Synergistic Swarm Optimization Algorithm
作者机构：Faculty of Computer Sciences and InformaticsAmman Arab UniversityAmman11953Jordan Computer Science DepartmentAl Al-Bayt UniversityMafraq25113Jordan Department of Electrical and Computer EngineeringLebanese American UniversityByblos13-5053Lebanon Hourani Center for Applied Scientific ResearchAl-Ahliyya Amman UniversityAmman19328Jordan MEU Research UnitMiddle East UniversityAmman11831Jordan Applied Science Research CenterApplied Science Private UniversityAmman11931Jordan School of Computer SciencesUniversiti Sains MalaysiaPulau Pinang11800Malaysia School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Department of Industrial EngineeringCollege of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia College of EngineeringAl Ain UniversityAbu Dhabi112612United Arab Emirates Department of Civil and Architectural EngineeringUniversity of MiamiCoral Gables1251USA School of Information EngineeringSanming UniversitySanming365004China
出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)
年 卷 期：2024年第139卷第6期
页 面：2557-2604页
核心收录：
学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：King Saud University for funding this research through Researchers Supporting Program Number(RSPD2023R704) King Saud University Riyadh Saudi Arabia
主 题：Synergistic swarm optimization algorithm optimization algorithm metaheuristic engineering problems benchmark functions
摘 要：This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search *** cooperation enhances the exploitation of promising regions in the search space while maintaining exploration ***,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and *** leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization *** effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design *** experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and *** codes of SSOA are available at:https://***/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.
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