EMoSOA: a new evolutionary multi-objective seagull optimization algorithm for global optimization

作     者：Dhiman, Gaurav Singh, Krishna Kant Slowik, Adam Chang, Victor Yildiz, Ali Riza Kaur, Amandeep Garg, Meenakshi 

作者机构：Govt Bikram Coll Commerce Dept Comp Sci Patiala 147001 Punjab India Delhi NCR Dept Elect & Commun Engn KIET Grp Inst Ghaziabad India Koszalin Univ Technol Dept Elect & Comp Sci Sniadeckich 2 PL-75453 Koszalin Poland Teesside Univ Sch Comp Engn & Digital Technol Middlesbrough Cleveland England Uludag Univ Dept Automot Engn Coll Engn TR-16059 Bursa Turkey Sri Guru Granth Sahib World Univ Dept Comp Sci & Engn Fatehgarh Sahib Punjab India 

出 版 物：《INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS》 (国际机器学习与控制论杂志)

年 卷 期：2021年第12卷第2期

页      面：571-596页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：VC Research [VCR 0000056] 

主　　题：Seagull Optimization Algorithm Multi-objective Optimization Evolutionary Pareto Engineering Design Problems Convergence Diversity 

摘      要：This study introduces the evolutionary multi-objective version of seagull optimization algorithm (SOA), entitled Evolutionary Multi-objective Seagull Optimization Algorithm (EMoSOA). In this algorithm, a dynamic archive concept, grid mechanism, leader selection, and genetic operators are employed with the capability to cache the solutions from the non-dominatedPareto. The roulette-wheel method is employed to find the appropriate archived solutions. The proposed algorithm is tested and compared with state-of-the-art metaheuristic algorithms over twenty-four standard benchmark test functions. Four real-world engineering design problems are validated using proposedEMoSOAalgorithm to determine its adequacy. The findings of empirical research indicate that the proposed algorithm is better than other algorithms. It also takes into account those optimal solutions from theParetowhich shows high convergence.