The Electrical Storm Optimization (ESO) algorithm, inspired by the dynamic nature of electrical storms, is a novel population-based metaheuristic that employs three dynamically adjusted parameters: field resistance, intensity, and conductivity. Field resistance assesses the spread of solutions within the search space, reflecting strategy diversity. Field intensity balances the exploration of new territories and the exploitation of promising areas. Field conductivity adjusts the adaptability of the search process, enhancing the algorithm's ability to escape local optima.

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[1] Manuel Soto, Han Soo Lee, "Raw results of the ESO algorithm", IEEE Dataport, 2024. [Online]. Available: http://dx.doi.org/10.21227/jwxr-hn50. Accessed: Sep. 15, 2024.
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author = {Manuel Soto; Han Soo Lee },
publisher = {IEEE Dataport},
title = {Raw results of the ESO algorithm},
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Manuel Soto, Han Soo Lee. (2024). Raw results of the ESO algorithm. IEEE Dataport. http://dx.doi.org/10.21227/jwxr-hn50
Manuel Soto, Han Soo Lee, 2024. Raw results of the ESO algorithm. Available at: http://dx.doi.org/10.21227/jwxr-hn50.
Manuel Soto, Han Soo Lee. (2024). "Raw results of the ESO algorithm." Web.
1. Manuel Soto, Han Soo Lee. Raw results of the ESO algorithm [Internet]. IEEE Dataport; 2024. Available from : http://dx.doi.org/10.21227/jwxr-hn50
Manuel Soto, Han Soo Lee. "Raw results of the ESO algorithm." doi: 10.21227/jwxr-hn50