Nature inspired evolutionary approaches for robot navigation: Survey

References

• N.H. Abbas and F.M. Ali , Path planning of an autonomous mobile robot using enhanced bacterial foraging optimization algorithm, Al-Khwarizmi Engineering Journal , 12.4, pp. 26-35, (2017) doi: 10.22153/kej.2016.01.001
   Google Scholar
• M. Algabri , H. Mathkour , H. Ramdane and M. Alsulaiman , Comparative study of soft computing techniques for mobile robot navigation in an unknown environment, Computers in Human Behavior , 50, pp. 42-56, (2015) doi: 10.1016/j.chb.2015.03.062
   Web of Science ®Google Scholar
• V. Agrawal , R. Rastogi and D.C. Tiwari , Spider monkey optimization: A survey, International Journal of System Assurance Engineering and Management , 9.4, pp. 929-941, (2018) doi: 10.1007/s13198-017-0685-6
   Web of Science ®Google Scholar
• A. Darwish , Bio-Inspired Computing: Algorithms Review, Deep Analysis, and the Scope of Applications, Future Computing and Informatics Journal , 3.2, pp. 231-246, (2018) doi: 10.1016/j.fcij.2018.06.001
   Google Scholar
• B.K. Patle , G. Babu , A. Pandey , D.R. Parhi , and A. Jagadeesh , A review: On path planning strategies for navigation of mobile robot, Defence Technology , 15.4, pp. 582-606, (2019) doi: 10.1016/j.dt.2019.04.011
   Web of Science ®Google Scholar
• X. Liang , L. Li , J. Wu and H. Chen , Mobile robot path planning based on adaptive bacterial foraging algorithm, Journal of Central South University , 20.12, pp. 3391-3400, (2013) doi: 10.1007/s11771-013-1864-5
   Web of Science ®Google Scholar
• S. Mirjalili , S.M. Mirjalili and A. Lewis , Grey wolf optimizer, Advances in Engineering Software , 69, pp. 46–61 (2014) doi: 10.1016/j.advengsoft.2013.12.007
   Web of Science ®Google Scholar
• T.K. Dao , T.S. Pan and J.S. Pan , A multi-objective optimal mobile robot path planning based on whale optimization algorithm, IEEE 13th International Conference on Signal Processing , pp. 337-342, (2016)
   Google Scholar
• Jaswal, G. , & Poonia, R. C. Selection of optimized features for fusion of palm print and finger knuckle? based person authentication. Expert Systems, e12523.
   Google Scholar
• A.H. Paniagua , M.A. Vega-Rodriguez , J. Ferruz and N. Pavon , Solving the multi-objective path planning problem in mobile robotics with a firefly-based approach, Soft Computing , 21.4, pp. 949-964, (2017) doi: 10.1007/s00500-015-1825-z
   Web of Science ®Google Scholar
• S. Mirjalili and A. Lewis , The Whale Optimization Algorithm, Advances in Engineering Software , 95, pp. 51–67, (2016). doi: 10.1016/j.advengsoft.2016.01.008
   Web of Science ®Google Scholar
• B.K. Patle , A. Pandey , A. Jagadeesh , and D.R. Parhi , Path planning in uncertain environment by using firefly algorithm, Defence Technology , 14.6, pp. 691-701, (2018) doi: 10.1016/j.dt.2018.06.004
   Web of Science ®Google Scholar
• K.M. Passino , Biomimicry of bacterial foraging for distributed optimization and control, IEEE control systems , 22.3, pp. 52-67, (2002) doi: 10.1109/MCS.2002.1004010
   Web of Science ®Google Scholar
• L.S. Coelho and C.C. Silveira , Improved bacterial foraging strategy for controller optimization applied to robotic manipulator system, IEEE International Conference on Control Applications , pp. 1276-1281, (2006)
   Google Scholar
