algorithms are employed to update parameters when new voltage data is available. A case study demonstrates that the proposed approach predicts RDT more accurately than the benchmark PF-based prediction method, regardless of the degradation period of the battery.

PL

W pracy zaproponowano metodę wczesnego przewidywania czasu pozostałego do rozładowania baterii (RDT), która uwzględnia informacje na temat przyszłego procesu jej rozładowywania. Zamiast analizować cały proces degradacji baterii, jak to ma miejsce w literaturze przedmiotu, wykorzystano informacje o przyszłym stanie baterii uzyskane na drodze podziału modelu procesu rozładowania na trzy etapy, według poziomu utraty napięcia. Korelacje między parametrami modelu uzyskanymi na pierwszym i ostatnim etapie procesu rozładowania baterii umożliwiają wykorzystanie przyszłych wartości parametrów do przewidywania wartości parametrów we wczesnych etapach rozładowania. Do aktualizacji parametrów zgodnie z napływającymi nowymi danymi napięciowymi wykorzystano algorytm optymalizacji rojem cząstek (PSO) i algorytm filtra cząsteczkowego (PF). Studium przypadku pokazuje, że proponowane podejście pozwala bardziej precyzyjnie prognozować RDT niż metoda prognozowania oparta na PF, niezależnie od okresu degradacji baterii.

11

The multi-constrained multicast routing improved by hybrid bacteria foraging-particle swarm optimization

Sahoo Satya Prakash, Kabat Manas Ranjan

Computer Science

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2019

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Vol. 20 (2)

245--269

EN

To solve multicast routing under multiple constraints, it is required to generate a multicast tree that ranges from a source to the destinations with minimum cost subject to several constraints. In this paper, PSO has been embedded with BFO to improve the convergence speed and avoid premature convergence that will be used for solving QoS multicast routing problem. The algorithm proposed here generates a set of delay compelled links to every destination present in the multicast group. Then the Bacteria Foraging Algorithm (BFA) selects the paths to all the destinations sensibly from the set of least delay paths to construct a multicast tree. The robustness of the algorithm being proposed had been established through the simulation. The efficiency and effectiveness of the algorithm being proposed was validated through the comparison study with other existing meta-heuristic algorithms. It shows that our proposed algorithm IBF-PSO outperforms its competitive algorithms.

12

Multi-channeled MR brain image segmentation: A novel double optimization approach combined with clustering technique for tumor identification and tissue segmentation

Narayanan Anitha, Rajasekaran M. Pallikonda, Zhang Yudong, Govindaraj Vishnuvarthanan, Thiyagarajan Arunprasath

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 2

350--381

EN

Growth of cancer cells within the human body is a major outcome of the manipulation of cells and it has resulted in the deterioration of the life span of humans. The impact of cancer cells is irretrievable and it has paved the way to the formation of tumors within the human body. For achieving and developing a single-structured framework to prominently identify the tumor regions and segmenting the tissue structures specifically in human brain, a novel combinational algorithm is proposed through this paper. The algorithm has been embodied with two optimization techniques namely particle swarm optimization (PSO) and bacteria foraging optimization (BFO), wherein, PSO helps in finding the best position of global bacterium for BFO, consecutively, BFO supports the modified fuzzy c means (MFCM) algorithm by providing optimized cluster heads. Finally, MFCM segments the tissue regions and identifies the tumor portion, thereby reducing the interaction and complication experienced by a radiologist during patient diagnosis. The strength of the proposed algorithm is proven by comparing it with the state-of-the-art techniques by means of evaluation parameters like mean squared error (MSE), peak signal to noise ratio (PSNR), sensitivity, specificity, etc., Data sets used in this paper were exclusively obtained from hospital, Brain web simulator and BRATS-2013 challenge. The sensitivity and specificity values for 115 MR brain slice images.

13

Clustering and Classification based real time analysis of health monitoring and risk assessment in Wireless Body Sensor Networks

Alameen Abdalla, Gupta Ashu

Bio-Algorithms and Med-Systems

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2019

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Vol. 15, no. 4

art. no. 20190016

EN

Wireless body sensor networks (WBSNs) play a vital role in monitoring the health conditions of patients and are a low-cost solution for dealing with several healthcare applications. However, processing a large amount of data and making feasible decisions in emergency cases are the major challenges attributed to WBSNs. Thus, this paper addresses these challenges by designing a deep learning approach for health risk assessment by proposing fractional cat based salp swarm algorithm (FCSSA). At first, the WBSN nodes are utilized for sensing data from patient health records to acquire certain parameters for making the assessment. Based on the obtained parameters, WBSN nodes transmit the data to the target node. Here, the hybrid harmony search algorithm and particle swarm optimization (hybrid HSA-PSO) is used for determining the optimal cluster head. Then, the results produced by the hybrid HSA-PSO are given to the target node, in which the deep belief network (DBN) is used for classifying the health records for the health risk assessment. Here, the DBN is trained using the proposed FCSSA, which is developed by integrating fractional cat swarm optimization (FCSO) and salp swarm algorithm (SSA) for initiating the classification. The proposed FCSSA-based DBN shows better performance using metrics, namely accuracy, energy, and throughput with values 94.604, 0.145, and 0.058, respectively.

