MCBÜ Açıkerişim Sistemi :: Browsing by Subject "Particle swarm optimization (PSO)"

Browsing by Subject "Particle swarm optimization (PSO)"

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Design optimization of moment resisting steel frames using a cuckoo search algorithm
(Civil-Comp Press, 2012) Saka M.P.; Doǧan E.
The Cuckoo search algorithm is a recent addition to metaheuristic techniques. It simulates the breeding behaviour of certain cuckoo species into a numerical optimization technique. Cuckoo birds lay their eggs in the nests of other host birds so that their chicks when hatched can be nurtured by the host birds. The optimum design algorithm presented for moment resisting steel frames is based on the cuckoo search algorithm. The design algorithm selects the appropriate W sections for the beams and column of a steel frame out of 272 W sections listed in the LRFD-AISC (Load and Resistance Factor Design, American Institute of Steel Construction) [52] such that the code requirements are satisfied and the weight of steel frame is the minimum. Code specifications necessitate the consideration of a combined strength constraint with lateral torsional buckling for beam-column members. Furthermore displacement constraints as well as inter-storey drift restrictions of multi-storey frames are also included in the design formulation. Further constraints related with the constructability of a steel frame are also considered. The number of steel frames are designed by the algorithm presented to demonstrate its efficiency. The same steel frames are also designed by using the big bang-big crunch algorithm as well as the particle swarm optimizer for comparison. © Civil-Comp Press, 2012.
An improved particle swarm optimizer for steel grillage systems
(Techno-Press, 2013) Erdal F.; Doǧan E.; Saka M.P.
In this paper, an improved version of particle swarm optimization based optimum design algorithm (IPSO) is presented for the steel grillage systems. The optimum design problem is formulated considering the provisions of American Institute of Steel Construction concerning Load and Resistance Factor Design. The optimum design algorithm selects the appropriate W-sections for the beams of the grillage system such that the design constraints are satisfied and the grillage weight is the minimum. When an improved version of the technique is extended to be implemented, the related results and convergence performance prove to be better than the simple particle swarm optimization algorithm and some other meta-heuristic optimization techniques. The efficiency of different inertia weight parameters of the proposed algorithm is also numerically investigated considering a number of numerical grillage system examples. Copyright © 2013 Techno-Press, Ltd.
An efficient grouping genetic algorithm for U-shaped assembly line balancing problems with maximizing production rate
(Springer Verlag, 2017) Şahin M.; Kellegöz T.
U-type assembly line is one of the important tools that may increase companies’ production efficiency. In this study, two different modeling approaches proposed for the assembly line balancing problems have been used in modeling type-II U-line balancing problems, and the performances of these models have been compared with each other. It has been shown that using mathematical formulations to solve medium and large size problem instances is impractical since the problem is NP-hard. Therefore, a grouping genetic and simulated annealing algorithms have been developed, and a particle swarm optimization algorithm is adapted to compare with the proposed methods. A special crossover operator that always obtains feasible offspring has been suggested for the proposed grouping genetic algorithm. Furthermore, a local search procedure based on problem-specific knowledge was applied to increase the intensification of the algorithm. A set of well-known benchmark instances was solved to evaluate the effectiveness of the proposed and existing methods. Results showed that while the mathematical formulations can only be used to solve small size instances, metaheuristics can obtain high quality solutions for all size problem instances within acceptable CPU times. Moreover, grouping genetic algorithm has been found to be superior to the other methods according to the number of optimal solutions, or deviations from the lower bound values. © 2017, Springer-Verlag GmbH Germany.
Investigating the effect of joint behavior on the optimum design of steel frames via hunting search algorithm
(Hong Kong Institute of Steel Construction, 2018) Doğan E.; Şeker S.; Polat Saka M.; Kozanoğlu C.
