Flow Direction Algorithm (FDA): a Novel Optimizer Approach for Solving Optimization Problems

2021 ◽  
pp. 107224
Author(s):  
Hojat Karami ◽  
Mahdi Valikhan Anaraki ◽  
Saeed Farzin ◽  
Seyedali mirjalili
Keyword(s):  
Optimization Problems ◽  
Flow Direction ◽  
Flow Direction Algorithm

scholarly journals Terrain Analysis for Locating Erosion Channels: Assessing LiDAR Data and Flow Direction Algorithm

10.5772/51526 ◽  
2012 ◽  
Author(s):  
Adam Pike ◽  
Tom Mueller ◽  
Eduardo Rienzi ◽  
Surendran Neelakantan ◽  
Blazan Mijatovic ◽  
...  
Keyword(s):  
Flow Direction ◽  
Lidar Data ◽  
Terrain Analysis ◽  
Flow Direction Algorithm

An adaptive approach to selecting a flow‐partition exponent for a multiple‐flow‐direction algorithm

2007 ◽  
Vol 21 (4) ◽  
pp. 443-458 ◽  
Author(s):  
C. Qin ◽  
A.‐X. Zhu ◽  
T. Pei ◽  
B. Li ◽  
C. Zhou ◽  
...  
Keyword(s):  
Flow Direction ◽  
Adaptive Approach ◽  
Flow Direction Algorithm

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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