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Understanding the Limitations of Particle Swarm Algorithm for Dynamic Optimization Tasks: A Survey Towards the Singularity of PSO for Swarm Robotic Applications

Published: 28 July 2016 Publication History

Abstract

One of the most widely used biomimicry algorithms is the Particle Swarm Optimization (PSO). Since its introduction in 1995, it has caught the attention of both researchers and academicians as a way of solving various optimization problems, such as in the fields of engineering and medicine, to computer image processing and mission critical operations. PSO has been widely applied in the field of swarm robotics, however, the trend of creating a new variant PSO for each swarm robotic project is alarming. We investigate the basic properties of PSO algorithms relevant to the implementation of swarm robotics and characterize the limitations that promote this trend to manifest. Experiments were conducted to investigate the convergence properties of three PSO variants (original PSO, SPSO and APSO) and the global optimum and local optimal of these PSO algorithms were determined. We were able to validate the existence of premature convergence in these PSO variants by comparing 16 functions implemented alongside the PSO variant. This highlighted the fundamental flaws in most variant PSOs, and signifies the importance of developing a more generalized PSO algorithm to support the implementation of swarm robotics. This is critical in curbing the influx of custom PSO and theoretically addresses the fundamental flaws of the existing PSO algorithm.

References

[1]
Nor Azlina Ab Aziz and Zuwairie Ibrahim. 2012. Asynchronous particle swarm optimization for swarm robotics. International Symposium on Robotics and Intelligent Sensors (IRIS 2012). Procedia Engineering 41 (2012) 951--957
[2]
Roberto Battiti and Mauro Brunato. 2010. Reactive search optimization: Learning while optimizing. Handbook of Metaheuristics. Vol. 146 of the series International Series in Operational Research and Management Science. Springer, 543--571.
[3]
Gerardo Beni. 2004. From swarm intelligence to swarm robotics. In Proceedings of International Conference on Swarm Robotics. 1--9, 2004.
[4]
Brian Birge. 2005. Particle swarm optimization toolbox (http://www.mathworks.com/matlabcentral/fileexchange/7506), MATLAB Central File Exchange. Retrieved Jan. 2, 2013.
[5]
Yifan Cai and X. Yang Simon. 2016. A PSO-based approach with fuzzy obstacle avoidance for cooperative multi-robots in unknown environments. Int. J. Comp. Intel. Appl. International Journal of Computational Intelligence and Applications 15.01 (2016).
[6]
Maurice Clerc. 2004. Semi-continuous challenge. Retrieved from http://clerc.maurice.free.fr/pso//Semi-continuous_challenge/Semi-continuous_challenge.htm.
[7]
S. Micael Couceiro, Rocha P. Rui, and Ferreira M. F. Nuno. 2011a. A novel multi-robot exploration approach based on particle swarm optimization algorithms. In IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR2011, Kyoto, Japan, (2011).
[8]
S. Micael Couceiro, Rocha P. Rui, and Ferreira M. F. Nuno. 2011b. Ensuring Ad Hoc connectivity in distributed search with Robotic Darwinian swarms. In Proceedings of the IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR2011, Kyoto, Japan, 2011. 284--289.
[9]
S. Micael Couceiro, Rocha P. Rui, and Ferreira M. F. Nuno. 2012a. Towards a further understanding of the robotic darwinian PSO. In Computational Intelligence and Decision Making - Trends and Applications, From Intelligent Systems, Control and Automation: Science and Engineering Bookseries, Springer Verlag, 17--26, (2012a).
[10]
S. Micael Couceiro, Rocha P. Rui, and Ferreira M. F. Nuno. 2012b. Analysis and parameter adjustment of the rdpso - towards an understanding of robotic network dynamic partitioning based on darwin's theory. International Mathematical Forum, Hikari, Ltd., 7, 32, 1587--1601, (2012b).
[11]
