Optimization Algorithms for Networks and Graphs, Second Edition,

2017 ◽  
Keyword(s):  
Optimization Algorithms ◽  
Networks And Graphs

Optimization Algorithms for Networks and Graphs.

1994 ◽  
Vol 63 (207) ◽  
pp. 431
Author(s):  
Collette Coullard ◽  
James R. Evans ◽  
Edward Minieka
Keyword(s):  
Optimization Algorithms ◽  
Networks And Graphs

Optimization Algorithms for Networks and Graphs

1980 ◽  
Vol 31 (2) ◽  
pp. 195
Author(s):  
David K. Smith ◽  
Edward Minieka
Keyword(s):  
Optimization Algorithms ◽  
Networks And Graphs

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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