scholarly journals A Fast Firefly Algorithm for Function Optimization: Application to the Control of BLDC Motor

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5267
Author(s):  
Smail Bazi ◽  
Redha Benzid ◽  
Yakoub Bazi ◽  
Mohamd Mahmoud Al Rahhal
Keyword(s):  
Direct Current ◽  
Electric Motor ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Function Optimization ◽  
Performance Criteria ◽  
Bldc Motor ◽  
Pi Regulator ◽  
Original Algorithm

Firefly Algorithm (FA) is a recent swarm intelligence first introduced by X.S. Yang in 2008. It has been widely used to solve several optimization problems. Since then, many research works were elaborated presenting modified versions intending to improve performances of the standard one. Consequently, this article aims to present an accelerated variant compared to the original Algorithm. Through the resolving of some benchmark functions to reach optimal solution, obtained results demonstrate the superiority of the suggested alternative, so-called Fast Firefly Algorithm (FFA), when faced with those of the standard FA in term of convergence fastness to the global solution according to an almost similar precision. Additionally, a successful application for the control of a brushless direct current electric motor (BLDC) motor by optimization of the Proportional Integral (PI) regulator parameters is given. These parameters are optimized by the FFA, FA, GA, PSO and ABC algorithms using the IAE, ISE, ITAE and ISTE performance criteria.

scholarly journals Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

2017 ◽  
Vol 1 (2) ◽  
pp. 82 ◽  
Author(s):  
Tirana Noor Fatyanosa ◽  
Andreas Nugroho Sihananto ◽  
Gusti Ahmad Fanshuri Alfarisy ◽  
M Shochibul Burhan ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Non Linear ◽  
Speed Up ◽  
Linear Problems

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

A Hybrid Optimization Algorithm for Single and Multi-Objective Optimization Problems

2017 ◽  
pp. 491-521 ◽  
Author(s):  
Rizk M. Rizk-Allah ◽  
Aboul Ella Hassanien
Keyword(s):  
Optimization Algorithm ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Hybrid Optimization ◽  
Multi Objective Optimization ◽  
Local Optima ◽  
Fruit Fly Optimization ◽  
Multi Objective ◽  
Hybrid Optimization Algorithm

This chapter presents a hybrid optimization algorithm namely FOA-FA for solving single and multi-objective optimization problems. The proposed algorithm integrates the benefits of the fruit fly optimization algorithm (FOA) and the firefly algorithm (FA) to avoid the entrapment in the local optima and the premature convergence of the population. FOA operates in the direction of seeking the optimum solution while the firefly algorithm (FA) has been used to accelerate the optimum seeking process and speed up the convergence performance to the global solution. Further, the multi-objective optimization problem is scalarized to a single objective problem by weighting method, where the proposed algorithm is implemented to derive the non-inferior solutions that are in contrast to the optimal solution. Finally, the proposed FOA-FA algorithm is tested on different benchmark problems whether single or multi-objective aspects and two engineering applications. The numerical comparisons reveal the robustness and effectiveness of the proposed algorithm.

A New Fuzzy Firefly Algorithm with Adaptive Parameters

2017 ◽  
Vol 16 (03) ◽  
pp. 1750017 ◽  
Author(s):  
Tahereh Hassanzadeh ◽  
Mohammad Reza Meybodi ◽  
Masoumeh Shahramirad
Keyword(s):  
Firefly Algorithm ◽  
Optimization Problems ◽  
Function Optimization ◽  
Local Searches ◽  
Adaptive Parameters ◽  
Otsu Method ◽  
Method Evidence ◽  
Global Optima ◽  
Global And Local

Firefly algorithm is a swarm based algorithm that can be used for solving optimization problems. This paper proposed an improved fuzzy adaptive firefly algorithm (FAFA). In the proposed FAFA, a fuzzy system is used to adapt Firefly Algorithm’s parameters in order to improve its ability in global and local searches. Also, we used different fireflies initializing intervals and different iteration numbers to show the algorithm capability to find global optima. Results focus on the two case study categories of function optimization (seven benchmark functions) and presented a novel optimal multilevel thresholding approach for histogram-based image segmentation by using proposed FAFA and Otsu method. Evidence indicates that the optimization results of proposed FAFA approach are so better than the standard FA.

