Hybrid Firefly Variants Algorithm for Localization Optimization in WSN

• Main Authors: P. SrideviPonmalar, V. Jawahar Senthil Kumar, R. Harikrishnan
• Format: Article
• Language: English
• Published: Atlantis Press 2017-01-01
• Series: International Journal of Computational Intelligence Systems
• Subjects: Localization; Genetic Algorithm; Differential Evolution; Particle Swarm Optimization; Firefly; WSN
• Online Access: https://www.atlantis-press.com/article/25883366/view

Localization is one of the key issues in wireless sensor networks. Several algorithms and techniques have been introduced for localization. Localization is a procedural technique of estimating the sensor node location. In this paper, a novel three hybrid algorithms based on firefly is proposed for localization problem. Hybrid Genetic Algorithm-Firefly Localization Algorithm (GA-FFLA), Hybrid Differential Evolution-Firefly Localization Algorithm (DE-FFLA) and Hybrid Particle Swarm Optimization -Firefly Localization Algorithm (PSO-FFLA) are analyzed, designed and implemented to optimize the localization error. The localization algorithms are compared based on accuracy of estimation of location, time complexity and iterations required to achieve the accuracy. All the algorithms have hundred percent estimation accuracy but with variations in the number of fireflies’ requirements, variation in time complexity and number of iteration requirements.