Optimization algorithms are the highly efficient algorithms which focus on finding solutions to highly complex optimization problems like travelling salesman problems, scheduling problems, profit maximization etc. Nature-inspired algorithms are a set of novel problem-solving methodologies and approaches derived from natural processes. Some of the popular examples of nature-inspired optimization algorithms include: genetic algorithm, particle swarm optimization, cukcoo search algorithm, ant colony optimization and so on.
Why do we need nature-inspired optimization algorithms?
These algorithms are highly efficient in finding optimized solutions to multi-dimensional and multi-modal problems. The conventional optimization approach in calculus finding the first order derivative of the objective function and equating it to zero to get the critical points. These critical points then give the maximum or minimum value as per the objective function. The calculation of gradients or even higher order derivatives needs more computing resources and is more error-prone than other methods.
Further, you can imagine how complex it is to find solution to a minimization/ maximization problem with 20 or even more number of variables. However, by using these nature inspired algorithms, the problem can be solved with less computational efforts and time complexity. These algorithms use a stochastic approach to find the best solution in the large search space of the problem.
Applications of nature-inspired optimization algorithms:
- Digital filter designing
- Image processing
- Digital integrator and differentiator designing
- Artificial neural networks