Crocodile optimization algorithm for solving real-world optimization problems

Abstract

Abstract Nature-inspired bionic algorithms have become one of the most fascinating techniques in computational intelligence research, and have shown great potential in real-world challenging problems for their simplicity and flexibility. This paper proposes a novel nature-inspired algorithm, called the crocodile optimization algorithm (COA), which mimics the hunting strategies of crocodiles. In COA, the hunting behavior of crocodiles includes premeditation and waiting hunting. The premeditation behavior is an important hunting way for crocodiles to hide themselves from their prey and to explore more potential areas, and the waiting hunting behavior is another means by which crocodiles make surprise attacks on their prey that appears in their hunting range. The performance of the proposed COA is validated by comparing it with other competitor algorithms on 29 standard test functions and 5 real-world engineering optimization problems. The experimental results show that the comprehensive performance of COA outperforms both of its similar variants and most of other state-of-the-art algorithms, in terms of solution accuracy, robustness and convergence speed. Statistical tests also validate the potential applications of the proposed algorithm.

Keywords

• Metaheuristics
• Crocodile optimization algorithm (COA)
• Swarm intelligence
• Global optimization