• C. Liu , B. Niu , H. Chen and Y. Zhu , Robot path planning using bacterial foraging algorithm, Journal of Computational and Theoretical Nanoscience , 10.12, pp. 2890-2896, (2013) doi: 10.1166/jctn.2013.3296
   Google Scholar
• A.R. Mehrabian and C. Lucas , A novel numerical optimization algorithm inspired from weed colonization, Ecological Informatics , 1.4, pp. 355-366, (2006) doi: 10.1016/j.ecoinf.2006.07.003
   Web of Science ®Google Scholar
• Y. Zhou , Q. Luo and H. Chen , A Novel Differential Evolution Invasive Weed Optimization Algorithm for Solving Nonlinear Equations Systems, Journal of Applied Mathematics , 2013.757391, pp. 1-18, (2013)
   Google Scholar
• X.S. Yang , A New Metaheuristic Bat-Inspired Algorithm, Nature Inspired Cooperative Strategies for Optimization , 284, pp. 65-74, (2010) doi: 10.1007/978-3-642-12538-6_6
   Google Scholar
• J. Guo , Y. Gao and G. Cui , The path planning for mobile robot based on bat algorithm, International Journal of Automation and Control , 9.1, pp. 50-60, (2015) doi: 10.1504/IJAAC.2015.068041
   Web of Science ®Google Scholar
• Divya Agarwal & Pushpendra S. Bharti (2019) Computation of cause and effect relationship for acceptance of autonomous mobile robots in industries, Journal of Statistics and Management Systems , 22:2, 237–256 doi: 10.1080/09720510.2019.1580903
   Web of Science ®Google Scholar
• S. Mirjalili , S.M. Mirjalili and X.S. Yang , Binary bat Algorithm , Journal of neural computing and applications , 25.3, pp. 663-681, (2014)
   Google Scholar
• H. Zaher , M. Sherbieny , N.R. Saied and E. Oun , An alternative Bat algorithm, Journal of Multidisciplinary Engineering Science and Technology , 4.2, pp. 6736-6746, (2017)
   Google Scholar
• X.S. Yang , Nature-inspired metaheuristic algorithm, Luniver press, (2008)
   Google Scholar
• A.K. Sadhu , A. Konar , T. Bhattacharjee and S. Das , Synergism of firefly algorithm and Q-learning for robot arm path planning, Swarm and Evolutionary Computation , 43, pp. 50-68, (2018) doi: 10.1016/j.swevo.2018.03.014
   Web of Science ®Google Scholar
• R. Sharma and A. Saha , Optimization of Object-oriented testing using firefly algorithm, Journal of Information and Optimization Sciences , 38(6), pp. 873-893, (2017) doi: 10.1080/02522667.2017.1372135
   Web of Science ®Google Scholar
• L. Tighzert , C. Fonlupt and B. Mendil , A set of new compact firefly algorithms, Swarm and Evolutionary Computation , 40, pp. 92-115, (2018) doi: 10.1016/j.swevo.2017.12.006
   Web of Science ®Google Scholar
• S. Saremi , S. Mirjalili and A. Lewis , Grasshopper Optimisation Algorithm: Theory and application, Adv. in Engg. Software , 105, pp. 30–47, (2017) doi: 10.1016/j.advengsoft.2017.01.004
   Web of Science ®Google Scholar
• Z. Elmi and M. Onder-Efe , Multi-objective grasshopper optimization algorithm for robot path planning in static environment, IEEE International Conference on Industrial Technology , pp. 244-249, (2018)
   Google Scholar
• A.G. Neve , G.M. Kakandikar and O. Kulkarni , Application of Grasshopper Optimization Algorithm for Constrained and Unconstrained Test Functions, International Journal of Swarm Intelligence and Evolutionary Computation , 6.3, pp. 1-7, (2017) doi: 10.4172/2090-4908.1000165
   Google Scholar
• X.S. Yang and S. Deb , Cuckoo search via Lévy flights, IEEE World congress on Nature and Biologically Inspired Computing , pp. 210-214, (2009) doi: 10.1109/NABIC.2009.5393690