14

A hybrid method for the optimal reactive power dispatch and the control of voltagesin an electrical energy network

Benchabira Aissa, Khiat Mounir

Archives of Electrical Engineering

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2019

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Vol. 68, nr 3

535--551

EN

This paper presents the resolution of the optimal reactive power dispatch (ORPD) problem and the control of voltages in an electrical energy system by using a hybrid algorithm based on the particle swarmoptimization (PSO) method and interior point method (IPM). The IPM is based on the logarithmic barrier (LB-IPM) technique while respecting the non-linear equality and inequality constraints. The particle swarmoptimization-logarithmic barrier-interior point method (PSO-LB-IPM) is used to adjust the control variables, namely the reactive powers, the generator voltages and the load controllers of the transformers, in order to ensure convergence towards a better solution with the probability of reaching the global optimum. The proposed method was first tested and validated on a two-variable mathematical function using MATLAB as a calculation and execution tool, and then it is applied to the ORPD problem to minimize the total active losses in an electrical energy network. To validate the method a testwas carried out on the IEEE electrical energy network of 57 buses.

15

Cellular particle swarm optimization with a simple adaptive local search strategy for the permutation flow shop scheduling problem

Seck-Tuoh-Mora Juan C., Medina-Marin Joselito, Martinez-Gomez Erick S., Hernandez-Gress Eva S., Hernandez-Romero Norberto, Volpi-Leon Valeria

Archives of Control Sciences

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2019

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Vol. 29, No. 2

205--226

EN

Permutation flow shop scheduling problem deals with the production planning of a number of jobs processed by a set of machines in the same order. Several metaheuristics have been proposed for minimizing the makespan of this problem. Taking as basis the previous Alternate Two-Phase PSO (ATPPSO) method and the neighborhood concepts of the Cellular PSO algorithm proposed for continuous problems, this paper proposes the improvement of ATPPSO with a simple adaptive local search strategy (called CAPSO-SALS) to enhance its performance. CAPSO-SALS keeps the simplicity of ATPPSO and boosts the local search based on a neighborhood for every solution. Neighbors are produced by interchanges or insertions of jobs which are selected by a linear roulette scheme depending of the makespan of the best personal positions. The performance of CAPSO-SALS is evaluated using the 12 different sets of Taillard’s benchmark problems and then is contrasted with the original and another previous enhancement of the ATPPSO algorithm. Finally, CAPSO-SALS is compared as well with other ten classic and state-of-art metaheuristics, obtaining satisfactory results.

16

Dynamics of Stochastic vs. Greedy Heuristics in Traveling Salesman Problem

Białogłowski M., Staniaszek M., Laskowski W., Grudniak M.

Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki

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2018

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nr 19

7--24

EN

We studied the relative performance of stochastic heuristics in order to establish the relations between the fundamental elements of their mechanisms. The insights on their dynamics, abstracted from the implementation details, may contribute to the development of an efficient framework for design of new probabilistic methods. For that, we applied four general optimization heuristics with varying number of hyperparameters to traveling salesman problem. A problem-specific greedy approach (Nearest Neighbor) served as a reference for the results of: Monte Carlo, Simulated Annealing, Genetic Algorithm, and Particle Swarm Optimization. The more robust heuristics – with higher configuration potential, i.e. with more hyperparameters – outperformed the smart ones, being surpassed only by the method specifically designed for the task.

17

Particle swarm optimization of the multioscillatory LQR for a three-phase grid-tie converter

Gałecki A., Michalczuk M., Kaszewski A., Ufnalski B., Grzesiak L.

Przegląd Elektrotechniczny

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2018

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R. 94, nr 6

43--48

EN

This paper presents an evolutionary optimization of the linear-quadratic (LQ) current controller for a three-phase grid-tie voltage source converter with an L-type input ﬁlter. The current control system is equipped with multi-oscillatory terms, which enable the converter to obtain nearly sinusoidal shape and balanced input currents under unbalanced and distorted grid voltage conditions. The augmentation of the state vector to include additional states which describe dynamics of disturbances increases the number of weights to be selected for a cost function in the LQR procedure design. Therefore, it is proposed that optimal weighting factors are sought using particle-swarm-based method. Finally, the simulational tuning based on the linear model and the numerical veriﬁcation based on a non-linear model of the system with a pulse width modulator are addressed.

