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About Ant Colony Opt
The document discusses accelerating ant colony optimization by using local search. It introduces ant colony optimization as a popular metaheuristic technique inspired by the foraging behavior of ants. The basic ant colony optimization algorithm and its advantages/disadvantages are described. The paper proposes modifying the ACO algorithm by incorporating SBX crossover and polynomial mutation to improve its performance on benchmark optimization problems under both static and dynamic environments. The performance of the modified SBX-ACO algorithm is evaluated and compared to other existing evolutionary algorithms using various benchmark test functions.
Detailed Information
| Author: | ['Ravi Teja'] |
|---|---|
| Publication Year: | 2018 |
| Pages: | 51 |
| Language: | English |
| Format: | |
| Price: | FREE |
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