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Overview of Fireworks Algorithm Research and Applications

Overview of Fireworks Algorithm Research and Applications. A Novel Swarm Intelligence Algorithm. Ying Tan ( 谭营 ) Peking University, CHINA Email: ytan@pku.edu.cn. 2019 PKU Summer School on FWA. OUTLINES. Brief Introduction to Swarm Intelligence Fireworks Algorithm (FWA) FWA Variants

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Overview of Fireworks Algorithm Research and Applications

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  1. Overview of Fireworks Algorithm Research and Applications A Novel Swarm Intelligence Algorithm Ying Tan (谭营) Peking University, CHINA Email: ytan@pku.edu.cn 2019 PKU Summer School on FWA

  2. OUTLINES • Brief Introduction to Swarm Intelligence • Fireworks Algorithm (FWA) • FWA Variants • GPU-Based Parallel FWA • Typical Applications of FWA • Concluding Remarks

  3. Lead-in Optimization is an everlasting topic in science/Engineering How to deal with Opt. Problem? Methods: Exact Mathematical approach? or Evolutionary computing with good enough ? Strategy：Inspired by natural rules

  4. 1.Brief Introduction to Swarm Intelligence Swarm Intelligence (SI) refers to • Simple individuals or information processing units • Interaction between individuals or with environment • Emerging behavior in the swarm-level

  5. Introduction • Swarm intelligence is the property of a system whereby the collective behaviours of (unsophisticated) agents interacting locally with their environment cause coherent functional global patterns to emerge. #Papers about Swarm Intelligence

  6. 1.1 Motivations of SI • Biological population/swarm • Social phenomena • Other laws in nature as a swarm

  7. 1.2. Typical SI algorithms • Particle Swarm Optimization (PSO) • Ant Colony Optimization (ACO) • Artificial Immune System (AIS) • Bee Colony Optimization (BCO) • Bacterial Foraging Optimization (BFO) • Fish School Search (FSS) • Fireworks Algorithm (FWA) • Brain Storm Optimization (BSO) • Water Drop Optimization (WDO) • Wild-Weed Optimization (WWO) • ……

  8. OUTLINES • Brief Introduction to Swarm Intelligence • Fireworks Algorithm (FWA) • FWA Variants • GPU-Based Parallel FWA • Typical Applications of FWA • Concluding Remarks

  9. 2. Fireworks Algorithm (FWA) Definition of FWA Operators in FWA FWA flowcharts Experimental results

  10. Fireworks Algorithm (FWA)—Proposed • FWA is inspired by the splendid fireworks in the sky. Search Solution Space Searching Solution Space Tan, Ying, and Yuanchun Zhu. "Fireworks algorithm for optimization.“ Advances in Swarm Intelligence. Springer Berlin Heidelberg, 2010. 355-364.

  11. 2.1 Definition of FWA • Ideas Explosive Search Strategy

  12. 2.1 Definition of firework • Good firework: firework can generate a big population of sparks within a small range. • Bad firework: firework that generate a small population of sparks within a big range. • The next will introduce the operators in FWA.

  13. Number of sparks Number of sparks generated by each firework (Xi) is defined as follows: Fireworks with better fitness values produce more sparks while those with worse fitness values produce small sparks.

  14. Amplitude of explosion Amplitude of explosion is defined by The amplitude of explosion of a good firework is smaller than that of a bad one.

  15. 2.2.1 Explosion Operator SMALL RANGE MORE SPARKS BIG RANGE LITTLE SPARKS BAD GOOD

  16. 2.1.2 Mutation Operator • To keep the diversity of sparks, we design another way of generating sparks, namely Gaussian mutation operator. Gaussian sparks

  17. 2.1.3 Mapping Rules • Boundary [-100, 100]

  18. 2.1.4 Selection Sparse KEEP DIVERSITY! Crowd elitism strategy

  19. 2.3 The flowchart of FWA Repeat N Y selection strategy End Explosion operator Mutation operator Mapping rule

  20. 2.4 Experimental Results of FWA

  21. 2.4 Experimental Results of FWA

  22. 2.4 Experimental Results of FWA Conclusion: FWA is very successful and has a good prospect.

