Metaheuristic algorithms
Published in
2 min readJan 1, 2022
Problems that can be solved using practical methods and shortcuts than the regular procedure are called heuristics. Heuristic algorithms give optimized and faster solutions than other traditional algorithms.
Sometimes it can be optimal but it helps t complete short-term goals. Many algorithms are formed based on real-life solutions. For example, Swarm algorithms, Artificial neural networks, Genetic Algorithms, etc.
Even many problems are solved based on real-life solutions. For example, Knapsack problem, traveling salesman problem, etc.
We will discuss a few algorithms in this article.
- Particle Swarm Algorithm: In this Algorithm, the particle moves around the solution space. The particle is guided toward the best-known positions. It is a computational method that optimizes a problem by iteratively trying to find the best suitable solution.
- Artificial Neural Networks: It is used to solve recognition patterns such as speech, vision, etc. It is a machine learning model which is trained from data sets.
- Genetic Algorithms: Finding the optimized solution using biological methods such as mutation, crossover, etc. It is one of the evolutionary meta-heuristic algorithms.
- Cuckoo search algorithm: This algorithm is similar to the behavior of cuckoo. The cuckoo lays eggs in the nest built by other host birds. If the host birds find the egg does not belong to them then they throw the egg from the nest. Here each egg represents a solution, a cuckoo’s egg is a new solution. It helps to find a potentially better solution.
Hope you find this article useful. Thanks for reading.
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