This study aims to carry out the effect of beam-to-column connections on the minimum weight design of steel plane frames. In the practical analysis of steel frames, end connections are assumed to be either fully restrained or pin-connected. However, experiments reveal that the real behavior is between these extremes and should be taken into account for the realistic design of structures. Hunting search algorithm is used for the automation of optimum design process. It is a numerical optimization method inspired by group hunting of animals such as wolves and lions. It is proven that it is a reliable and efficient technique for obtaining the solution of discrete structural optimization problems. Present design algorithm developed on the basis of hunting search algorithm selects w-sections for the members of semi rigid steel frame from the complete list of w-sections given in LRFD-AISC (Load and Resistance Factor Design, American Institute of Steel Construction). The design constraints are implemented from the specifications of the same code which covers serviceability and strength limitations. The selection of w-sections is carried out such that the design limitations are satisfied and the weight of semi-rigid frame is the minimum. In order to demonstrate its efficiency, three different steel frames are designed by the optimum design algorithm presented. The designs obtained by use of this algorithm are also compared with the ones produced by particle swarm optimization method. © 2018, Hong Kong Institute of Steel Construction. All rights reserved.
Biomedical Text Categorization Based on Ensemble Pruning and Optimized Topic Modelling
(Hindawi Limited, 2018) Onan A.
Text mining is an important research direction, which involves several fields, such as information retrieval, information extraction, and text categorization. In this paper, we propose an efficient multiple classifier approach to text categorization based on swarm-optimized topic modelling. The Latent Dirichlet allocation (LDA) can overcome the high dimensionality problem of vector space model, but identifying appropriate parameter values is critical to performance of LDA. Swarm-optimized approach estimates the parameters of LDA, including the number of topics and all the other parameters involved in LDA. The hybrid ensemble pruning approach based on combined diversity measures and clustering aims to obtain a multiple classifier system with high predictive performance and better diversity. In this scheme, four different diversity measures (namely, disagreement measure, Q-statistics, the correlation coefficient, and the double fault measure) among classifiers of the ensemble are combined. Based on the combined diversity matrix, a swarm intelligence based clustering algorithm is employed to partition the classifiers into a number of disjoint groups and one classifier (with the highest predictive performance) from each cluster is selected to build the final multiple classifier system. The experimental results based on five biomedical text benchmarks have been conducted. In the swarm-optimized LDA, different metaheuristic algorithms (such as genetic algorithms, particle swarm optimization, firefly algorithm, cuckoo search algorithm, and bat algorithm) are considered. In the ensemble pruning, five metaheuristic clustering algorithms are evaluated. The experimental results on biomedical text benchmarks indicate that swarm-optimized LDA yields better predictive performance compared to the conventional LDA. In addition, the proposed multiple classifier system outperforms the conventional classification algorithms, ensemble learning, and ensemble pruning methods. © 2018 Aytuǧ Onan.
An Approach for Online Weight Update Using Particle Swarm Optimization in Dynamic Fuzzy Cognitive Maps
(Institute of Electrical and Electronics Engineers Inc., 2018) Altundogan T.G.; Karakose M.
Fuzzy cognitive maps (FCM) is a method to update a given initial vector to obtain the most stable state of a system, using a neighborhood of weights between these vectors and updating it over a series of iterations. FCMs are modeled with graphs. Neighbor weights between nodes are between-1 and 1. Nowadays it is used in business management, information technology, communication, health and medical decision making, engineering and computer vision. In this study, a dynamic FCM structure based on Particle Swarm Optimization (PSO) is given for determining node weights and online updating for modeling of dynamic systems with FCMs. Neighborhood weights in dynamic FCMs can be updated instantly and the system feedback is used for this update. In this work, updating the weights of the dynamic FCM is a PSO based approach that takes advantage of system feedback. In previous literature suggestions, dynamic FCM structure performs the weight updating process by using rule-based methods such as Hebbian. Metaheuristic methods are less complex and more efficient than rule-based methods in such optimization problems. In the developed PSO approach, the initialize vector state of the system, the weights between the vector nodes, and the desired steady state vector are taken into consideration. As a fitness function, the system has benefited from the convergence state to the desired steady state vector. As a stopping criterion for PSO, 100 ∗ n number of iteration limits have been applied for the initial vector with n nodes. The proposed method has been tested for five different scenarios with different node counts. © 2018 IEEE.
