Next Generation Metaheuristic: Jaguar Algorithm

• Published in: IEEE access Vol. 6; pp. 9975 - 9990
• Main Authors: Chou, Yao-Hsin, Kuo, Shu-Yu, Yang, Li-Sheng, Yang, Chia-Yun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Animals; Approximation algorithms; Cloning; dependency problem; Exploitation; Exploration; function optimization problem; Heuristic algorithms; Heuristic methods; jaguar algorithm (JA); Metaheuristic algorithms; Optimization; Parameters; Sociology; Statistics
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2797059

Metaheuristic algorithms are implemented to solve optimization problems and have recently received significant research attention. Metaheuristic algorithms rely primarily on two properties, exploration, and exploitation. Traditional metaheuristic algorithms use many weights (parameters) to balance these two properties to increase the chance of finding a better solution in limited cost and time. However, traditional algorithms have some problems. Exploration and exploitation are different abilities and restrict each other, therefore, traditional algorithms need many parameters and lots of costs to achieve the balance, and also need to adjust parameters for different optimization problems. Jaguar Algorithm (JA) has great abilities both in exploitation and exploration, is proposed to address these issues. First, JA attempts to find the optimal solution in the designated search area. It then uses history information to jump to a better area. JA can, therefore, determine the position of the global optimum. JA achieves strong exploitation and exploration with these features. Also, according to different problems, JA implements adaptive parameter adjustment. The self-analysis and experiment of this research demonstrate that each JA capability can have various positive effects, while the performance comparison demonstrates JAs superiority over traditional metaheuristic algorithms.