PL

Artykuł prezentuje optymalizację ewolucyjną liniowo-kwadratowego regulatora prądu dla trójfazowego przekształtnika sieciowego z ﬁltrem wejściowym typu L. Układ regulacji prądu jest wypozażony w człony oscylacyjne co pozwala na kształtowanie niemal sinusoidalnych i symetrycznych prądów wejściowych w warunkach występowania wyższych harmonicznych i asymetrii napięć sieci. Rozszerzenie wektora stanu o dodatkowe stany opisujące dynamikę zakłóceń zwiększa liczbę wag, które należy dobrać dla funkcji celu ujętej w procedurze projektowania LQR. Dlatego zaproponowano dobór optymalnych wsółczynników wagowych przy użyciu optymalizacji metodą roju cząstek. Finalnie zostały omówione strojenie symulacyjne na modelu liniowym oraz weryﬁkacja numeryczna na modelu nieliniowym z modulatorem szerokości impulsów.

18

Error mitigation algorithm based on bidirectional fitting method for collision avoidance of Unmanned Surface Vehicle

Song L., Chen Z., Mao Y., Dong Z., Xiang Z.

Polish Maritime Research

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2018

|

nr 4

13--20

EN

Radars and sensors are essential devices for an Unmanned Surface Vehicle (USV) to detect obstacles. Their precision has improved significantly in recent years with relatively accurate capability to locate obstacles. However, small detection errors in the estimation and prediction of trajectories of obstacles may cause serious problems in accuracy, thereby damaging the judgment of USV and affecting the effectiveness of collision avoidance. In this study, the effect of radar errors on the prediction accuracy of obstacle position is studied on the basis of the autoregressive prediction model. The cause of radar error is also analyzed. Subsequently, a bidirectional adaptive filtering algorithm based on polynomial fitting and particle swarm optimization is proposed to eliminate the observed errors in vertical and abscissa coordinates. Then, simulations of obstacle tracking and prediction are carried out, and the results show the validity of the algorithm. Finally, the method is used to simulate the collision avoidance of USV, and the results show the validity and reliability of the algorithm.

19

An Analytical Study for the Role of Fuzzy Logic in Improving Metaheuristic Optimization Algorithms

Vij Sonakshi, Jain Amita, Tayal Devendra, Castillo Oscar

Journal of Automation Mobile Robotics and Intelligent Systems

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2018

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Vol. 12, No. 4

11--27

EN

The research applications of fuzzy logic have always been multidisciplinary in nature due to its ability in handling vagueness and imprecision. This paper presents an analytical study in the role of fuzzy logic in the area of metaheuristics using Web of Science (WoS) as the data source. In this case, 178 research papers are extracted from it in the time span of 1989-2016. This paper analyzes various aspects of a research publication in a scientometric manner. The top cited research papers, country wise contribution, topmost organizations, top research areas, top source titles, control terms and WoS categories are analyzed. Also, the top 3 fuzzy evolutionary algorithms are extracted and their top research papers are mentioned along with their topmost research domain. Since neuro fuzzy logic poses feasible options for solving numerous research problems, hence a section is also included by the authors to present an analytical study regarding research in it. Overall, this study helps in evaluating the recent research patterns in the field of fuzzy metaheuristics along with envisioning the future trends for the same. While on one hand this helps in providing a new path to the researchers who are beginners in this field as they can start exploring it through the analysis mentioned here, on the other hand it provides an insight to professional researchers too who can dig a little deeper in this field using knowledge from this study.

20

Using particle swarm optimization to accurately identify syntactic phrases in free text

Tambouratzis G.

Journal of Artificial Intelligence and Soft Computing Research

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2018

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Vol. 8, No. 1

63--77

EN

The present article reviews the application of Particle Swarm Optimization (PSO) algorithms to optimize a phrasing model, which splits any text into linguistically-motivated phrases. In terms of its functionality, this phrasing model is equivalent to a shallow parser. The phrasing model combines attractive and repulsive forces between neighbouring words in a sentence to determine which segmentation points are required. The extrapolation of phrases in the specific application is aimed towards the automatic translation of unconstrained text from a source language to a target language via a phrase-based system, and thus the phrasing needs to be accurate and consistent to the training data. Experimental results indicate that PSO is effective in optimising the weights of the proposed parser system, using two different variants, namely sPSO and AdPSO. These variants result in statistically significant improvements over earlier phrasing results. An analysis of the experimental results leads to a proposed modification in the PSO algorithm, to prevent the swarm from stagnation, by improving the handling of the velocity component of particles. This modification results in more effective training sequences where the search for new solutions is extended in comparison to the basic PSO algorithm. As a consequence, further improvements are achieved in the accuracy of the phrasing module.