Mohd Nadhir Ab Wahab (19369825), Amril Nazir (10342167), Ashraf Khalil (4591618) et al. · Figshare · 2024
<div><p>Path planning is a crucial element of mobile robotics applications, attracting considerable interest from academics. This paper presents a path-planning approach that utilises the Enhanced Firefly Algorithm (EFA), a new meta-heuristic technique. The Enhanced Firefly Algorithm (FA) differs from the ordinary FA by incorporating a linear reduction in the <i>α</i> parameter. This modification successfully resolves the constraints of the normal FA. The research involves experiments on three separate maps, using the regular FA and the suggested Enhanced FA in 20 different runs for each map. The evaluation criteria encompass the algorithms’ ability to move from the initial location to the final position without experiencing any collisions. The assessment of path quality relies on elements such as the distance of the path and the algorithms’ ability to converge and discover optimum solutions. The results demonstrate significant improvements made by the Enhanced FA, with a 10.270% increase in the shortest collision-free path for Map 1, a 0.371% increase for Map 2, and a 0.163% increase for Map 3, compared to the regular FA. This work highlights the effectiveness of the Enhanced Firefly Algorithm in optimising path planning for mobile robotics applications, providing potential improvements in navigation efficiency and collision avoidance.</p></div>
Cited by 0Open access
Kavitha Rathore · 2018
Optimization means to find the best solution for any situation under given constraints. In today’s era, the problems are huge and complex. Nature always finds a way to deal with such problems efficiently in an optimized way. The computational algorithms which are inspired by nature to find solutions for such problems are called Nature inspired optimization algorithms. There are various nature-inspired algorithms and Firefly Algorithm (FA) is one among them. FA is a bio-inspired population-based stochastic algorithm which imitates the behavior of fireflies shown when they attract other fireflies. FA is an algorithm with many parameters that affect the accuracy and the convergence speed. A number of variants and parameter tuning related papers are available in the literature. In this paper first an introduction of Optimization, specifically Nature inspired optimization has been provided. Then, a detailed discussion about FA has been given. It is followed by a brief literature survey in which the work has been compared in tabular form to provide the readers with a better understanding. Further, we intend to improve the accuracy of Firefly algorithm by tuning the parameters namely α and βmin. A range of values of the above parameters is tested by forming their combinations to find out the mutual effect of both these parameters. These values are tested on a test bed of nine benchmark functions. The result is a combination of optimized values of both the parameters. The results are quite clear and provide a pair of optimized values of both the parameters.
Cited by 2
Sankalap Arora, Ranjit Kaur · Journal of King Saud University - Computer and Information Sciences · 2018
Firefly algorithm is a powerfulalgorithm, however, it may show inferior convergence rate towards global optimum because the optimization process of the firefly algorithm depends upon on a random quantity that facilitates the fireflies to explore the search space at the cost of the exploitation. As a result, to improve the performance of firefly algorithm in terms of convergence rate even as its ability to exploit the solutions is preserved, firefly algorithm is altered using two different approaches in this research work. In the first approach, a local search strategy, i.e., classical unidimensional local search is employed in the firefly algorithm in order to improve its exploitation capability. In the second approach, a new solution search strategy, i.e., stochastic diffusion scout search is integrated with the searching phase of the firefly algorithm in order to increase the probability of inferior solutions to improve themselves. Both the approaches are evaluated on various benchmark problems having different complexities and their performances are compared with artificial bee colony and classical firefly algorithm. Results prove the capability of the proposed approaches in finding the global optimum in the search space and avoid the local optima at the same time.
Cited by 14Open access
Dedy Abdianto Nggego · Dewantara Journal of Technology · 2021
Penelitian ini bertujuan untuk mencari nilai parameter optimal algoritma kunang-kunang dilihat dari waktu eksekusi yang dibutuhkan dalam menemukan solusi optimal pada kasus n-queens problem. Metode yang digunakan yaitu metode eksperimen dengan melakukan 10 kali percobaan pada setiap parameter dengan populasi 15 dan 50 kunang-kunang pada dimensi 10x10. Parameter optimal yang diperoleh yaitu α = 2.0, β = 1.0, dan γ = 0.2 dengan rata-rata waktu eksekusi 0.63776 detik pada populasi 15 kunang-kunang dan rata-rata waktu eksekusi 0.98321 detik pada populasi 50 kunang-kunang.
