Exponential Distribution Optimizer (EDO)

EDO is a novel math-Inspired Algorithm for Global Optimization and Engineering Problems

The main inspiration for EDO comes from mathematics based on the exponential probability distribution model. At the outset, a population of random solutions representing multiple exponential distribution models is initialized. The positions in each solution represent the exponential random variables. The proposed algorithm includes two methodologies for exploitation and exploration strategies. For the exploitation stage, the algorithm utilizes three main concepts, memoryless property, guiding solution and the exponential variance among the exponential random variables to update the current solutions. EDO is tested against classical test functions in addition to the Congress on Evolutionary Computation (CEC) 2014, CEC 2017, CEC 2020 and CEC 2022 benchmarks, as well as six engineering design problems. EDO is compared with the winners of CEC 2014, CEC 2017 and CEC 2020, which are L-SHADE, LSHADE−cnEpSin and AGSK, respectively. EDO reveals exciting results and can be a robust tool for CEC competitions. Statistical analysis demonstrates the superiority of the proposed EDO at a 95% confidence interval.

Main Paper: Abdel-Basset, M., El-Shahat, D., Jameel, M., & Abouhawwash, M. (2023). Exponential distribution optimizer (EDO): a novel math-inspired algorithm for global optimization and engineering problems. Artificial Intelligence Review, 1-72.‏ DOI: https://doi.org/10.1007/s10462-023-10403-9