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 A New Evolutionary Algorithm: Learner Performance Based Behavior Algorithm

2020 ◽  
Author(s):  
Chnoor M. Rahman ◽  
Tarik A. Rashid
Keyword(s):  
High School ◽  
Evolutionary Algorithm ◽  
Real World ◽  
Optimization Problems ◽  
Initial Population ◽  
Test Functions ◽  
Great Ability ◽  
Learner Performance ◽  
Global Optima

A novel evolutionary algorithm called learner performance based behavior algorithm (LPB) is proposed in this article. The basic inspiration of LPB originates from the process of accepting graduated learners from high school in different departments at university. In addition, the changes those learners should do in their studying behaviors to improve their study level at university. The most important stages of optimization; exploitation and exploration are outlined by designing the process of accepting graduated learners from high school to university and the procedure of improving the learner’s studying behavior at university to improve the level of their study. To show the accuracy of the proposed algorithm, it is evaluated against a number of test functions, such as traditional benchmark functions, CEC-C06 2019 test functions, and a real-world case study problem. The results of the proposed algorithm are then compared to the DA, GA, and PSO. The proposed algorithm produced superior results in most of the cases and comparative in some others. It is proved that the algorithm has a great ability to deal with the large optimization problems comparing to the DA, GA, and PSO. The overall results proved the ability of LPB in improving the initial population and converging towards the global optima. Moreover, the results of the proposed work are proved statistically. <br>

scholarly journals A New Evolutionary Algorithm: Learner Performance Based Behavior Algorithm

2020 ◽  
Author(s):  
Chnoor M. Rahman ◽  
Tarik A. Rashid
Keyword(s):  
High School ◽  
Evolutionary Algorithm ◽  
Real World ◽  
Optimization Problems ◽  
Initial Population ◽  
Test Functions ◽  
Great Ability ◽  
Learner Performance ◽  
Global Optima

A novel evolutionary algorithm called learner performance based behavior algorithm (LPB) is proposed in this article. The basic inspiration of LPB originates from the process of accepting graduated learners from high school in different departments at university. In addition, the changes those learners should do in their studying behaviors to improve their study level at university. The most important stages of optimization; exploitation and exploration are outlined by designing the process of accepting graduated learners from high school to university and the procedure of improving the learner’s studying behavior at university to improve the level of their study. To show the accuracy of the proposed algorithm, it is evaluated against a number of test functions, such as traditional benchmark functions, CEC-C06 2019 test functions, and a real-world case study problem. The results of the proposed algorithm are then compared to the DA, GA, and PSO. The proposed algorithm produced superior results in most of the cases and comparative in some others. It is proved that the algorithm has a great ability to deal with the large optimization problems comparing to the DA, GA, and PSO. The overall results proved the ability of LPB in improving the initial population and converging towards the global optima. Moreover, the results of the proposed work are proved statistically. <br>

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.

A microbial treatment population dynamics optimization approach

2021 ◽  
pp. 1-15
Author(s):  
Jinding Gao
Keyword(s):  
Population Dynamics ◽  
Information Exchange ◽  
Information Diffusion ◽  
Optimization Problems ◽  
Microbial Control ◽  
Function Optimization ◽  
Optimization Approach ◽  
Dynamics Model ◽  
Population Dynamics Model ◽  
Number Of Individuals

In order to solve some function optimization problems, Population Dynamics Optimization Algorithm under Microbial Control in Contaminated Environment (PDO-MCCE) is proposed by adopting a population dynamics model with microbial treatment in a polluted environment. In this algorithm, individuals are automatically divided into normal populations and mutant populations. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, it solves the problem of artificially determining the number of individuals. There are 7 operators in the algorithm, they realize the information exchange between individuals the information exchange within and between populations, the information diffusion of strong individuals and the transmission of environmental information are realized to individuals, the number of individuals are increased or decreased to ensure that the algorithm has global convergence. The periodic increase of the number of individuals in the mutant population can greatly increase the probability of the search jumping out of the local optimal solution trap. In the iterative calculation, the algorithm only deals with 3/500∼1/10 of the number of individual features at a time, the time complexity is reduced greatly. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. The test case shows that the PDO-MCCE algorithm has better performance and is suitable for solving some optimization problems with higher dimensions.

