Sea-horse optimizer: a novel nature-inspired meta-heuristic for global optimization problems - Zhao, Shijie; Zhang, Tianran; Ma, Shilin; Wang, Mengchen (Applied intelligence (Dordrecht, Netherlands), 2023)
This paper proposes a novel swarm intelligence-based metaheuristic called as sea-horse optimizer (SHO), which is inspired by the movement, predation and breeding behaviors of sea horses in nature. In the first two stages, SHO mimics different movements patterns and the probabilistic predation mechanism of sea horses, respectively. In detail, the movement modes of a sea horse are divided into floating spirally affected by the action of marine vortices or drifting along the current waves. For the predation strategy, it simulates the success or failure of the sea horse for capturing preys with a certain probability. Furthermore, due to the unique characteristic of the male pregnancy, in the third stage, the proposed algorithm is designed to breed offspring while maintaining the positive information of the male parent, which is conducive to increase the population diversity. These three intelligent behaviors are mathematically expressed and constructed to balance the local exploitation and global exploration of SHO. The performance of SHO is evaluated on 23 well-known functions and CEC2014 benchmark functions compared with six state-of-the-art metaheuristic algorithms. Finally, five real-world engineering problems are utilized to test the effectiveness of SHO. The experimental results demonstrate that SHO is a high-performance optimizer and positive adaptability to deal with constraint problems. SHO source code is available from: https://www.mathworks.com/matlabcentral/fileexchange/115945-sea-horse-optimizer