In this modern world, we are encountered with numerous complex and emerging problems. The metaheuristic optimization science plays a key role in many fields from medicine to engineering, design, etc. Metaheuristic algorithms inspired by nature are among the most effective and fastest optimization methods utilized to optimize different objective functions to minimize or maximize one or more specific objectives. The use of metaheuristic algorithms and their modified versions is expanding every day. However, due to the abundance and complexity of various problems in the real world, it is always necessary to select the most proper metaheuristic method; hence, there is a strong need to create new algorithms to achieve our desired goal. In this paper, a new and powerful metaheuristic algorithm, called the coronavirus metamorphosis optimization algorithm (CMOA), is proposed based on metabolism and transformation under various conditions. The proposed CMOA algorithm has been tested and implemented on the comprehensive and complex CEC2014 benchmark functions, which are functions based on real-world problems. The results of the experiments in a comparative study under the same conditions show that the CMOA is superior to the newly-developed metaheuristic algorithms including AIDO,
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