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Open AccessArticle

Fuzzy Fireworks Algorithm Based on a Sparks Dispersion Measure

Tijuana Institute of Technology, Tijuana 22414, Mexico
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Algorithms 2017, 10(3), 83; https://doi.org/10.3390/a10030083
Received: 30 May 2017 / Revised: 7 July 2017 / Accepted: 18 July 2017 / Published: 21 July 2017
(This article belongs to the Special Issue Extensions to Type-1 Fuzzy Logic: Theory, Algorithms and Applications)
The main goal of this paper is to improve the performance of the Fireworks Algorithm (FWA). To improve the performance of the FWA we propose three modifications: the first modification is to change the stopping criteria, this is to say, previously, the number of function evaluations was utilized as a stopping criteria, and we decided to change this to specify a particular number of iterations; the second and third modifications consist on introducing a dispersion metric (dispersion percent), and both modifications were made with the goal of achieving dynamic adaptation of the two parameters in the algorithm. The parameters that were controlled are the explosion amplitude and the number of sparks, and it is worth mentioning that the control of these parameters is based on a fuzzy logic approach. To measure the impact of these modifications, we perform experiments with 14 benchmark functions and a comparative study shows the advantage of the proposed approach. We decided to call the proposed algorithms Iterative Fireworks Algorithm (IFWA) and two variants of the Dispersion Percent Iterative Fuzzy Fireworks Algorithm (DPIFWA-I and DPIFWA-II, respectively). View Full-Text
Keywords: fuzzy logic; dynamic adaptation; dispersion percent; FWA; IFFWA; DPIFWA fuzzy logic; dynamic adaptation; dispersion percent; FWA; IFFWA; DPIFWA
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MDPI and ACS Style

Barraza, J.; Melin, P.; Valdez, F.; Gonzalez, C.I. Fuzzy Fireworks Algorithm Based on a Sparks Dispersion Measure. Algorithms 2017, 10, 83.

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