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Nature-Inspired Metaheuristic Search Strategies

Research on metahurestic for solving optimization problems has been appeared as a great subject of interest during last few years. It involves modeling the natural phenomena of various species foraging for the food and as well as theory of natural evolution of species. In this paper we discuss three major metahurestic approaches for optimization problems appeared in software testing such as path prioritization, automatic test case generation, test case selection etc. First we discuss Ant colony optimization as suggested by Grasse in 1959 and later modeled by Dorigo, Maniezzo, and Colorni in 1996 as one of the optimization algorithm for solving optimization problems. Second focus is on natural inspired phenomena of Honey bee colony suggested by V. Tereshko, based on Reaction–diffusion diffusion model of a honeybee colony’s foraging behavior. Finally we end up with genetic algorithm inspired by theory of evolution for solving optimization problems. The survey results the potential use of mentioned metahurestic approaches in software testing.


Mukesh Mann, Pradeep Tomar, Om Praksah Sangwan, Shivani Singh

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