ABVIndian Institute of Information Technology and Management

Gwalior, India

ABVIndian Institute of Information Technology and Management

Gwalior, India
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Rana P.S.,ABVIndian Institute of Information Technology and Management | Sharma K.,Government of Rajasthan | Bhattacharya M.,ABVIndian Institute of Information Technology and Management | Shukla A.,ABVIndian Institute of Information Technology and Management | Sharma H.,ABVIndian Institute of Information Technology and Management
Advances in Intelligent Systems and Computing | Year: 2014

Differential evolution (DE) is a vector population-based stochastic search optimization algorithm.DEconverges faster, finds the global optimum independent to initial parameters, and uses few control parameters. The exploration and exploitation are the two important diversity characteristics of population-based stochastic search optimization algorithms. Exploration and exploitation are compliment to each other, i.e., a better exploration results in worse exploitation and vice versa. The objective of an efficient algorithm is to maintain the proper balance between exploration and exploitation. This paper focuses on a comparative study based on diversity measures for DE and its prominent variants, namely JADE, jDE, OBDE, and SaDE. © Springer India 2014.


Ghai B.,ABVIndian Institute of Information Technology and Management | Shukla A.,ABVIndian Institute of Information Technology and Management
Smart Innovation, Systems and Technologies | Year: 2016

Path planning problem revolves around finding a path from start node to goal node without any collisions. This paper presents an improved version of Focused Wave Front Algorithm for mobile robot path planning in static 2D environment. Existing wave expansion algorithms either provide speed or optimality. We try to counter this problem by preventing the full expansion of the wave and expanding specific nodes such that optimality is retained. Our proposed algorithm ‘Optimally Focused Wave Front algorithm’ provides a very attractive package of speed and optimality. It allocates weight and cost to each node but it defines cost in a different fashion and employs diagonal distance instead of Euclidean distance. Finally, we compared our proposed algorithm with existing Wave Front Algorithms. We found that our proposed approach gave optimal results when compared with Focused Wave Front Algorithm and faster results when compared with Modified Wave Front Algorithm. © Springer International Publishing Switzerland 2016.

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