A novel metaheuristic algorithm inspired by COVID-19 for real-parameter optimization.

Abstract

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, ITGO, RFOA, SCA, CSA, CS, SOS, GWO, WOA, MFO, PSO, Jaya, CMA-ES, GSA, RW-GWO, mTLBO, MG-SCA, TOGPEAe, m-SCA, EEO and OB-L-EO, indicating the effectiveness and robustness of the CMOA algorithm as a powerful algorithm. As it was observed from the results, the CMOA provides more suitable and optimized solutions than its competitors for the problems studied. The CMOA preserves the diversity of the population and prevents trapping in local optima. The CMOA is also applied to three engineering problems including optimal design of a welded beam, a three-bar truss and a pressure vessel, showing its high potential in solving such practical problems and effectiveness in finding global optima. According to the obtained results, the CMOA is superior to its counterparts in terms of providing a more acceptable solution. Several statistical indicators are also tested using the CMOA, which demonstrates its efficiency compared to the rest of the methods. This is also highlighted that the CMOA is a stable and reliable method when employed for expert systems.