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FOA: fireworks optimization algorithm
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DOI：10.1007/s11042-022-13093-7.png)
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Abstract
En 中文
In this paper, a novel meta-heuristic algorithm called Fireworks Optimization Algorithm (FOA) is introduced with few control parameters for discrete and continuous optimization problems. This algorithm is inspired from explosion pyrotechnic devices producing colorful spikes like red, blue and silver. By modelling the explosion behavior of the Fireworks in the sky, the search space can be swept efficiently to find the global optima. To improve the balance between the exploration and exploitation of individuals, three categories are defined to avoid local optimal traps and applied to the search agents. Each category has a different task and predefined updating position rules. A grouping strategy is considered to prevent the algorithm from premature convergence. The performance of FOA is demonstrated over 15 standard benchmarks in the continuous version and 30 images thresholding problems in the discrete version. The obtained results reveal the superiority of the proposed algorithm with fewer input parameters over other state-of-the-art optimization methods in most cases.
Keywords:
Fireworks optimization algorithm
Bio-inspired algorithm
Benchmarked functions
meta-heuristic technique
Image segmentation
Engineering applications
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