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Ant Colony Optimization
| Content Provider | Scilit |
|---|---|
| Author | Vasuki, A. |
| Copyright Year | 2020 |
| Description | Swarm intelligence has been the inspiration behind the development of a class of nature-inspired optimization algorithms that are different from the traditional methods. These optimization techniques are unconventional and have been found to be successful in solving a diverse range of real-life problems. The traditional optimization algorithms are suitable for continuous functions that require the computation of derivatives. The nature-inspired optimization algorithms can be applied for continuous and discrete as well as mixed-variable problems, and they do not require the computation of derivatives. They are mostly search algorithms that use a population of agents to search in parallel for the optimum, thus saving time. Metaheuristics is an important component of such algorithms since approximations greatly simplify the process in arriving at the optimum solution for the problem. Book Name: Nature-Inspired Optimization Algorithms |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2019-0-98738-8&isbn=9780429289071&doi=10.1201/9780429289071-8&format=pdf |
| DOI | 10.1201/9780429289071-8 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2020-05-31 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Nature-inspired Optimization Algorithms Cybernetical Science Functions Problems Diverse Nature Inspired Inspired Optimization Algorithms |
| Content Type | Text |
| Resource Type | Chapter |
