scholarly journals Particle Swarm Optimization for Solving a Class of Type-1 and Type-2 Fuzzy Nonlinear Equations

2018 ◽  
Vol 8 (2) ◽  
pp. 103-110 ◽  
Author(s):  
Sheriff Sadiqbatcha ◽  
Saeed Jafarzadeh ◽  
Yiannis Ampatzidis
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Sets ◽  
Nonlinear Equations ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fuzzy Variables ◽  
Modified Particle Swarm Optimization ◽  
Fuzzy Polynomials ◽  
Exponential Equations

Abstract This paper proposes a modified particle swarm optimization (PSO) algorithm that can be used to solve a variety of fuzzy nonlinear equations, i.e. fuzzy polynomials and exponential equations. Fuzzy nonlinear equations are reduced to a number of interval nonlinear equations using alpha cuts. These equations are then sequentially solved using the proposed methodology. Finally, the membership functions of the fuzzy solutions are constructed using the interval results at each alpha cut. Unlike existing methods, the proposed algorithm does not impose any restriction on the fuzzy variables in the problem. It is designed to work for equations containing both positive and negative fuzzy sets and even for the cases when the support of the fuzzy sets extends across 0, which is a particularly problematic case.

scholarly journals Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

2021 ◽  
Author(s):  
Na Geng ◽  
Zhiting Chen ◽  
Quang A. Nguyen ◽  
Dunwei Gong
Keyword(s):  
Particle Swarm Optimization ◽  
Task Allocation ◽  
Optimal Algorithm ◽  
Particle Swarm ◽  
Evolutionary Process ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fitness Evaluation ◽  
Modified Particle Swarm Optimization ◽  
Uncertain Time

AbstractThis paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

Monopole-Gear Design Based on Neural Network and Modified Particle Swarm Optimization

2013 ◽  
Vol 477-478 ◽  
pp. 368-373 ◽  
Author(s):  
Hai Rong Fang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Simulated Annealing Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test Results ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Gear Design ◽  
Annealing Algorithm

In order to raise the design efficiency and get the most excellent design effect, this paper combined Particle Swarm Optimization (PSO) algorithm and put forward a new kind of neural network, which based on PSO algorithm, and the implementing framework of PSO and NARMA model. It gives the basic theory, steps and algorithm; The test results show that rapid global convergence and reached the lesser mean square error MSE) when compared with Genetic Algorithm, Simulated Annealing Algorithm, the BP algorithm with momentum term.

A Modified Particle Swarm Optimization Algorithm with Cases Studies

2011 ◽  
Vol 268-270 ◽  
pp. 823-828
Author(s):  
Cheng Chien Kuo ◽  
Hung Cheng Chen ◽  
Teng Fa Taso ◽  
Chin Ming Chiang
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Fast Calculation ◽  
Modified Particle Swarm Optimization ◽  
Stochastic Optimization Algorithms

s paper presents a hybrid algorithm, the “particle swarm optimization with simulated annealing behavior (SA-PSO)” algorithm, which combines the advantages of good solution quality in simulated annealing and fast calculation in particle swarm optimization. As stochastic optimization algorithms are sensitive to its parameters, this paper introduces criteria in selecting parameters to improve solution quality. To prove the usability and effectiveness of the proposed algorithm, simulations are performed using 20 different mathematical optimized functions of different dimensions. The results made from different algorithms are then compared between the quality of the solution, the efficiency of searching for the solution and the convergence characteristics. According to the simulation results, SA-PSO obtained higher efficiency, better quality and faster convergence speed than other compared algorithms.

scholarly journals Training High-Order Takagi-Sugeno Fuzzy Systems Using Batch Least Squares and Particle Swarm Optimization

2019 ◽  
Vol 22 (1) ◽  
pp. 22-34 ◽  
Author(s):  
Krzysztof Wiktorowicz ◽  
Tomasz Krzeszowski
Keyword(s):  
Particle Swarm Optimization ◽  
Least Squares ◽  
Fuzzy Sets ◽  
Fuzzy Systems ◽  
Piecewise Linear ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Takagi Sugeno

AbstractThis paper proposes two methods for training Takagi–Sugeno (T-S) fuzzy systems using batch least squares (BLS) and particle swarm optimization (PSO). The T-S system is considered with triangular and Gaussian membership functions in the antecedents and higher-order polynomials in the consequents of fuzzy rules. In the first method, the BLS determines the polynomials in a system in which the fuzzy sets are known. In the second method, the PSO algorithm determines the fuzzy sets, whereas the BLS determines the polynomials. In this paper, the ridge regression is used to stabilize the solution when the problem is close to the singularity. Thanks to this, the proposed methods can be applied when the number of observations is less than the number of predictors. Moreover, the leave-one-out cross-validation is used to avoid overfitting and this way to choose the structure of a fuzzy model. A method of obtaining piecewise linear regression by means of the zero-order T-S system is also presented.