scholarly journals Path Planning for Autonomous Ships: A Hybrid Approach Based on Improved APF and Modified VO Methods

2021 ◽  
Vol 9 (7) ◽  
pp. 761
Author(s):  
Liang Zhang ◽  
Junmin Mou ◽  
Pengfei Chen ◽  
Mengxia Li
Keyword(s):  
Path Planning ◽  
Local Minimum ◽  
Hybrid Approach ◽  
Artificial Potential Field ◽  
Whole Process ◽  
International Regulations ◽  
Local Path ◽  
Velocity Obstacle ◽  
Global And Local

In this research, a hybrid approach for path planning of autonomous ships that generates both global and local paths, respectively, is proposed. The global path is obtained via an improved artificial potential field (APF) method, which makes up for the shortcoming that the typical APF method easily falls into a local minimum. A modified velocity obstacle (VO) method that incorporates the closest point of approach (CPA) model and the International Regulations for Preventing Collisions at Sea (COLREGS), based on the typical VO method, can be used to get the local path. The contribution of this research is two-fold: (1) improvement of the typical APF and VO methods, making up for previous shortcomings, and integrated COLREGS rules and good seamanship, making the paths obtained more in line with navigation practice; (2) the research included global and local path planning, considering both the safety and maneuverability of the ship in the process of avoiding collision, and studied the whole process of avoiding collision in a relatively entirely way. A case study was then conducted to test the proposed approach in different situations. The results indicate that the proposed approach can find both global and local paths to avoid the target ship.

Improved Particle Swarm Optimization Algorithm and its Application Based on the Aggregation Degree

2013 ◽  
Vol 427-429 ◽  
pp. 1934-1938
Author(s):  
Zhong Rong Zhang ◽  
Jin Peng Liu ◽  
Ke De Fei ◽  
Zhao Shan Niu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Diversity ◽  
Function Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The aim is to improve the convergence of the algorithm, and increase the population diversity. Adaptively particles of groups fallen into local optimum is adjusted in order to realize global optimal. by judging groups spatial location of concentration and fitness variance. At the same time, the global factors are adjusted dynamically with the action of the current particle fitness. Four typical function optimization problems are drawn into simulation experiment. The results show that the improved particle swarm optimization algorithm is convergent, robust and accurate.

scholarly journals How emissions from cruise ships in the port of Naples changed in the COVID-19 lock down period

2021 ◽  
pp. 147509022110284
Author(s):  
Luigia Mocerino ◽  
Franco Quaranta
Keyword(s):  
Local Scale ◽  
The Other ◽  
Cruise Ships ◽  
Global Pandemic ◽  
Transportation Sector ◽  
Reduction Of Emissions ◽  
The One ◽  
Global And Local

The scope of this work is to try to quantify the reduction of emissions due to COVID-19; an analysis covering the entire port of Naples will be presented. The explosion of the global pandemic from SARS-CoV-2 led to the adoption of local and global countermeasures aimed at containing contagions. The transportation sector, and in particular the passenger moving sector, was deeply affected; this almost total block of movements between regions and countries if, on the one hand, seriously slowed the economy, on the other, it drastically reduced the emissions on a global and local scale. In this work, the case study of the cruise ships berthed at the Maritime Station (Stazione Marittima) in the port of Naples is examined. The traffic of cruise ships during the lockdown and in the immediately following months was analysed and compared first with respect to the calendars scheduled for the same period and then with respect to the same months of 2019. The reduction in number of cruise ships and passengers were analysed and compared to the previous trends. The vessels collected, for 2019 and 2020 (both those that arrived and those that suffered the effects of the movement block) were subsequently characterized in terms of power and speed. Finally, an estimate of the emissions of NOX, SOX, CO2 produced and saved was carried out. The 2020 results will be compared with the hypothetical emissions that would have occurred in the absence of the lockdown and with those of the same period of the previous year.

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