A new mixed-integer linear programming formulation and particle swarm optimization based hybrid heuristic for the problem of resource investment and balancing of the assembly line with multi-manned workstations
(Elsevier Ltd, 2019) Şahin M.; Kellegöz T.
Resource investment and balancing problem of an assembly line with parallel multi-manned workstations can be defined as the assignment of tasks to reduce the cost of the line, which includes the cost of opened workstations and required renewable resources. Although mentioned problem has been commonly occurred in industrial environment that produce large scale products in high volumes, there have been restricted number of studies in the literature about this field. This article proposes a new mixed-integer linear programming approach that can be used in solving small size instances of the problem. In addition, a new hybrid method has been developed to solve larger scale instances by combining particle swarm optimization algorithm with a special constructive heuristic. In the constructive heuristic, serial schedule generation scheme widely used in solving resource constrained project scheduling problems has been adapted to resource investment problem with some modifications. Proposed metaheuristic has been compared against a tabu search and cuckoo search algorithm taken from the assembly line balancing literature. Many precedence diagrams commonly used in solving various assembly line balancing problems in the literature, have been used to generate test instances for the considered problem type. After solving these test instances using each solution methods, it has been observed that the proposed hybrid metaheuristic yielded the solutions, which have acceptable deviations from the lower bounds. © 2019 Elsevier Ltd
Balancing Multi-Manned Assembly Lines With Walking Workers: Problem Definition, Mathematical Formulation, and an Electromagnetic Field Optimisation Algorithm
(Taylor and Francis Ltd., 2019) Şahin M.; Kellegöz T.
Assembly lines are widely used in industrial environments that produce standardised products in high volumes. Multi-manned assembly line is a special version of them that allows simultaneous operation of more than one worker at the same workstation. These lines are widely used in large-sized product manufacturing since they have many advantages over the simple one. This article has dealt with multi-manned assembly line balancing problem with walking workers for minimising the number of workers and workstations as the first and second objectives, respectively. A linear mixed-integer programming formulation of the problem has been firstly addressed after the problem definition is given. Besides that, a metaheuristic based on electromagnetic field optimisation algorithm has been improved. In addition to the classical electromagnetic field optimisation algorithm, a regeneration strategy has been applied to enhance diversification. A particle swarm optimisation algorithm from assembly line balancing literature has been modified to compare with the proposed algorithm. A group of test instances from many precedence diagrams were generated for evaluating the performances of all solution methods. Deviations from lower bound values of the number of workers/workstations and the number of optimal solutions obtained by these methods are concerned as performance criteria. The results obtained by the proposed programming formulations have been also compared with the solutions obtained by the traditional mathematical model of the multi-manned assembly line. Through the experimental results, the performance of the metaheuristic has been found very satisfactory according to the number of obtained optimal solutions and deviations from lower bound values. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.
An Anomaly Detection Study on Automotive Sensor Data Time Series for Vehicle Applications
(Institute of Electrical and Electronics Engineers Inc., 2021) Derse C.; El Baghdadi M.; Hegazy O.; Sensoz U.; Gezer H.N.; Nil M.
Anomaly detection in automotive systems has been a strong challenge: first, during the development phase, then after the manufacturing approval in ramp-up production and finally during the vehicles life cycle management. The numerous sensors positioned inside a vehicle generate more than a gigabyte of data at each second timeframe. These sensors are connected through the vehicle network, which comprises Electronic Control Units (ECUs) and Controller Area Network (CAN) buses. Each ECU gets input from its sensors, executes specific instructions and aims to monitor the vehicle's normal state detecting any irregular action corresponding to its observed behavior. The aggregator of all sensor data and control actions detects the anomalies in vehicle systems, which poses a multi-source big data problem. Detecting anomalies during manufacturing has turned out to be another research challenge after the introduction of Industry 4.0. This paper presents a performance comparison of different anomaly detection algorithms on time series originating from automotive sensor data. Interquartile range, isolation forest, particle swarm optimization and k-means clustering algorithms are used to detect outlier data in the study. © 2021 IEEE.