A novel particle swarm optimization based robust H-infinity control for rotorcrafts

2014 ◽  
Vol 31 (4) ◽  
pp. 726-741 ◽  
Author(s):  
Jiyang Dai ◽  
Jin Ying ◽  
Chang Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Adaptive Mutation ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
H Infinity ◽  
Content Type ◽  
Modified Particle Swarm Optimization ◽  
H Infinity Control

Purpose – The purpose of this paper is to present a novel optimization approach to design a robust H-infinity controller. Design/methodology/approach – To use a modified particle swarm optimization (PSO) algorithm and to search for the optimal parameters of the weighting functions under the circumstance of the given structures of three weighting matrices in the H-infinity mixed sensitivity design. Findings – This constrained multi-objective optimization is a non-convex, non-smooth problem which is solved by a modified PSO algorithm. An adaptive mutation-based PSO (AMBPSO) algorithm is proposed to improve the search accuracy and convergence of the standard PSO algorithm. In the AMBPSO algorithm, the inertia weights are modified as a function with the gradient descent and the velocities and positions of the particles. Originality/value – The AMBPSO algorithm can efficiently solve such an optimization problem that a satisfactory robust H-infinity control performance can be obtained.

scholarly journals Efficient Energy Load Distribution Model using Modified Particle Swarm Optimization Algorithm

2021 ◽  
Vol 2 (4) ◽  
pp. 226-231
Author(s):  
Vijayakumar T ◽  
Vinothkanna R
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Electric Power ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Economic Load Dispatch ◽  
Electric Power Generation ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Modified Pso

Reduction of emission and energy conservation plays a major role in the current power system for realizing sustainable socio-economic development. The application prospects and practical significance of economic load dispatch issue in the electric power market is remarkable. The various generating sets must be assigned with load capacity in a reasonable manner for reducing the cost of electric power generation. This problem may be overcome by the proposed modified particle swarm optimization (PSO) algorithm. The practical issue is converted and modelled into its corresponding mathematical counterpart by establishing certain constraints. Further, a novel interdependence strategy along with a modified PSO algorithm is implemented for balancing the local search capability and global optimization. Multiple swarms are introduced in the modified PSO algorithm. Certain standard test functions are executed for specific analysis. Finally, the proposed modified PSO algorithm can optimize the economic load dispatch problem while saving the energy resources to a larger extent. The algorithm evaluation can be performed using real-time examples for verifying the efficiency. When compared to existing schemes like artificial bee colony (ABC), genetic algorithms (GAs), and conventional PSO algorithms, the proposed scheme offers lowest electric power generation cost and overcomes the load dispatch issue according to the simulation results.

scholarly journals Enhancement transient stability of power system using UPFC with M-PSO

2020 ◽  
Vol 17 (1) ◽  
pp. 61
Author(s):  
Rashid H. AL-Rubayi ◽  
Luay G. Ibrahim
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Transient Stability ◽  
Electrical Power ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Simulation Results ◽  
The Stability

<span>During the last few decades, electrical power demand enlarged significantly whereas power production and transmission expansions have been brutally restricted because of restricted resources as well as ecological constraints. Consequently, many transmission lines have been profoundly loading, so the stability of power system became a Limiting factor for transferring electrical power. Therefore, maintaining a secure and stable operation of electric power networks is deemed an important and challenging issue. Transient stability of a power system has been gained considerable attention from researchers due to its importance. The FACTs devices that provide opportunities to control the power and damping oscillations are used. Therefore, this paper sheds light on the modified particle swarm optimization (M-PSO) algorithm is used such in the paper to discover the design optimal the Proportional Integral controller (PI-C) parameters that improve the stability the Multi-Machine Power System (MMPS) with Unified Power Flow Controller (UPFC). Performance the power system under event of fault is investigating by utilizes the proposed two strategies to simulate the operational characteristics of power system by the UPFC using: first, the conventional (PI-C) based on Particle Swarm Optimization (PI-C-PSO); secondly, (PI-C) based on modified Particle Swarm Optimization (PI-C-M-PSO) algorithm. The simulation results show the behavior of power system with and without UPFC, that the proposed (PI-C-M-PSO) technicality has enhanced response the system compared for other techniques, that since it gives undershoot and over-shoot previously existence minimized in the transitions, it has a ripple lower. Matlab package has been employed to implement this study. The simulation results show that the transient stability of the respective system enhanced considerably with this technique.</span>

Multilevel Image Segmentation Using Modified Particle Swarm Optimization

2017 ◽  
pp. 106-142 ◽  
Author(s):  
Sourav De ◽  
Firoj Haque
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Optimization Technique ◽  
Real Life ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Gray Scale ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Segmented Images

Particle Swarm Optimization (PSO) is a well-known swarm optimization technique. PSO is very efficient to optimize the image segmentation problem. PSO algorithm have some drawbacks as the possible solutions may follow the global best solution at one stage. As a result, the probable solutions may bound within that locally optimized solutions. The proposed chapter tries to get over the drawback of the PSO algorithm and proposes a Modified Particle Swarm Optimization (MfPSO) algorithm to segment the multilevel images. The proposed method is compared with the original PSO algorithm and the renowned k-means algorithm. Comparison of the above mentioned existing methods with the proposed method are applied on three real life multilevel gray scale images. For this purpose, three standard objective functions are applied to evaluate the quality of the segmented images. The comparison shows that the proposed MfPSO algorithm is done better than the PSO algorithm and the k-means algorithm to segment the real life multilevel gray scale images.

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