Abstract
A novel Chaotic Grenade Explosion Algorithm (CGEA) is presented for global optimization. The GEA is a new meta-heuristic optimization developed based on the observation of a grenade explosion, in which the thrown pieces of shrapnel destruct the objects near the explosion location. Here different chaotic maps are utilized to improve solution search equation of the algorithm. Seven different chaotic maps are investigated. Comparing the new algorithm with the GEA demonstrates the superiority of the CGEA for the benchmark functions.
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