scholarly journals Combined economic-emission load dispatch solution using firefly algorithm and fuzzy approach

2019 ◽  
Vol 16 (1) ◽  
pp. 127
Author(s):  
M. N. Abdullah ◽  
N. A. Abdullah ◽  
N. F. Aswan ◽  
S. A. Jumaat ◽  
N. H. Radzi ◽  
...  
Keyword(s):  
Power Generation ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Test System ◽  
Fuzzy Approach ◽  
Front Solution ◽  
Emission Level ◽  
Weighted Sum Method ◽  
Solution Convergence

This paper proposes Firefly Algorithm (FA) with fuzzy approach for solving combined economic-emission load dispatch (CEELD) problem in power generation. The main objective of the CEELD problem is to determine the optimal cost and emission level of power generation and fulfill the load demand and operational constraints. The CEELD problem used weighted sum method to combine two objective functions (cost and emission) into single objective. Then, the Fuzzy-based mechanism approach is integrated in FA to find the best compromise solution for both conflicting cost and emission level. The FA has been tested on IEEE 118-bus 14-unit test system for minimization both cost of power generation and emission level. The performance of FA for solving CEELD problem has been investigated in terms of optimal solution, convergence characteristics and Pareto front solution. From the comparison of the CEELD solutions, it found that FA can provide significant cost and emission reduction compared to other methods. Thus, it can be used as alternative approach for solving any optimization problems.

scholarly journals A Cooperative Harmony Search Algorithm for Function Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Gang Li ◽  
Qingzhong Wang
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Cooperative Behavior ◽  
Optimization Technique ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Function Optimization ◽  
Solution Vector ◽  
Original Algorithm ◽  
Harmony Memory

Harmony search algorithm (HS) is a new metaheuristic algorithm which is inspired by a process involving musical improvisation. HS is a stochastic optimization technique that is similar to genetic algorithms (GAs) and particle swarm optimizers (PSOs). It has been widely applied in order to solve many complex optimization problems, including continuous and discrete problems, such as structure design, and function optimization. A cooperative harmony search algorithm (CHS) is developed in this paper, with cooperative behavior being employed as a significant improvement to the performance of the original algorithm. Standard HS just uses one harmony memory and all the variables of the object function are improvised within the harmony memory, while the proposed algorithm CHS uses multiple harmony memories, so that each harmony memory can optimize different components of the solution vector. The CHS was then applied to function optimization problems. The results of the experiment show that CHS is capable of finding better solutions when compared to HS and a number of other algorithms, especially in high-dimensional problems.

scholarly journals The Improvement of Quantum Genetic Algorithm and Its Application on Function Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Huaixiao Wang ◽  
Jianyong Liu ◽  
Jun Zhi ◽  
Chengqun Fu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Evolutionary Process ◽  
Optimal Solution ◽  
Function Optimization ◽  
The Self ◽  
Improved Method ◽  
Simulation Test ◽  
Quantum Genetic Algorithm ◽  
Mutation Operation

To accelerate the evolutionary process and increase the probability to find the optimal solution, the following methods are proposed to improve the conventional quantum genetic algorithm: an improved method to determine the rotating angle, the self-adaptive rotating angle strategy, adding the quantum mutation operation and quantum disaster operation. The efficiency and accuracy to search the optimal solution of the algorithm are greatly improved. Simulation test shows that the improved quantum genetic algorithm is more effective than the conventional quantum genetic algorithm to solve some optimization problems.

scholarly journals Determination of BLDC Motor Capacity for Electric Car Drive

2021 ◽  
Vol 4 (1) ◽  
pp. 120
Author(s):  
Purnawan Purnawan ◽  
Casnan Casnan ◽  
Arief Kurniawan ◽  
Ananda Riski
Keyword(s):  
Vocational Education ◽  
Direct Current ◽  
Electric Motor ◽  
Drive System ◽  
Bldc Motor ◽  
Research Subjects ◽  
Electric Car ◽  
Automotive Technology ◽  
Level 1

The study's objectives were to: determine the type of Brushless Direct Current (BLDC) motor that is right for an electric car drive system with a capacity of one passenger, and Knowing the capacity of the BLDC motor used as an electric car drive system with a capacity of one passenger. This research uses Research and Development (R&D) level 1. The research subjects taken are students and lecturers of Vocational Education, Automotive Technology and Electrical Engineering, Ahmad Dahlan University, totalling eight students four lecturers. Ahmad Dahlan University " AL-QORNI " electric car is planned to use an electric motor type Brushless Direct Current (BLDC) with a capacity of 2000 watts which works with a voltage of 49 volts - 96 volts.

