Outline

  • Abstract
  • Keywords
  • 1 Introduction
  • 2 Background Information
  • 2.1 Uniform Distribution
  • 2.2 Normal or Gaussian Distribution
  • 2.3 Lévy Flights
  • 2.4 Chaotic Maps
  • 2.5 Random Sampling in Turbulent Fractal Cloud
  • 3 Randomized Firefly Algorithms
  • 3.1 Original Firefly Algorithm
  • 3.2 Variants of the Randomized Firefly Algorithm
  • 4 Experiments and Results
  • 4.1 Test Suite
  • 4.2 Experimental Setup
  • 4.3 Pc Configuration
  • 4.4 Results
  • 5 Conclusion
  • References

رئوس مطالب

  • چکیده
  • کلید واژه ها
  • 1 مقدمه
  • 2 اطلاعات زمینه
  • 2.1 توزیع یکنواخت
  • 2.2 توزیع گاوسی یا نرمال
  • 2.3 پرواز لوی
  • 2.4 نقشه بی نظمی
  • 3 الگوریتم های کرم شب تاب تصادفی
  • 3.1 الگوریتم کرم شب تاب اصلی
  • 3.2 انواع الگوریتم های کرم شب تاب تصادفی
  • 4 آزمایشات و نتایج
  • 4.1 مجموعه ی تست
  • 4.2 راه اندازی آزمایشی
  • 4.3 تنظیم PC
  • 4.4 نتایج
  • 5 نتیجه گیری

Abstract

The firefly algorithm is a stochastic meta-heuristic that incorporates randomness into a search process. Essentially, the randomness is useful when determining the next point in the search space and therefore has a crucial impact when exploring the new solution. In this chapter, an extensive comparison is made between various probability distributions that can be used for randomizing the firefly algorithm, e.g., Uniform, Gaussian, Lévi flights, Chaotic maps, and the Random sampling in turbulent fractal cloud. In line with this, variously randomized firefly algorithms were developed and extensive experiments conducted on a well-known suite of functions. The results of these experiments show that the efficiency of a distributions largely depends on the type of a problem to be solved.

Keywords: - - - -

Conclusions

In this chapter, an extensive comparison of various probability distributions is performed that can be used to randomize the firefly algorithm, e.g., uniform, Gaussian, Lévi flights, chaos maps and the random sampling in turbulent fractal cloud. In line with this, various firefly algorithms with various randomized methods were developed and extensive experiments were conducted on well-known suite of functions.

The goal of the experiments were threefold. Firstly, the mentioned randomized methods were analyzed. Secondly, an impact of randomized methods on the results of the RFA algorithms were verified. Finally, the results of the original FA algorithm and FFA variant of RFA were compared with the other well-known algorithms like ABC, BA, and DE.

In summary, the selection of an appropriate randomized method has a great impact on the results of RFA. Moreover, this selection depends on the nature of the problem to be solved. On the other hand, selecting the appropriate randomized method can improve the results of the original FA significantly.

In the future, further experiments should be performed with the random sampling in turbulent fractal cloud that exhibits the excellent results especially by optimizing the multi-modal functions.

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