Behavioral Study of Drosophila Fruit Fly and Its Modeling for Soft Computing Application

Behavioral Study of Drosophila Fruit Fly and Its Modeling for Soft Computing Application

Tapan Kumar Singh (National Institute of Technology, India) and Kedar Nath Das (National Institute of Technology Silchar, India)
DOI: 10.4018/978-1-4666-9885-7.ch003
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Abstract

Most of the problems arise in real-life situation are complex natured. The level of the complexity increases due to the presence of highly non-linear constraints and increased number of decision variables. Finding the global solution for such complex problems is a greater challenge to the researchers. Fortunately, most of the time, bio-inspired techniques at least provide some near optimal solution, where the traditional methods become even completely handicapped. In this chapter, the behavioral study of a fly namely ‘Drosophila' has been presented. It is worth noting that, Drosophila uses it optimized behavior, particularly, when searches its food in the nature. Its behavior is modeled in to optimization and software is designed called Drosophila Food Search Optimization (DFO).The performance, DFO has been used to solve a wide range of both unconstrained and constrained benchmark function along with some of the real life problems. It is observed from the numerical results and analysis that DFO outperform the state of the art evolutionary techniques with faster convergence rate.
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Introduction

Optimization is an art of selecting best alternatives amongst the set of individuals. Henceforth, the collective intelligent behavior of insect groups such as flocks of birds, colonies of ants, school of fish’s and swarms of bees have attracted the attention of researchers. The aggregative behavior of insects is known as swarm behavior and the collective phenomenon of collective model swarms have been studied by Entomologists, while engineers have applied these models for solving complex real-world problems, as because, it has some advantages like scalability, fault tolerance, adaption, speed, modularity, autonomy and parallelism (Kassabalidis, EI-Sharkawi, Marks, Arabshai, & Gray 2001) etc.

Now a day, a number of complex real life problems arising in the field of Engineering, Science, Industry and Finance which were turn out to be nonlinear optimization problems associates with many constraints. For this reason, the need for designing efficient, reliable and robust optimization techniques is required. According, to No Free Launch Theorem (Wolpert, & Macready, 1995), no single algorithm exists in order to solve all optimization problems and therefore, a number of optimization techniques are available in literature to deal with non-linear problems both constraint and unconstraint in nature.

The mathematical form of the general nonlinear optimization problem is

Optimize

subject to

The set is called the feasible region and is the objective function, are inequality constraints, are equality constraints and, are the lower and upper bounds of the decision variables. A point is called local minima of if where is an neighborhood of and is called the global minima of if for all .

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