A novel intelligent optimization algorithm inspired by nature, called sea otter optimization algorithm (SOOA), is proposed. The SOOA simulates the natural behaviors of sea otters, such as using tactile senses to search for food in seawater, grooming their fur, feeding with the aid of stones, and escaping from danger. In the exploration stage, a wetness factor is introduced to control the behavior of sea otters in foraging and grooming; a danger factor is introduced to control the behavior of sea otters in feeding and avoiding dangers in the exploitation stage, and the behaviors of sea otters in responding to different dangers are mathematically modeled. The proposed algorithm is compared with 9 well-known intelligent optimization algorithms, and evaluated in 13 benchmark functions as well as wireless sensor network coverage optimization problems to verify the effectiveness of the proposed algorithm. The experimental results show that the node coverage after SOOA optimization reaches 91.2% in 2D environment and 90.47% in 3D environment. Compared with other algorithms, SOOA is superior and possesses the ability to solve complex optimization problems.
Wu Jin, Gao Yaqiong, Su Zhengdong, Chong Gege, Xiong Hao
. Novel Sea Otter Optimization Algorithm for WSN Coverage Intelligence Optimization[J]. Journal of Shanghai Jiaotong University(Science), 2026
, 31(4)
: 828
-842
.
DOI: 10.1007/s12204-024-2765-5
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