   Google Scholar
• M. Mareli and B. Twala , An adaptive Cuckoo search algorithm for optimisation, Applied Computing and Informatics , 14, pp. 107–115, (2018) doi: 10.1016/j.aci.2017.09.001
   Google Scholar
• P.K. Mohanty and D.R. Parhi , Optimal path planning for a mobile robot using cuckoo search algorithm, Journal of Experimental and Theoretical Artificial Intelligence , 28.1-2, pp. 35-52, (2016) doi: 10.1080/0952813X.2014.971442
   Web of Science ®Google Scholar
• F.F. Razi and N. Shadloo , Portfolio selection using hybrid algorithm of data envelopment analysis based on Kohonen neural network and Cuckoo Algorithm, Journal of Information and Optimization Sciences , 37(4), pp. 549-567, (2016) doi: 10.1080/02522667.2015.1103483
   Web of Science ®Google Scholar
• B. Yang , J. Miao , Z. Fan , J. Long and X. Liu , Modified cuckoo search algorithm for the optimal placement of actuators problem, Applied Soft Computing Journal , 67, pp. 48-60, (2018) doi: 10.1016/j.asoc.2018.03.004
   Web of Science ®Google Scholar
• M.C. Chiu , Path planning for an autonomous robot using a simulating annealing, Journal of Information and Optimization Sciences , 32 (2), pp. 297-314, (2011) doi: 10.1080/02522667.2011.10700058
   Google Scholar
• Kamalova, S. Navruzov, D. Qian and S.G. Lee , Multi-Robot exploration based on multi-objective grey wolf optimizer, Applied Sciences , 9.2931, pp. 1-16, (2019) doi: 10.3390/app9142931
   Google Scholar
• M. Petrović , and Z. Miljković , Grey Wolf Optimization Algorithm for Single Mobile Robot Scheduling, Proceedings of 4th International Conference on Electrical, Electronics and Computing Engineering , pp. 1-6, (2017)
   Google Scholar
• A.M. Rao , K. Ramji and T.N. Kumar , Intelligent Navigation of mobile robot using Grey wolf Colony Optimization, Materials Today Proceedings , 5.9, pp. 19116-19125, (2018) doi: 10.1016/j.matpr.2018.06.183
   Google Scholar
• J. Xu , F. Yan , O.G. Ala , L. Su and F. Li , Chaotic dynamic weight grey wolf optimizer for numerical function optimization, Journal of Intelligent & Fuzzy Systems , 37.2, pp. 2367-2384, (2019) doi: 10.3233/JIFS-182706
   Web of Science ®Google Scholar
• J.C. Bansal , H. Sharma , S.S. Jadon and M. Clerc , Spider Monkey Optimization algorithm for numerical optimization, Memetic Computing , 6.1, pp. 31-47, (2014) doi: 10.1007/s12293-013-0128-0
   Web of Science ®Google Scholar
• K. Gupta , K. Deep and J.C. Bansal , Spider Monkey optimization algorithm for constrained optimization problems, Soft Computing , 21.23, pp. 6933-6962, (2017) doi: 10.1007/s00500-016-2419-0
   Web of Science ®Google Scholar
• N. Mittal , U. Singh , R. Salgotra and B.S. Sohi , A Boolean spider monkey optimization-based energy efficient clustering approach for WSNs, Wireless Network , 24.6, pp. 2093-2109, (2018) doi: 10.1007/s11276-017-1459-4
   Web of Science ®Google Scholar
• R. Sharma and A. Saha , Ant Lion optimizer for state based object oriented testing, Journal of Information and Optimization Sciences , 40(2), pp. 219-232, (2019) doi: 10.1080/02522667.2019.1578085
   Web of Science ®Google Scholar
• L. Wang , S.X. Yang and M. Biglarbegian , Bio-inspired Navigation of Mobile Robots, International Conference on Autonomous and Intelligent Systems , pp. 59-68, (2012) doi: 10.1007/978-3-642-31368-4_8
   Google Scholar