Читать книгу Nature-Inspired Algorithms and Applications - Группа авторов - Страница 16

Nature-inspired computing is an emerging technique which introduces a new discipline by observing the phenomena happening in nature used to give solution to the difficult problem in the surroundings. NIC had has a best presentation for attracting responsiveness in a substantial way. NIC has developed new innovative study with new branch, namely, swarm intelligence (SI), evolutionary computation (EC), quantum computing, neural networks, fractal geometry, artificial life and artificial immune systems (AIS), and DNA computing. It also used in the field of biology, physics, engineering, management, and economics. Some of the examples of nature-inspired algorithms are like evolutionary computing (EC), artificial neural networks (ANN), fuzzy systems (FS), and SI. Nature-inspired computing is also referred as natural-inspired computation which is defined as an expression to include three methods of classes. They are as follows:

1 For the improvement of innovative problem solving, it takes technique which is inspired by nature.

2 Based on utilization of processer for the manufacture of phenomena by nature.

3 Based on the molecules of natural material that hire for computation.

To solve optimization problem of real world is challenging and more application need to deal with problem of NP-hard. Even though optimization tool is used to solve this problem, there is no assurance for reaching the optimal solution. There is no efficiency of algorithm for NP problems. As a conclusion for NP problems, technique of optimization is used to solve by experimental method. Some of new algorithm like particle swarm optimization (PSO), cuckoo search (CS), and firefly algorithm (FA) are developed to face this challenging problem of optimization. These new algorithm are developed to gain popularity for the performance with high efficiency. In recent survey, there are about more than 40 new different algorithms. This classification of these different algorithms is risky as it should be based on criteria with no guideline [1].

In growth of new algorithm which is inspiration of nature, some algorithms like SI algorithms and bio-inspired algorithms are developed. Metaheuristic algorithm like nature-inspired algorithm is based on physical, biological, chemical, and SI. These algorithms are called as physical-based, biological-based, chemical-based, and SI-based algorithms depending on the inspiration of nature. As the entire algorithms are not efficient, some algorithms became more common for solving all problem of real world.