International Journal of Machine Learning and Cybernetics ( IF 5.6 ) Pub Date : 2020-09-09 , DOI: 10.1007/s13042-020-01189-1 Gaurav Dhiman , Krishna Kant Singh , Adam Slowik , Victor Chang , Ali Riza Yildiz , Amandeep Kaur , Meenakshi Garg
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-dominated Pareto. 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 proposed EMoSOA algorithm 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 the Pareto which shows high convergence.
中文翻译:
EMoSOA:用于全局优化的新的进化多目标海鸥优化算法
本研究介绍了进化多目标海鸥优化算法(SOA),称为进化多目标海鸥优化算法(EMoSOA)。在该算法中,采用了动态归档概念,网格机制,领导者选择和遗传算子,并具有从非支配Pareto缓存解决方案的能力。轮盘赌法用于找到适当的存档解决方案。对提出的算法进行了测试,并与最新的24种标准基准测试功能的元启发式算法进行了比较。使用提议的EMoSOA验证了四个实际工程设计问题确定其适当性的算法。实证研究结果表明,该算法优于其他算法。它还考虑了帕累托算法中显示出高收敛性的最优解。