S. Micael Couceiro, Rocha P. Rui, and Ferreira M. F. Nuno. 2012c. Introducing the fractional order robotic darwinian PSO. In Proceedings of the 9th International Conference on Mathematical Problems in Engineering, Aerospace and Sciences - ICNPAA’2012, Vienna, Austria, (2012c).
[12]
S. Micael Couceiro, Rocha P. Rui, and Ferreira M. F. Nuno. 2013. Benchmark of swarm robotics distributed techniques in a search task. Robotics and Autonomous Systems (2013), http://dx.doi.org/10.1016/j.robot.2013.10.004
[13]
Kurt Derr and Milos Manic. 2009. Multi-robot, multi-target particle swarm optimization search in noisy wireless environments. In Proceedings of the 2nd Conference on Human System Interactions, Catania, Italy. 78--83, 2009.
[14]
S. Doctor, G. K. Venayagamoorthy, and V. G. Gudise. 2004. Optimal PSO for collective robotic search applications. In IEEE Congress on Evolutionary Computation 2004, 1390--1395.
[15]
K. Easton and J. Burdick. 2005. A coverage algorithm for multi-robot boundary inspection. In Proceeding of the IEEE International Conference on Robotics and Automation, ICRA, Barcelona, Spain, 2005, 727--734.
[16]
Peter Eberhard and Kai Sedlaczek. 2009. Using augmented lagrangian particle swarm optimization for constrained problems in engineering. Advanced Design of Mechanical Systems: From Analysis to Optimization CISM International Centre for Mechanical Sciences, 253--71, (2009).
[17]
R. C. Eberhart and Yuhui Shi. 2000. Comparing inertia weights and constriction factors in particle swarm optimization. In Proceedings of the Congress on Evolutionary Computation. 1, 84--88, (2000).
[18]
R. C. Eberhart and Yuhui Shi. 2001. Tracking and optimizing dynamic systems with particle swarms. In Proceedings of the 2001 Congress on Evolutionary Computation, 2001, 1, 94--100. IEEE, 2002.
[19]
Masehian Ellips and Davoud Sedighizadeh. 2010. A multi-objective PSO-based algorithm for robot path planning. IEEE Journal 465--470, (2010).
[20]
P. Andries Engelbrecht. 2005. Fundamentals of computational swarm intelligence. Wiley 1st Edition (December 16, 2005). ISBN-10: 0470091916, 672
[21]
M. Fikret Ercan and Li Xiang. 2011. Swarm robot flocking: An empirical study. Intelligent Robotics and Applications Lecture Notes in Computer Science (2011), 495--504.
[22]
D. Fritsch. 2009. Steuerung selbstorganisierender multi-roboter-system für dynamische Sammelaufgaben am Beispiel der Bekämpfung maritimer Ölverschmutzungen (in German). Doctoral thesis, University of Stuttgart, Germany, (2009).
[23]
Zhi-Feng Hao, Guo Guang-Han, and Huang Han. 2007. A particle swarm optimization algorithm with differential evolution. In Proceedings of Sixth International Conference on Machine Learning and Cybernetics. 1031--1035, (2007).
[24]
T. Adam Hayes, Martinoli Alcherio, and M. Goodman Rodney. 2003. Swarm robotic odor localization: Off-line optimization and validation with real robots. Robotica 2003, 21, 4, 427--441.
[25]
M. James Hereford. 2006. A distributed particle swarm optimization algorithm for swarm robotic applications. In IEEE Congress on Evolutionary Computation 2006, 1678--1685.
[26]
M. James Hereford and Michael Siebold. 2008. Multi-robot search using a physically embedded particle swarm optimization. Int. Journal of Comput. Intell. Res. 2008, 4, 2, 197--209.
[27]
M. James Hereford, Siebold Michael, and Nichols Shannon. 2007. Using the particle swarm optimization algorithm for robotic search applications. In Proceedings of the IEEE Swarm Intelligence Symposium, Honolulu, USA. 53--59, (2007).
[28]
X. Hu and R. C. Eberhart. 2002. Multi-objective optimization using dynamic neighborhood particle swarm optimization. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC’02), IEEE Service Center, Honolulu, Hawaii, USA. 2, 1677--1681, (2002).
[29]
M. Jager and B. Nebel. 2002. Dynamic decentralized area partitioning for cooperative cleaning robots. In Proceeding of IEEE International Conference on Robotics and Automation, ICRA. Washington DC, USA, 2002, Page 3577--3582.
[30]
W. Jatmiko, K. Sekiyama, and T. Fukuda. 2007. A PSO-based mobile robot for odor source localization in dynamic advection-diffusion with obstacles environment: Theory, simulation and measurement. IEEE Comput. Intell. Mag. 2007, 2, 2, 37--51.
