Abstract
Physical phenomena have been the inspiration for proposing different optimization methods such as electro-search algorithm, central force optimization, and charged system search among others. This work presents a new optimization algorithm based on some principles from physics and mechanics, which is called Evolutionary Centers Algorithm (ECA). We utilize the center of mass definition for creating new directions for moving the worst elements in the population, based on their objective function values, to better regions of the search space. The efficiency of the new approach is showed by using the CEC 2017 competition benchmark functions. We present a comparison against the best algorithm (jSO) in such competition and against a classical method (SQP) for nonlinear optimization. The results obtained are promising.
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Mejía-de-Dios, JA., Mezura-Montes, E. (2019). A New Evolutionary Optimization Method Based on Center of Mass. In: Deep, K., Jain, M., Salhi, S. (eds) Decision Science in Action. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-13-0860-4_6
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DOI: https://doi.org/10.1007/978-981-13-0860-4_6
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