Abstract
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).
Highlights
IntroductionComputer science is used for solving problems through its different areas (fuzzy logic, neural networks, evolutionary computing, among others)
At the present time, computer science is used for solving problems through its different areas
We presented the Iterative Fireworks Algorithm (IFWA), which is an improvement to the original fireworks algorithm (FWA), and other variations, such as DPIFFWA-I and DPIIFWA-II
Summary
Computer science is used for solving problems through its different areas (fuzzy logic, neural networks, evolutionary computing, among others). Depending on the type of problem, the aim may be minimizing or maximizing (optimization) the expected results, which is the main idea in this area. As with all things in life, the optimization algorithms have advantages and disadvantages. One of the disadvantages of these algorithms is that in early versions, or conventional versions, almost all the parameters are constant, that is, relevant parameters in the mathematical formulas of each algorithm remain statics during their performance. The performance of the exploration and exploitation into the algorithm is chaotic, that is, the static parameters do not allow for control of the balance between exploration and exploitation. Sometimes the algorithms just explore, and so, move away from the goal, or the algorithm could just exploit and become stagnate without allowing for a search for better solutions to achieve of the goal
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