Chaotic slime mould optimization algorithm for global optimization
(Springer Science and Business Media B.V., 2022) Altay O.
Metaheuristic optimization methods; It is a well-known global optimization approach for large-scale search and optimization problems, commonly used to find the solution many different optimization problems. Slime mould optimization algorithm (SMA) is a recently presented metaheuristic technique that is inspired by the behavior of slime mould. Slow convergence speed is a fundamental problem in SMA as in other metaheuristic optimization methods. In order to improve the SMA method, 10 different chaotic maps have been applied for the first time in this article to generate chaotic values instead of random values in SMA. Using chaotic maps, it is aimed to increase the speed of SMA’s global convergence and prevent it from getting stuck in its local solutions. The Chaotic SMA (CSMA) proposed for the first time in this study was applied to 62 different benchmark functions. These are unimodal, multimodal, fixed dimension, CEC2019, and CEC2017 test suite. The results of the application have been comparatively analyzed and statistical analysis performed with the well-known metaheuristic optimization methods, particle swarm optimization and differential evolution algorithm, and recently proposed grey wolf optimization (GWO) and whale optimization algorithm (WOA). In addition, in the CEC2017 test suite, the CSMA method has been compared with the SMA, WOA, GWO, harris hawk optimization, archimedes optimization algorithm and COOT algorithms that have been proposed in recent years, and statistical analyzes have been made. In addition, CSMA has been tested in 3 different real-world engineering design problems. According to the experimental results, it was observed that CSMA achieved relatively more successful results in 62 different benchmark functions and real-world engineering design problems compared to other compared methods and standard SMA. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.
COMPARISON OF THE SUCCESS OF META-HEURISTIC ALGORITHMS IN TOOL PATH PLANNING OF COMPUTER NUMERICAL CONTROL MACHINE
(World Scientific, 2022) Çaşka S.; Gök K.; Gök A.
Carrying out an engineering process with the least cost and within the shortest time is the basic purpose in many fields of industry. In Computer Numerical Control (CNC) machining, performing a process by following a certain order reduces cost and time of the process. In the literature, there are research works involving varying methods that aim to minimize the length of the CNC machine tool path. In this study, the trajectory that the CNC machine tool follows while drilling holes on a plate was discussed within the Travelling Salesman Problem (TSP). Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO) methods were used to solve TSP. The case that the shortest tool path was obtained was determined by changing population size parameter in GA, PSO, and GWO methods. The results were presented in tables. © 2022 World Scientific Publishing Company.
Determination of Insulation Parameters with Optimization Algorithms
(Institute of Electrical and Electronics Engineers Inc., 2022) Gunal O.; Akpinar M.; Akpinar K.O.
Insulation is one of the essential energy efficiency and sustainability topics. While insulation is primarily the subject of buildings, insulation can also be made in pipes and heat exchangers in the factory environment. In the first example of this study, the insulation material that should be used for the specified heat loss in the case of a basic circular heat source being covered with insulation material was determined, while in the second case, the targeted insulation thickness with the accepted maximum heat loss and insulation material coefficient was tried to be determined. The results obtained show that errors were zero in particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms in the first case. In the second case, PSO, ABC, and firefly (FA) optimization algorithms have the lowest average error with 3x10-5%, 1x10-5%, 3x10-5%, respectively. © 2022 IEEE.
Prediction of natural frequencies of Rayleigh pipe by hybrid meta-heuristic artificial neural network
(Springer Science and Business Media Deutschland GmbH, 2023) Dagli B.Y.; Ergut A.; Turan M.E.