  23. History (from 2010 to 2018) 21. MO-FWA 22. FWA-CM 23. CoFFWA 24. Map-Reduce 25. CUDA 26. Surrogate-Assisted FWA 27 Privacy Preserving 8. EFWA 9. IFWA 10. GPU-FWA 11. Swarm Robots 12. Equations Problems 13. MOFWA 14. Spam Detection 15. Image Recognition 2. Digital Filter Design 3. NMF 4. 0/1 Problem 5. CA-FWA 34. FWA for JSP 35. Binary FWA for Feature Selection 36. FWA for Cloud Computing … 2014 2012 2016 2018 2010 28. GFWA 29. Community Detection 30. Neural Networks 31. Opposition-based FWA 32. Chaotic FWA 33. CoFFWA-CM 2015 2011 2013 16. dynFWA 17. AFWA 18. FWA-DM 19. Convergence Analysis 20. BBO-FWA 2017 1. FWA 37. Bare Bones FWA 38. Quantum FWA 39. Loser-Out Tournament-Based FWA 40. FWA with Estimated Convergence Point 6. AcFWA 7. FWA-DE

  24. An Overview of the Variants of the FWA

  25. Tendency of FWA-related publications The number of papers regarding FWA year-by-year

  26. FWA paper got 523Citations in Google Scholar 504

  27. FWA Monograph Ying Tan Fireworks Algorithm ---A Swarm Intelligence Optimization Method Springer 2015.10 ISBN: 978-3-662-46352-9. [Book at Springer.Com] www.springer.com/gp/book/9783662463529

  28. 2.Theoretical Results of FWA Ying Tan, Fireworks Algorithm: A Swarm Intelligence Optimization Method, Springer, 2015.10.

  29. FWA Tutorial Paper • Y. Tan, C. Yu, S.Q. Zheng and K. Ding • "Introduction to Fireworks Algorithms" • International Journal of Swarm Intelligence Research (IJSIR), • October-December 2013, vol. 4, No. 4, pp. 39-71.

  30. OUTLINES • Brief Introduction to Swarm Intelligence • Fireworks Algorithm (FWA) • FWA Variants • GPU-Based Parallel FWA • Typical Applications of FWA • Concluding Remarks • Further readings

  31. 3. Important Developments Fireworks Algorithm (2010) 5 Basic Improvements Enhanced FWA (2013) Adaptive Explosion Amplitude Dynamic/Adaptive FWA (2014) Orienting Mutation Cooperative Framework (2016) Cooperative FWA Guided FWA /Orienting Mutation FWA (2015) Competitive Interaction Simplified Bare Bone FWA Loser-out Tournament based FWA (2017) (2018)

  32. New Strategies in FWA • Dynamic/Adaptive amplitude of explosion for the best firework • Guiding Spark (GS) based on Information Utilization Concept • Loser-out Tournament (LOT) - Competitive Mechanism in FWA • Bare Bones Fireworks – A minimalist global optimizer Shaoqiu Zheng, JunzhiLi,AndreasJanecek, and Ying Tan, “A Cooperative Framework for Fireworks Algorithm,”IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)，2015, 14 (1) :27-41 Junzhi Li, Shaoqiu Zheng, and Ying Tan, "The Effect of Information Utilization: Introducing a Novel Guiding Spark in the Fireworks Algorithm"; IEEE Transactions on Evolutionary Computation(TEC), Vol. 21, No. 1, 2017, pp. 153-166. Junzhi Li, Ying Tan, “Loser-out Tournament Based Fireworks Algorithm for Multi-modal Function Optimization”; IEEE Transactions on Evolutionary Computation(TEC), 2018. 10.1109/TEVC.2017.2787042. Junzhi Li, Ying Tan, "The Bare Bones Fireworks Algorithm: A Minimalist Global Optimizer";Applied Soft Computing, 62(2018): 454-462. The most downloaded articles from Applied Soft Computing in the last 90 days!

  33. 3.1 Enhanced Fireworks Algorithm - EFWA 5 improvements are proposed in EFWA to overcome the disadvantages of conventional FWA. • Improvement 1 • FWA (Same distances) • EFWA (Different distances) • Improvement 2 • FWA -------- Amplitude tends to 0. • EFWA -------- Check minimal explosion amplitude. • Improvement 3 • FWA Gaussian explosion close to original point. • EFWA Use a new explosion strategy. • Improvement 4 • FWA ---- Mapping strategy tends to original point. • Improvement 5 • FWA ---- Select the individuals by density. • EFWA ---- Randomlyselect the individuals. S.Zheng, A. Janecek and Y. Tan, "Enhanced Fireworks Algorithm "2013 IEEE Congress on Evolutionary Computation, (CEC 2013) , June 20-23,, Cancun, Mexico, pp. 10-19.

  34. 3.2 Dynamic Search FWA (dynFWA) • Fireworks • Exploitation -> Accelerate the convergence speed. • Exploration -> Move towards to global optimum, the fireworks swarm can get a better position. Exploitation Exploration S.Q. Zheng, Andreas Janecek, J.Z. Li, and Y. Tan, "Dynamic Search in Fireworks Algorithm, “ 2014 IEEE World Conference on Computational Intelligence (IEEE WCCI'2014) - IEEE Congress on Evolutionary Computation (CEC'2014) , July 07-11, 2014, pp. 3222-3229.

  35. 3.2 Dynamic Search FWA (dynFWA) Exploration Exploitation

  36. 3.2 Dynamic Search FWA (dynFWA) • CEC 2013 • 28 functions

  37. 3.3 Raising Information Utilization Rate • Typically, most swarm/evolutionary algorithms share the same framework consisting of two steps: • New individuals are produced • Old individuals are eliminated. • In the first step, new information is acquired; while in the second step, some information about the objective function is abandoned. • The performances of these algorithms depend intensively on how and to what extent the information is utilized before it is abandoned. Therefore, • By raising information utilization rate, full usage of the information acquired by a heuristic algorithm in the search process is helpful for better guiding future behaviors.

  38. 3.3 dynFWA-OM: Orienting Mutation Spark

  39. Generating Orienting/guiding mutation spark G The position of the Gifor firework Xi is determined by Algorithm 2.

  40. 3.3 dynFWA-OM J. Li and Y. Tan, "Orienting Mutation Based Fireworks Algorithm, " 2015 IEEE Congress on Evolutionary Computation (CEC'2015) , May 25-28, 2015, Sendai International Center, Sendai, Japan, pp.1-8.

  41. 3.3 dynFWA-OM

  42. 3.3 dynFWA-OM

  43. 3.3 dynFWA-OM

  44. 3.3 dynFWA-OM

  45. 3.3 dynFWA-OM

  46. 3.3 dynFWA-OM

  47. 3.3 dynFWA-OM By raising the info utilization rate (IUR) via introducing the OM, it can help to increase the exploitation capability of the dynFWA algorithm greatly.

  48. Guided Fireworks Algorithm • Outperforms other classic algorithms on the standard benchmark. • Achieves state-of-the-art performance on 1000 dimensional optimization problems J. Li, S.Q. Zheng, and Y. Tan, " The Effect of Information Utilization: Introducing a Novel Guiding Spark in the Fireworks Algorithm," IEEE Transactions on Evolutionary Computation, Feb. 2017, Vol. 21, No.1, pp.153 – 166.

  49. Loser-out Tournament based FWA (LOT-FWA) • A competitive interaction mechanism: “exile” unpromising fireworks to reduce the probability of being trapped in local optima. Junzhi Li and Ying Tan, “Loser-out Tournament Based Fireworks Algorithm for Multi-modal Function Optimization," IEEE Transactions on Evolutionary Computation (TEVC), 2017 (accepted).

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