[31]
Xu Jun-Jie and Xin Zhan-Hong. 2005. An extended particle swarm optimizer. Proc. IEEE Symp. Parallel and Distributed (2005).
[32]
M. P. Nikolaos Kakalis and Ventikos Yiannis. 2008. Robotic swarm concept for efficient oil spill confrontation. Journal of Hazardous Materials 154, 1--3, 880--887, (2008).
[33]
Dervis Karaboga and Bahriye Basturk. 2007. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. J. Glob. Optim. 39, 459--471.
[34]
D. Karaboga and S. Ökdem. 2004. A simple and global optimization algorithm for engineering problems: Differential evolution algorithm. Turkish Journal of Electrical Engineering 12, 1, 53--60.
[35]
J. Karimi and S. H. Pourtakdoust. 2013. Optimal manoeuvre-based motion planning over terrain and threats using a dynamic hybrid PSO algorithm. Aerospace Science and Technology Journal, Elsevier, 2012
[36]
F. James Kennedy, C. Eberhart Russell, and Shi Yuhui. 2001. Swarm intelligence. Morgan Kaufmann Publishers, US, 2001.
[37]
F. James Kennedy, C. Eberhart Russell, and Shi Yuhui. 2004. Swarm intelligence. Morgan Kaufmann Publishers, San Francisco, CA, 2004.
[38]
James Kennedy and C. Russell Eberhart. 1995. Particle swarm optimization. In Proceedings of the International Conference on Neural Networks, vol. 4, IEEE Press, Piscataway, NJ, 1995 pp. 1942--1948.
[39]
J. Kennedy and R. C. Eberhart. 1997. A discrete binary version of the particle swarm algorithm. Int. IEEE Conf. on Systems, Man, and Cyber, 5, 4104--4108, (1997).
[40]
Byung-II Koh, Alan D. George, Raphael T. Haftka, and Benjamin J. Fregly. 2006. Parallel asynchronous particle swarm optimization. International Journal of Numerical Methods Engineering 67, 4, 578--595.
[41]
R. Mendes, J. Kennedy, and J. Neves. 2004. The fully informed particle swarm: Simpler, may be better. IEEE Transactions on Evolutionary Computation 8, 3, 204--210, (2004).
[42]
Yan Meng, Kazeem Olorundamilola, and C. Muller Juan. 2007. A hybrid ACO/PSO control algorithm for distributed swarm robots. IEEE International Symposium on Computational Intelligence in Robotics and Automation Conference, (2007).
[43]
Iñaki Navarro and Fernando Matía. 2013. An introduction to swarm robotics. Hindawi Publishing Corporation ISRN Robotics, 2013, Article ID 608164, 1--10, (2013).
[44]
Bijaya Ketan Panigrahi, Shi Yuhui, and Lim Meng-Hiot. 2011. Handbook of swarm intelligence: concepts, principles and applications. Springer-Verlag Berlin Heidelberg, ISBN 978-3-642-17389-9, 119--132, 2011.
[45]
Jim Pugh and Martinoli Alcherio. 2007. Inspiring and modeling multi-robot search with particle swarm optimization. In IEEE Swarm Intelligence Symposium 2007, Honolulu, USA. 332--339.
[46]
Jim Pugh, Segapelli Loïc, and Martinoli Alcherio. 2006. Applying aspects of multi robot search to particle swarm optimization. In Proceedings of the 5th International Workshop on ant Colony Optimization and Swarm Intelligence, Brussels, Belgium. 506--507, (2006).
[47]
Yuan-Qing Qin, De-Bao Sun, Ning Li, and Yi-Gang Cen. 2004. Path planning for mobile robot using the particle swarm optimization with mutation operator. In Proceedings of IEEE International Conference on Machine Learning and Cybernetics. 4, 2473--2478.
[48]
Ashish Raj. 1994. Evolutionary optimization algorithms for non-linear systems. Thesis Submitted to the Department Electrical and Computer Engineering, Utah State University, Logan, Utah. 1994
[49]
P. Raja and S. Pugazhenthi. 2009. Path planning for mobile robots in dynamic environments using particle swarm optimization. International Conference on Advances in Recent Technologies in Communication and Computing (ARTcom09). IEEE, 401--405.
[50]
Ioannis Rekleitis, Gregory Dudek, and Evangelos Milios. 2001. Multi-robot collaboration for robust exploration. Annals of Mathematics and Artificial Intelligence 31, 1--4, 7--14, (2001).
[51]
Samuel Rutishauser, Nikolaus Correll, and Alcherio Martinoli. 2009. Collaborative coverage using a swarm of networked miniature robots. Journal of Robotics and Autonomous Systems 57, 517--525, (2009).
