Genetic Algorithm and Firefly Algorithm Hybrid Schemes for Cultivation Processes Modelling

2014 ◽  
pp. 196-211 ◽  
Author(s):  
Olympia Roeva
Keyword(s):  
Genetic Algorithm ◽  
Firefly Algorithm ◽  
Hybrid Schemes

Cuckoo Search and Genetic Algorithm Hybrid Schemes for Optimization Problems

2016 ◽  
Vol 10 (3) ◽  
pp. 1185-1192 ◽  
Author(s):  
Mohamed Abdel-Baset ◽  
Ibrahim Hezam
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Cuckoo Search ◽  
Hybrid Schemes

Hybridizing Bees Algorithm with Firefly Algorithm for Solving Complex Continuous Functions

2020 ◽  
Vol 11 (2) ◽  
pp. 27-55
Author(s):  
Mohamed Amine Nemmich ◽  
Fatima Debbat ◽  
Mohamed Slimane
Keyword(s):  
Firefly Algorithm ◽  
Continuous Optimization ◽  
Population Based ◽  
Continuous Functions ◽  
Complex Problem ◽  
Bees Algorithm ◽  
The Novel ◽  
Hybrid Schemes ◽  
Problem Resolution ◽  
Natural Behaviour

In this article, two hybrid schemes using the Bees Algorithm (BA) and the Firefly Algorithm (FA) are presented for numerical complex problem resolution. The BA is a recent population-based optimization algorithm, which tries to imitate the natural behaviour of honey bees foraging for food. The FA is a swarm intelligence technique based upon the communication behaviour and the idealized flashing features of tropical fireflies. The first approach, called the Hybrid Bee Firefly Algorithm (HBAFA), centres on improvements to the BA with FA during the local search thus increasing exploitation in each research zone. The second one, namely the Hybrid Firefly Bee Algorithm (HFBA), uses FA in the initialization step for a best exploration and detection of promising areas in research space. The performance of the novel hybrid algorithms was investigated on a set of various benchmarks and compared with standard BA, and other methods found in the literature. The results show that the proposed algorithms perform better than the Standard BA, and confirm their effectiveness in solving continuous optimization functions.

scholarly journals A Hybrid Maximum Power Point Tracking Approach for Photovoltaic Systems under Partial Shading Conditions Using a Modified Genetic Algorithm and the Firefly Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yu-Pei Huang ◽  
Xiang Chen ◽  
Cheng-En Ye
Keyword(s):  
Genetic Algorithm ◽  
Firefly Algorithm ◽  
High Accuracy ◽  
Photovoltaic Systems ◽  
Tracking Accuracy ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

This paper proposes a modified maximum power point tracking (MPPT) algorithm for photovoltaic systems under rapidly changing partial shading conditions (PSCs). The proposed algorithm integrates a genetic algorithm (GA) and the firefly algorithm (FA) and further improves its calculation process via a differential evolution (DE) algorithm. The conventional GA is not advisable for MPPT because of its complicated calculations and low accuracy under PSCs. In this study, we simplified the GA calculations with the integration of the DE mutation process and FA attractive process. Results from both the simulation and evaluation verify that the proposed algorithm provides rapid response time and high accuracy due to the simplified processing. For instance, evaluation results demonstrate that when compared to the conventional GA, the execution time and tracking accuracy of the proposed algorithm can be, respectively, improved around 69.4% and 4.16%. In addition, in comparison to FA, the tracking speed and tracking accuracy of the proposed algorithm can be improved around 42.9% and 1.85%, respectively. Consequently, the major improvement of the proposed method when evaluated against the conventional GA and FA is tracking speed. Moreover, this research provides a framework to integrate multiple nature-inspired algorithms for MPPT. Furthermore, the proposed method is adaptable to different types of solar panels and different system formats with specifically designed equations, the advantages of which are rapid tracking speed with high accuracy under PSCs.

scholarly journals Implementation of the Firefly Algorithm in the Case of N-Queens Problem

2020 ◽  
Vol 4 (2) ◽  
pp. 219-227
Author(s):  
Dedy Abdianto Nggego ◽  
Arief Setyanto ◽  
Sukoco
Keyword(s):  
Genetic Algorithm ◽  
Positive Integer ◽  
Experimental Method ◽  
Firefly Algorithm ◽  
Optimal Solution ◽  
Optimal Solutions ◽  
Number Of Iterations

N-Queen problem is a form of puzzle game that uses chess rules for the queen on the standard chessboard with modified size. The challenge of the n-queen problem is finding the N ( N is positive integer) queens position on the chessboard, so that no queen can attack another queen on the board in a single move. Implementation of firefly algorithm in n-queens problem in this study aims to find n-queen problem solutions and count the number of iterations to achieve the optimal solution of each queen which will then be compared with the results of Sarkar and Nag's research (2017). This study uses an experimental method with a number of N between 10 to 20 and uses a population of 15 and 1000 firefly. The results showed that the firefly algorithm is able to find all the optimal solutions for the queen's position on a chessboard with dimensions 10 to 20 in a population of 1000 firefly. The firefly algorithm can find the optimal solution fewer iterations compared to the genetic algorithm. According to the experiment, firefly algorithm shows better performance in finding the optimal solution compared to genetic algorithm.

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