This paper focuses on determination of the natural frequencies in slenderness pipe flows by considering fluid–structure interaction approach. Rayleigh beam theory is used to model the pipe. The fluid in the pipe is assumed as ideal, steady and uniform. Hamilton’s variation principle is demonstrated to obtain the equation of motion of pipe–fluid system. The dimensionless partial differential equations of motion are converted into matrix equations, and the values of natural frequencies of first three modes are archived with the analytical method. The results are arranged to be a data set for hybrid meta-heuristic artificial neural network (ANN) method. Three different meta-heuristic algorithms are used to train the ANN: particle swarm optimization (PSO) and artificial bee colony (ABC) and grey wolf optimizer (GWO). The comparison is presented to find a suitable algorithm based on accuracy for determining the natural frequency of the Rayleigh pipe conveying fluid. The results show that the PSO algorithm outperforms the other meta-heuristics in terms of performance indicators in prediction analysis. However, all algorithms and models can predict the natural frequencies with rate with satisfactory accuracy. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering.
A novel hybrid multilayer perceptron neural network with improved grey wolf optimizer
(Springer Science and Business Media Deutschland GmbH, 2023) Altay O.; Varol Altay E.
The multilayer perceptron (MLP), a type of feed-forward neural network, is widely used in various artificial intelligence problems in the literature. Backpropagation is the most common learning method used in MLPs. The gradient-based backpropagation method, which is one of the classical methods, has some disadvantages such as entrapment in local minima, convergence speed, and initialization sensitivity. To eliminate or minimize these disadvantages, there are many studies in the literature that use metaheuristic optimization methods instead of classical methods. These methods are constantly being developed. One of these is an improved grey wolf optimizer (IMP-GWO) proposed to eliminate the disadvantages of the grey wolf optimizer (GWO), which suffers from a lack of search agent diversity, premature convergence, and imbalance between exploitation and exploration. In this study, a new hybrid method, IMP-GWO-MLP, machine learning method was designed for the first time by combining IMP-GWO and MLP. IMP-GWO was used to determine the weight and bias values, which are the most challenging parts of the MLP training phase. The proposed IMP-GWO-MLP was applied to 20 datasets consisting of three different approximations, eight regression problems, and nine classification problems. The results obtained have been suggested in the literature and compared with the gradient descent-based MLP, commonly used GWO, particle swarm optimization, whale optimization algorithm, ant lion algorithm, and genetic algorithm-based MLP methods. The experimental results show that the proposed method is superior to other state-of-the-art methods in the literature. In addition, it is thought that the proposed method can be modeled with high success in real-world problems. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Design of a Novel PID Controller Based on Machine Learning Algorithm for a Micro-Thermoelectric Cooler of the Polymerase Chain Reaction Device
(Institute of Electrical and Electronics Engineers Inc., 2024) Arikusu Y.S.; Bayhan N.
The combined use of Extreme Gradient Boosting (XGBoost) algorithm, one of the machine learning (ML) methods, and a generalization of Hermite-Biehler theorem to obtain a novel PID controller that will ensure robustly stable and optimized operation of a micro thermoelectric cooler (Micro-TEC), which is the main part of a Polymerase chain reaction (PCR) device, is a unique approach of our study compared to previous studies. Therefore, we first established a mathematical model of the micro-TEC by making real-time measurements and then, a new data set was created to find the optimum parameter values of PID controller, and finally, XGBoost Hyperparameters with GridSearchCV was used for the first time to predict PID controller parameters. The XGBoost algorithm achieved 97% training success and 91% test success in estimating the parameters of the PID controller. Moreover, the novel controller developed using the XGBoost algorithm in this study has an impressive speed of 3 seconds. Additionally, our proposed method was compared with various metaheuristic optimization algorithms in terms of error percentage. The error percentages of XGBoost, the equilibrium optimization, the particle swarm optimization and the artificial bee colony optimization algorithms were found to be 0.4%, 1.1%, 3.7% and 11.1%, respectively. It is observed the settling times of micro-TEC with ML-PID controller for all five PCR cycles are 4.86, 44, 83.4, 123 and 162.5 seconds, respectively, and the overshoot values are below 5%. The proposed method gave the smallest settling time, error and overshoot percentages compared to these metaheuristic optimization algorithms. © 2013 IEEE.

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