[52]
Martin Saska, Vojtěch Vonásek, and Libor Přeučil. 2013. Trajectory planning and control for airport snow sweeping by autonomous formations of ploughs. Journal of Intelligent & Robotic Systems, April, 1--23, (2013).
[53]
Y. Shi and R. Eberhart. 1998. A modified particle swarm optimizer. 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98TH8360). 69--73.
[54]
Yuhui Shi and R. C. Eberhart. 2001. Fuzzy adaptive particle swarm optimization. Proc. IEEE / Conf. on Evolutionary Computing, (2001).
[55]
M. Stefik. 1985. Vehicles: experiments in synthetic psychology v. braitenberg, (MIT, cambridge, MA, 1984); 152 pages,. Artificial Intelligence, 27.2, 246--248, (1985).
[56]
Jesus Suarez and Robin Murphy. 2011. A survey of animal foraging for directed, persistent search by rescue robotics. In Proceedings of the IEEE International Symposium on Safety, Security and Rescue Robotics, Kyoto, Japan, 314--320, (2011).
[57]
Jun Sun, Lai Choi-Hong, and Wu Xiao-Jun. 2012. Particle swarm optimization: classical and quantum perspectives. CRC press, Taylor and Francis Group 2012. ISBN: 978-1-4398-3576-0, 60--61.
[58]
Qirong Tang and Peter Eberhard. 2011. A PSO-based algorithm designed for a swarm of mobile robots. Journal of Industrial Application. Struct Multidisc Optim. 44, 483--498, (2011).
[59]
Y. Wang, I. P. Sillitoe, and D. J. Mulvaney. 2007. Mobile Robot Path Planning in Dynamic Environments. IEEE International Conference on Robotics and Automation Roma, Italy, 10--14.
[60]
Li Wang, Yushu Liu Hongbin Deng, and Yuanqing Xu. 2006. Obstacle-avoidance path planning for soccer robots using particle Swarm Optimization. In IEEE International Conference on Robotics and Biomimetics. 1233--1238.
[61]
Yao Xin. 2004. Parallel problem solving from nature. 8th International Conference Berlin, 2004 Proceedings, Springer.
[62]
Songdong Xue and Jianchao Zeng. 2009. Controlling swarm robots for target search in parallel and asynchronously. International Journal of Modelling, Identification and Control 8, 4, 353--360, (2009).
[63]
Songdong Xue, Zhang Jianhua, and Zeng Jianchao. 2009a. Parallel asynchronous control strategy for target search with swarm robots. Int J Bio-Inspired Comput (IJBIC) 1, 3, 151--163, (2009).
[64]
Yinghua Xue, Guohui Tian, and Bin Huang. 2009b. Optimal robot path planning based on danger degree map. IEEE International Conference on Automation and Logistics, 2009. ICAL’09. (2009).
[65]
Songdong Xue, Jin Li, Jianchao Zeng, Xiaojuan He, and Guoyou Zhang. 2011. Synchronous and asynchronous communication modes for swarm robotics search. In “mobile robots -- control architectures, bio-interfacing, navigation, multi robot motion planning and operator training”, J. Bedkowski, Editor, Intech, 2011.
[66]
Songdong Xue, Zan Yunlong, Zeng Jianchao, Xue Zhibin, and Du Jing. 2012. Group decision making aided PSO-type swarm robotic search. International Symposium on Computer, Consumer and Control, IEEE 2012. 785--788.
[67]
Yudong Zhang, Wu Lenan, and Wang Shuihua. 2013. UCAV path planning by fitness-scaling adaptive chaotic particle swarm optimization. Mathematical Problems in Engineering, 2013, Article ID 705238, 9 (2013).

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    cover image ACM Computing Surveys
    ACM Computing Surveys  Volume 49, Issue 1
    March 2017
    705 pages
    ISSN:0360-0300
    EISSN:1557-7341
    DOI:10.1145/2911992
    • Editor:
    • Sartaj Sahni
    Issue’s Table of Contents
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    Publication History

    Published: 28 July 2016
    Accepted: 01 February 2016
    Revised: 01 January 2016
    Received: 01 October 2014
    Published in CSUR Volume 49, Issue 1

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    1. Particle swarm optimization (PSO)
    2. swarm intelligence
    3. swarm robotics

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    • Ministry of Education (Malaysia) and University of Malaya

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