Cited by 0Open access
Mahapatra, Gautam, Soumya Banerjee · 2014
Cited by 1Open access
Akash Sharma, Smriti Sehgal · 2016
Image segmentation is an important step in the domain of image processing in which we segment the image into several parts which carry certain type of information for the user. Image segmentation is very difficult step in the processing of the image which aims at extracting the information from image. Clustering is used to segment the image. Clustering algorithms are part of data mining algorithm that groups the data into various number of given clusters. All the data points in one cluster have similar properties based on which they are clustered i.e. each cluster has minimum difference between its points and maximum difference from other cluster data points. The proposed algorithm uses k-mean algorithm and firefly to cluster image pixels into k cluster for segmentation. Since k-mean clustering algorithm is gets trapped in local optima it is optimized using firefly algorithm. Swarm intelligence based algorithms forms the basis of the firefly algorithm which has several application and used to solve optimization problems. Firefly algorithm has been applied in many research and optimization areas. Firefly algorithm and its hybridized version have been used to solve various problems successfully. To apply firefly algorithm to wide areas of problem the firefly algorithm must be modified or integrated with other algorithms. Presently metaheuristic nature of algorithm plays an important role and current optimization algorithm include this nature and are very efficient in solving NP-hard problems.
Cited by 21
Hu Peng, Shunxu Peng · International Journal of Innovative Computing and Applications · 2019
Firefly algorithm (FA), as a relatively recent emerged swarm intelligence algorithm, is powerful and popular for the complex real parameter global optimisation. However, the premature convergence has greatly affected the performance of original FA. To overcome this problem, we proposed a Gaussian bare-bones FA, named GBFA, in which each firefly moves to a Gaussian bare-bones method generated learning object rather than its better neighbours. The experiments are conducted on a set of widely used benchmark functions. Experimental results and comparison with the state-of-the-art FA variants have proved that the proposed algorithm is promising.
Cited by 9
Masaki Takeuchi, Haruna Matsushita, Yoko Uwate et al. · 2016
The community of human-beings, such as company and laboratory, is created as core on the leader of the community. In this study, we propose Firefly Algorithm existing leader fireflies. In our proposed FA, leader fireflies attract other fireflies within certain distance. We compare our proposed FA to the conventional FA with benchmark functions of CEC 2013. Numerical experiments indicate our proposed FA is more efficient algorithm than the conventional FA.
Cited by 1
Amanpreet Kaur, Niyati Aggrawal, Aastha Maheshwari et al. · 2023
Node localization is an important requirement in the area of wireless sensor networks. Numerous proposals have been put forward in this area, but still all these proposals are in babyhood. The main limitation of node localization algorithm is accuracy. In order to achieve this objective, an improved node localization algorithm is proposed that applies one of the popular algorithms that is Firefly Optimization Algorithm. Simulation Results prove this fact.
Cited by 0
Hu Peng, Shunxu Peng · International Journal of Innovative Computing and Applications · 2019
Firefly algorithm (FA), as a relatively recent emerged swarm intelligence algorithm, is powerful and popular for the complex real parameter global optimisation. However, the premature convergence has greatly affected the performance of original FA. To overcome this problem, we proposed a Gaussian bare-bones FA, named GBFA, in which each firefly moves to a Gaussian bare-bones method generated learning object rather than its better neighbours. The experiments are conducted on a set of widely used benchmark functions. Experimental results and comparison with the state-of-the-art FA variants have proved that the proposed algorithm is promising.