scholarly journals Torque Analysis of 2 KW BLDC (Brushless Direct Current) Motor with Speed Variations in Electric Cars E-Falco

2021 ◽  
Vol 2 (2) ◽  
pp. 76-86
Author(s):  
Bambang Darmono ◽  
Hadi Pranoto ◽  
Zainal Arifin
Keyword(s):  
Direct Current ◽  
Electric Motor ◽  
Peak Torque ◽  
Bldc Motor ◽  
Power Capacity ◽  
Motor Torque ◽  
Electric Cars ◽  
Electric Car ◽  
Torque Analysis ◽  
Brushless Direct Current Motor

The motor releases torque and power to drive an electric car by carrying the load from a start position until it travels at the desired speed. The KMLI E-Falco electric car uses a BLDC type electric motor with a power capacity of 2 kW. To find out the amount of torque of a 2 kW BLDC motor when driving with variations in speed, it can be done by manual calculations using the torque equation and doing a dynotest test. The dynotest results show that the motor torque at the speed: 1 km/h is 1 Nm, 10 km/h is 131 Nm, 13 km/h is 228 Nm, 20 km/h is 225 Nm, 30 km/h is 219 Nm, 40 km / h is 188 Nm, 50 km / hour is 145 Nm, 60 km / h is 113 Nm, and 70 km / h is 85 Nm. From the results of the dynotest, it shows that the peak torque occurs at a speed of 13 km / h at 228 Nm. Racing software installed in the controller can increase the motor torque by four times at a speed variation of 13-70 km/h based on the results of the dynotest above. Keywords: motor, BLDC, torque, speed, acceleration.

scholarly journals Structural Model Identification Using a Modified Electromagnetism-Like Mechanism Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4789
Author(s):  
Zhouquan Feng ◽  
Zhengtao Ye ◽  
Wenzan Wang ◽  
Yang Lin ◽  
Zhengqing Chen ◽  
...  
Keyword(s):  
Em Algorithm ◽  
Parameter Identification ◽  
Structural Model ◽  
Optimization Problems ◽  
Model Identification ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Model Parameters ◽  
Original Algorithm ◽  
Force Calculation

A modified electromagnetism-like mechanism (EM) algorithm is proposed to identify structural model parameters using modal data. EM is a heuristic algorithm, which utilizes an attraction–repulsion mechanism to move the sample points towards the optimal solution. In order to improve the performance of original algorithm, a new local search strategy, new charge and force calculation formulas, new particle movement and updating rules are proposed. The test results of benchmark functions show that the modified EM algorithm has better accuracy and faster convergence rate than the original EM algorithm and the particle swarm optimization (PSO) algorithm. In order to investigate the applicability of this approach in parameter identification of structural models, one numerical truss model and one experimental shear-building model are presented as illustrative examples. The identification results show that this approach can achieve remarkable parameter identification even in the case of large noise contamination and few measurements. The modified EM algorithm can also be used to solve other optimization problems.

The Application of Genetic Algorithm on Optimization Problem for Mayonnaise Compositions

2011 ◽  
Vol 480-481 ◽  
pp. 219-224
Author(s):  
Zhi Yang Luo ◽  
Hong Xia Zhao ◽  
Xin Yuan ◽  
Yuan Li
Keyword(s):  
Genetic Algorithm ◽  
Population Size ◽  
Mutation Rate ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Function Optimization ◽  
Solve Optimization Problem ◽  
Iteration Number

For some function optimization problems of non-linear, multi-model and multi-objective, they are difficult to solve by other optimization methods, however, genetic algorithm is easy to find good results, so a kind of optimization problem for mayonnaise compositions based on genetic algorithm is introduced. This termination condition is selected according to the iteration number of maximum generation, the optimal solution of last generation in the evolution is the final result with genetic algorithm to solve optimization problem. The population size is 20, crossover rate is 0.7, and mutation rate is 0.04. Via the evolution of 100 generations, the optimization solution is gotten, which has certain guiding significance for the production.

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