Neural Computing and Applications

, Volume 31, Issue 1, pp 79–99 | Cite as

A best firework updating information guided adaptive fireworks algorithm

  • Haitong Zhao
  • Changsheng ZhangEmail author
  • Jiaxu Ning
Original Article


As a new variant of swarm intelligence algorithm, fireworks algorithm (FWA) has significant performance on solving single objective problems, and has been applied broadly on a number of fields. To further improve its performance, a best firework updating information guided adaptive fireworks algorithm (PgAFWA) is proposed, in which the evolving process is guided by the direction from previous best firework to the current best firework from two aspects: amplifying the explosion amplitude on the direction that the best firework is updated, and making more sparks which are generated by the best firework distributed on this direction to further enhance the exploring ability on it. Numerical experiment on CEC2015 test suite was implemented to verify performance of the proposed algorithm. The experiment results indicated that the PgAFWA outperformed the compared algorithms in terms of both convergence speed and solving quality.


Fireworks algorithm Best firework Adaptive fireworks algorithm Updating direction Explosion range 



This work was supported by the National Natural Science Foundation Program of China (61572116, 61572117), and the Special Fund for Fundamental Research of Central Universities of Northeastern University (N150408001, N150404009, N161606003).

Compliance with ethical standards

Conflict of interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or comspany that could be construed as influencing the position presented in, or the review of, the manuscript entitled, “A Best Firework Updating Information Guided Adaptive Fireworks Algorithm”.


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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.College of Computer Science & EngineeringNortheastern UniversityShenyangPeople’s Republic of China

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