On firefly algorithm: optimization and application in mobile robot navigation

Purpose This paper aims to propose an optimized overview of firefly algorithm (FA) over physical-natural impression of fireflies and its application in mobile robot navigation under the natural intelligence mechanism. Design/methodology/approach The brightness and luminosity are the decision variables in proposed study. The paper achieves the two major goals of robot navigation; first, the optimum path generation and, second, as an obstacle avoidance by co-in-centric sphere-based geometrical technique. This technique comprises the optimum path decision to objective function and constraints to paths and obstacles as the function of algebraic-geometry co-relation. Co-in-centric sphere is the proposed technique to correlate the constraints. Findings It is found that the present FA based on concentric sphere is suitable for efficient navigation of mobile robots at the level of optimum significance when compared with other approaches. Originality/value The paper introduces a novel approach to implement the FA for unknown and uncertain environment.

[1]  Mohammad Reza Meybodi,et al.  A Gaussian Firefly Algorithm , 2011 .

[2]  Michael Brand,et al.  Autonomous robot path optimization using firefly algorithm , 2013, 2013 International Conference on Machine Learning and Cybernetics.

[3]  Anders Lyhne Christensen,et al.  Synchronization and fault detection in autonomous robots , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Farid Nouioua,et al.  Quantum-inspired firefly algorithm with particle swarm optimization for discrete optimization problems , 2016, Soft Comput..

[5]  Bing Zeng,et al.  The modified firefly algorithm considering fireflies’ visual range and its application in assembly sequences planning , 2016 .

[6]  Dayal R. Parhi,et al.  Optimal path planning for a mobile robot using cuckoo search algorithm , 2016, J. Exp. Theor. Artif. Intell..

[7]  Roberto Sepúlveda,et al.  Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles , 2015, Expert Syst. Appl..

[8]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[9]  Adil Baykasoglu,et al.  Adaptive firefly algorithm with chaos for mechanical design optimization problems , 2015, Appl. Soft Comput..

[10]  Craig A. Tovey,et al.  On Honey Bees and Dynamic Server Allocation in Internet Hosting Centers , 2004, Adapt. Behav..

[11]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[12]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[13]  Dayal R. Parhi,et al.  Intelligent neuro-controller for navigation of mobile robot , 2009, ICAC3 '09.

[14]  Zoran Miljkovic,et al.  Bio-inspired approach to learning robot motion trajectories and visual control commands , 2015, Expert Syst. Appl..

[15]  Salwani Abdullah,et al.  Hybridizing firefly algorithms with a probabilistic neural network for solving classification problems , 2015, Appl. Soft Comput..

[16]  Ivan Stojmenovic,et al.  A harmony-seeking firefly swarm to the periodic replacement of damaged sensors by a team of mobile robots , 2012, 2012 IEEE International Conference on Communications (ICC).

[17]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[18]  Oscar Castillo,et al.  Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation , 2009, Appl. Soft Comput..

[19]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[20]  Surafel Luleseged Tilahun,et al.  Modified Firefly Algorithm , 2012, J. Appl. Math..

[21]  A. Gandomi,et al.  Mixed variable structural optimization using Firefly Algorithm , 2011 .

[22]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[23]  Keshav Kaushik,et al.  A Hybrid Data Clustering Using Firefly Algorithm Based Improved Genetic Algorithm , 2015 .

[24]  R. Venkatesan,et al.  Meta-heuristic approaches for minimizing error in localization of wireless sensor networks , 2015, Appl. Soft Comput..

[25]  Leandro dos Santos Coelho,et al.  A chaotic firefly algorithm applied to reliability-redundancy optimization , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[26]  Syamsiah Mashohor,et al.  A highly interpretable fuzzy rule base using ordinal structure for obstacle avoidance of mobile robot , 2011, Appl. Soft Comput..

[27]  Pratyusha Rakshit,et al.  Adaptive Firefly Algorithm for nonholonomic motion planning of car-like system , 2013, 2013 IEEE Congress on Evolutionary Computation.

[28]  Hassan Mathkour,et al.  Comparative study of soft computing techniques for mobile robot navigation in an unknown environment , 2015, Comput. Hum. Behav..

[29]  S. A. MirHassani,et al.  A hybrid Firefly-Genetic Algorithm for the capacitated facility location problem , 2014, Inf. Sci..

[30]  Mohammad Reza Meybodi,et al.  Speciation based firefly algorithm for optimization in dynamic environments , 2012 .

[31]  Theofanis Apostolopoulos,et al.  Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem , 2011 .

[32]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[33]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .