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Standard fireworks algorithm 2017

Standard fireworks algorithm 2017

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Fireworks algorithm (FWA) [1] is a novel evolution algorithm developed since 2010. Unlike other population-based algorithms, individuals in FWA cooperate to control their behavior and allocation of computation resource. Instead, most algorithms like particle swarm optimization (PSO) concentrate on the moving of populations. Firework algorithm was well studied in these years. There has been theoretical analysis, engineering applications and algorithm improvements in FWA, making it a well-known and competitive optimization method. Plenty of researchers are trying to test and adjust some old operators or introduce new ones in order to improve FWA's performance. However, these works' contribution to the further development of FWA is indeed limited for several reasons. The most common problem is the selection of benchmark functions. Some researchers chose old-fashioned functions, and some of them designed their own. Besides, some researchers did not explain how they fine-tuned the hyperparameters of the old algorithms in detail. In experiments, some researchers chose a relatively old version of FWA as the base of their new algorithm, even though some parts of these old algorithms have already been proved inefficient. So, their methods might not be valid for the later versions of FWA. When some researchers designed a set of operators, sometimes, they neglect to test each combination of the new and the old ones. Thus, we do not know how each operator works and how they cooperate with each other. So, we gathered some works on FWA and did a series of experiments on the latest benchmark functions of CEC2017 [2], hoping to get a set of operators that is both simple and effective (do not have to perform best), and form the standard FWA in 2017. On the one hand, this would help researchers to stop worrying about on which version of FWA they should implement their ideas and on which they should test them. And on the other hand, this is a summary of the development of FWA and the experiments can reveal some of the abilities of those methods. The remainder of this chapter is organized as follows. Section 1.1 presents the principle of FWA and some developments on it, which will be applied later in the experiments. Section 1.2 explains our experiment procedures and settings. Section 1.3 analyses the results of experiments. Section 1.4 concludes the chapter and discusses the further researches on FWA.

Chapter Contents:

  • Abstract
  • 1.1 Introduction to fireworks algorithms
  • 1.1.1 Principle of fireworks algorithm
  • 1.1.2 Explosion
  • 1.1.2.1 Mutation
  • 1.1.2.2 Selection
  • 1.1.2.3 Communication
  • 1.1.2.4 Mapping rule
  • 1.1.3 Dynamic search fireworks algorithm
  • 1.1.3.1 Dynamic explosion amplitude
  • 1.1.4 Exponentially decreased dimension number strategy based dynFWA
  • 1.1.4.1 Exponentially decreased dimension number strategy
  • 1.1.5 Fireworks algorithm with adaptive transfer function
  • 1.1.5.1 Adaptive transfer function
  • 1.1.6 Fireworks algorithm with covariance mutation
  • 1.1.6.1 Covariance mutation
  • 1.1.7 Guided fireworks algorithm
  • 1.1.7.1 Guiding sparks mutation
  • 1.1.8 Cooperative framework of fireworks algorithm
  • 1.1.8.1 Local selection
  • 1.1.8.2 The crowdness- avoiding communication
  • 1.1.9 Other unpublished works
  • 1.1.9.1 Distribute sparks by power law
  • 1.1.9.2 Competitive communication
  • 1.2 Design of experiments
  • 1.2.1 Test environment
  • 1.2.2 Parameters and operators setting
  • 1.2.2.1 Parameters
  • 1.2.2.2 Operators
  • 1.2.3 Experimental procedures
  • 1.2.3.1 Step one
  • 1.2.3.2 Step two
  • 1.2.3.3 Step three
  • 1.2.4 Experiments results
  • 1.3 Analysis of the results
  • 1.3.1 Analysis of step one
  • 1.3.2 Analysis of step two
  • 1.3.3 Analysis of step three
  • 1.3.4 Comparing with former FWAs
  • 1.3.5 Summary and some further discuss
  • 1.4 Conclusion and prospect
  • Acknowledgments
  • References

Inspec keywords: particle swarm optimisation; evolutionary computation

Other keywords: computation resource; particle swarm optimization; PSO; standard fireworks algorithm; population-based algorithms; FWA's performance; evolution algorithm

Subjects: Optimisation techniques; Optimisation techniques; Optimisation

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