A Review on Bio-Inspired Migration Optimization Techniques

A Review on Bio-Inspired Migration Optimization Techniques

Jyotsna Verma (Department of Computer Science, Central University of Rajasthan, Ajmer, India) and Nishtha Kesswani (Department of Computer Science, Central University of Rajasthan, Ajmer, India)
DOI: 10.4018/IJBDCN.2015010103
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Abstract

Nature inspired computing techniques has become a very popular topic in recent years. Number of applications in computer networks, robotics, biology, combinatorial optimization, etc. can be seen in literatures which are based on the bio-inspired techniques. Nature inspired techniques are proven to solve complex optimization problems irrespective of their problem size. This review summarizes various nature inspired migration algorithms and comparison between them, based on the automated tools, evolutionary techniques and applications.
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1. Introduction

Bio inspired computing is a technique inspired by nature. It is basically related to artificial intelligence and also called as nature inspired techniques, where the solutions of the problems are inspired from social behavior of natural systems like bees, ants, flock of birds, a flock of fishes, etc. There are various nature inspired algorithms, which solves various complex optimization functions in many science and engineering domains. Optimization selects the best alternatives from the given set of options. It finds the smallest or largest possible values for a given function. The function which is to be optimized could be linear, non-linear and fractional. Optimization can be applied in various application domains. So, there is a requirement of developing efficient, robust and flexible computational algorithms which solves problems irrespective of problem size. The conventional technique to solve optimization problems required great computational efforts and failed to realistically represent the problem when problems size increases. The nature inspired algorithms can be applied to a wider set of problem as they used the stochastic search strategy and do not require continuity or explicit definitions of functions. It is designed to solve various single or multi-objective problems and can tackle NP hard and the NP complete problems. The process executes with the initial set of variables to obtain objective function or global optimal solutions.

The paper is organized as follows: Section 2 highlights the related works. Section 3 presents the review of nature inspired migration algorithms on the basis of their control parameters, evolutionary techniques and the application areas. Conclusion and future work are discussed in Section 4.

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