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Nature-Inspired Algorithms
| Content Provider | Scilit |
|---|---|
| Author | Vasuki, A. |
| Copyright Year | 2020 |
| Description | Nature-inspired optimization algorithms are metaheuristic algorithms that are developed from the principles of biological evolution, swarm behavior, and physical and chemical processes [1]. Nature-inspired optimization algorithms are bioinspired computational intelligence techniques since they incorporate intelligence in the algorithms. The research into these algorithms has grown by leaps and bounds in the last two decades. The first breakthrough occurred in the 1960s with the pioneering development of the evolutionary genetic algorithm (GA) by John Holland and his colleagues at the University of Michigan. Since then several evolutionary algorithms have been proposed, including many variants and hybrids of GA. Evolutionary algorithms are based on biological evolution, and GA is one of the classical examples under this category. GP is another popular evolutionary algorithm that is similar to GA and has a population of evolving programs and uses the same operators as GA. Swarm intelligence algorithms are another category of bioinspired algorithms that are inspired by the behavior of swarms in nature such as bird flocking, ant trailing, fish schooling, elephant herding, and so on. Using populations of search agents combined with heuristics has a profound effect on the solutions to complex engineering design problems. The nature-inspired algorithms are novel in attaining effective solutions easily with the least computational resources. The sharing of information and social interaction among members of their own species as well as with the environment by biological agents such as ants, bees, crows, bat, cuckoo, etc. has led to the rise of collective intelligence. They adapt themselves to the environment and make the optimum use of resources available, whether it is sharing of food or any task to be completed, with cooperation amongst their group. Hence any algorithm modeled on their behavior can find solutions to complex problems easily. Book Name: Nature-Inspired Optimization Algorithms |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2019-0-98738-8&isbn=9780429289071&doi=10.1201/9780429289071-3&format=pdf |
| Ending Page | 46 |
| Page Count | 18 |
| Starting Page | 29 |
| DOI | 10.1201/9780429289071-3 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2020-05-31 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Nature-inspired Optimization Algorithms Information Systems Behavior Optimization Intelligence Evolutionary Nature Inspired Inspired Algorithms Nature |
| Content Type | Text |
| Resource Type | Chapter |
