## 1 Introduction  
The underwater sensor network consists of nodes equipped with wireless communication and information processing capabilities, which profoundly impact marine data acquisition and environmental monitoring [1]. Traditional underwater communication technologies include acoustic, electromagnetic, and optical technologies. However, acoustic technology has limitations such as multi-path effects, time-varying effects, low capacity, and other drawbacks [2]. Although underwater electromagnetic communication technology offers advantages such as high communication rates and high noise immunity, the high electrical conductivity of seawater causes severe signal attenuation and multi-path effects, which limit its application [3]. In contrast, underwater optical communication uses lasers as information carriers with ultra-high transmission bandwidth, high transmission rates, and low time delay. However, water bodies' significant absorption and scattering effects on optical communication can seriously affect the quality of communication [4].  
Magnetic induction (MI) is a wireless communication method that uses the transmitting antenna coil to generate a magnetic field and the receiving coil to generate an induced current. MI uses magnetic fields for communication, which has several advantages, including low power consumption, low latency, stable channels, multi-path effects [5], and the absence of Doppler effects. Research has shown that the magnetic permeability of MI is almost identical in soil, water, and air and that MI has practically no impact on communication when passing through the water-air interface [6]. The communication distance is approximately the same in all three environments, with slight variations in the channel [7]. Numerous applications for MI in underwater communications have been identified [8; 9]. Multi-input-multi-output (MIMO) can significantly increase transmission rates and spectral efficiency with limited bandwidth by increasing the number of antennas at the transmitting and receiving ends [10; 11]. However, signal detection in MIMO systems is challenging due to the mutual interference between different transmit antennas and the multi-path effects of the communication systems. Traditional signal detection algorithms such as the zero-forcing (ZF) algorithm [12], minimum mean square error (MMSE) [13], and maximum likelihood (ML) [14] fail to achieve a trade-off between performance and complexity in energy- and power-constrained underwater networks. The whale optimization algorithm (WOA) is a bionic method [15] that mimics the hunting behavior of humpback whales. WOA has been applied to wireless resource allocation [16; 17], wireless network energy clustering [18], cloud computing [19], and other areas. This paper presents a model of an underwater MI-MIMO communication system and develops an advanced whale optimization algorithm (AWOA) for signal detection. We attempt to optimize the signal detection of the MI-MIMO system based on AWOA. The main contributions of this paper are as follows:  
1. We propose a model of an underwater MIMO communication system based on magnetic induction technology;
2. We implement AWOA, an advanced whale optimization algorithm, to achieve signal detection in the proposed underwater MI-MIMO system;
3. We perform simulation experiments to compare the performance and complexity of AWOA with three standard signal detection algorithms, namely ZF, MMSE, and ML.  
The rest of the paper is organized as follows: Section 2 gives an overview of related work. Section 3 describes the details of AWOA for underwater MI-MIMO systems. Section 4 presents the experimental results and analysis. Finally, Section 5 concludes the paper.