Multi Access Interference (MAI) is the main source limiting the capacity and quality of the Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system which fulfills the demand of high-speed transmission rate and high quality of service for future underwater acoustic (UWA) communication. Multi User Detection (MUD) is needed to overcome the performance degradation caused by MAI. In this research, both local and global optimal solutions are obtained in Bionic Binary Spotted Hyena Optimizer (BBSHO) algorithm using the Position Coordinate Vectors (PCVs) of the social behavior of spotted hyenas to achieve MUD. Further, Extremal Optimization (EO) is introduced in BBSHO algorithm to improve the local search ability within the search space. Hence, a hybrid BBSHO algorithm is proposed for achieving MUD at the receiver of the MIMO-OFDM system whose transceiver model in underwater is implemented using BELLHOP simulation system. By MATLAB simulation, it is shown that the Bit Error Rate (BER) performance of the proposed hybrid algorithm outperforms with best optimal solution within the search space towards MUD for Interference to Noise Ratio (INR) at 10 dB, 20 dB, and 40 dB over conventional detectors and metaheuristic approaches such as Binary Spotted Hyena Optimizer (BSHO), Binary Particle Swarm Optimization (BPSO) in the UWA network.

多址干扰（MAI）是限制多输入多输出正交频分复用（MIMO-OFDM）系统的容量和质量的主要来源，它满足了未来对高速传输速率和高质量服务的需求水下声学（UWA）通信。需要多用户检测（MUD）来克服由MAI引起的性能下降。在这项研究中，使用斑点鬣狗的社会行为的位置坐标向量（PCV）在仿生二进制斑点鬣狗优化器（BBSHO）算法中获得了局部和全局最优解，以实现MUD。此外，在BBSHO算法中引入了极值优化（EO），以提高搜索空间内的局部搜索能力。因此，提出了一种混合BBSHO算法，用于在MIMO-OFDM系统的接收器上实现MUD，该MIMO-OFDM系统的水下收发器模型是使用BELLHOP仿真系统实现的。通过MATLAB仿真，结果表明，在10 dB，20 dB和40 dB的干扰噪声比（INR）的情况下，所提出的混合算法的误码率（BER）性能优于针对MUD的搜索空间内的最佳解决方案。 UWA网络中的常规检测器和元启发式方法（例如，二进制斑点鬣狗优化器（BSHO），二进制粒子群优化（BPSO））。