A fireworks algorithm (FWA) is a recent swarm intelligence algorithm that is inspired\nby observing fireworks explosions. An adaptive fireworks algorithm (AFWA) proposes additional\nadaptive amplitudes to improve the performance of the enhanced fireworks algorithm (EFWA).\nThe purpose of this paper is to add opposition-based learning (OBL) to AFWA with the goal of further\nboosting performance and achieving global optimization. Twelve benchmark functions are tested in\nuse of an opposition-based adaptive fireworks algorithm (OAFWA). The final results conclude that\nOAFWA significantly outperformed EFWA and AFWA in terms of solution accuracy. Additionally,\nOAFWA was compared with a bat algorithm (BA), differential evolution (DE), self-adapting control\nparameters in differential evolution (jDE), a firefly algorithm (FA), and a standard particle swarm\noptimization 2011 (SPSO2011) algorithm. The research results indicate that OAFWA ranks the highest\nof the six algorithms for both solution accuracy and runtime cost.
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