Cited by 2
Iosr Journals, Adil Hashmi, Nishant Goel et al. · Figshare · 2014
Cited by 0Open access
LIU Xiaonan, AN Jiale, HE Ming, SONG Huichao · SHILAP Revista de lepidopterología · 2023
In order to improve the search performance of quantum firefly algorithm(QFA) and solve the problem that it is easy to fall into local optimality when facing some problems,an improved QFA with chaotic map,neighborhood search and adaptive random disturbance is proposed,named chaos adaptive quantum firefly algorithm(CAQFA).In this algorithm,chaotic map is applied to the initialization stage of the population to improve the quality of the initial population.In the update stage,the neighborhood search is carried out for the optimal individual of the current population to enhance the ability of the algorithm to jump out of the local optimization.The introduction of adaptive random disturbance to other individuals increases the randomness of the algorithm and achieves a balance between the exploration and development of search space,so as to improve the performance of the algorithm.Eighteen different types of benchmark functions are selected to test the performance of the algorithm.The test results show that CAQFA has better search ability,stability and strong competitiveness compared with firefly algorithm(FA),QFA and quantum particle swarm optimization(QPSO).
Cited by 0Open access
Iztok Fister, Xin‐She Yang, Janez Brest et al. · Elsevier eBooks · 2013
Cited by 48
Shashank Pandey, Sanjay K. Dwivedi, Vipul Singh et al. · Communications in computer and information science · 2024
Cited by 0
Muhammad Sufyan (6335450), Nasrudin Abd Rahim (6335453), ChiaKwang Tan (6335456) et al. · Figshare · 2019
Cited by 0Open access
Xin‐She Yang · Studies in computational intelligence · 2013
Cited by 231
Zahra Honarpisheh, Karim Faez · 2012
Biometric technology is an efficient personal authentication and identification technique. As one of the main-stream branches, dorsal hand vein recognition has been recently attracted the attention of researchers. It is more preferable than the other types of biometrics becuse it’s impossible to steal or counterfeit the patterns and the pattern of the vessels of back of the hand is fixed and unique with repeatable biometric features. Also, the recent researches have been obtained no certain recognition rate yet becuse of the noises in the imaging patterns, and impossibility of Dimension reducing because of the non-complexity of the models, and proof of correctness of identification is required. Therefore, in this paper, first, the images of blood vessels on back of the hands of people is analysed, and after pre-processing of images and feature extraction (in the intersection between the vessels) we began to identify people using firefly clustering algorithms. This identification is done based on the distance patterns between crossing vessels
Cited by 0
Iosr Journals, Nishant Goel Adil Hashmi · Figshare · 2014
Cited by 0Open access
Nur Farahlina Johari, Azlan Mohd Zain, Mustaffa H. Noorfa et al. · Applied Mechanics and Materials · 2013
This paper reviews the applications of Firefly Algorithm (FA) in various domain of optimization problem. Optimization is a process of determining the best solution to make something as functional and effective as possible by minimizing or maximizing the parameters involved in the problems. Several categories of optimization problem such as discrete, chaotic, multi-objective and many more are addressed by inspiring the behavior of fireflies as mentioned in the literatures. Literatures found that FA was mostly applied by researchers to solve the optimization problems in Computer Science and Engineering domain. Some of them are enhanced or hybridized with other techniques to discover better performance. In addition, literatures found that most of the cases that used FA technique have outperformed compare to other metaheuristic algorithms.
Cited by 184
Najid Zaid · University of Malaya Students Repository · 2015
The changes of the structure of the power system, size, and complexity have increased the important of LFC. For this reason, this research studies the controller aspect of LFC by using the Fractional Order Integral-Derivative (FOID) controller or IλDμ Controller. In order to obtain the best controller parameter values for LFC, Firefly Algorithm (FA) and Chaos Firefly Algorithm (CFA) are used. This project analyzes the performance of the algorithms based LFC in three power system area. The primary objectives of LFC are to maintain frequency and minimize power interchanges with neighboring control areas. These objectives are met by measuring a control error signal called the area control error (ACE), which calculates the real power difference between generation and load. In this project, the integral of time multiply squared error (ITSE) as the objective function is used on the ACE. The model of the system is designed using Matlab software to carry out simulation studies. Step input load deviation is injected to the system at designated location and the optimization of ramp rate, speed regulation and the IλDμ parameters are carried out. The frequency deviation and tie line power changes characteristics are analyzed to observe the system performance. The maximum overshoot, settling time and ITSE value is also recorded and compared. Result shows that the CFA is the best optimization method due to its robustness and consistency. The project can be further improved by tuning the controller using other optimization techniques and including other physical constraints.
Cited by 0