{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T08:29:08Z","timestamp":1776328148323,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T00:00:00Z","timestamp":1741219200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"JSPS Kakenhi","doi-asserted-by":"publisher","award":["24K00991"],"award-info":[{"award-number":["24K00991"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>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, field intensity, and field conductivity. Field resistance assesses the spread of solutions within the search space, reflecting strategy diversity. The field intensity balances the exploration of new territories and the exploitation of promising areas. The field conductivity adjusts the adaptability of the search process, enhancing the algorithm\u2019s ability to escape local optima and converge on global solutions. These adjustments enable the ESO to adapt in real-time to various optimization scenarios, steering the search toward potential optima. ESO\u2019s performance was rigorously tested against 60 benchmark problems from the IEEE CEC SOBC 2022 suite and 20 well-known metaheuristics. The results demonstrate the superior performance of ESOs, particularly in tasks requiring a nuanced balance between exploration and exploitation. Its efficacy is further validated through successful applications in four engineering domains, highlighting its precision, stability, flexibility, and efficiency. Additionally, the algorithm\u2019s computational costs were evaluated in terms of the number of function evaluations and computational overhead, reinforcing its status as a standout choice in the metaheuristic field.<\/jats:p>","DOI":"10.3390\/make7010024","type":"journal-article","created":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T09:59:17Z","timestamp":1741255157000},"page":"24","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Electrical Storm Optimization (ESO) Algorithm: Theoretical Foundations, Analysis, and Application to Engineering Problems"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4312-6300","authenticated-orcid":false,"given":"Manuel","family":"Soto Calvo","sequence":"first","affiliation":[{"name":"Coastal Hazards and Energy Sciences Laboratory, Transdisciplinary Science and Engineering Program, Graduate School and Engineering, Hiroshima University, Hiroshima 739-8529, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7749-0317","authenticated-orcid":false,"given":"Han Soo","family":"Lee","sequence":"additional","affiliation":[{"name":"Coastal Hazards and Energy Sciences Laboratory, Transdisciplinary Science and Engineering Program, Graduate School and Engineering, Hiroshima University, Hiroshima 739-8529, Japan"},{"name":"Center for Planetary Health and Innovation Science (PHIS), The IDEC Institute, Hiroshima University, Hiroshima 739-8529, Japan"},{"name":"Smart Energy School of Innovation and Practice for Smart Society, Hiroshima University, Hiroshima 739-8529, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Agushaka, J.O., and Ezugwu, A.E. 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