A Novel Method Based on Nonlinear Binary Grasshopper Whale Optimization Algorithm for Feature Selection

Feature Selection(FS)is considered as an important preprocessing step in data mining and is used to remove redundant or unrelated features from high-dimensional data.Most optimization algorithms for FS problems are not balanced in search.A hybrid algorithm called nonlinear binary grasshopper whale optimization algorithm(NL-BGWOA)is proposed to solve the problem in this paper.In the proposed method,a new position updating strategy combining the position changes of whales and grasshoppers population is expressed,which optimizes the diversity of searching in the target domain.Ten distinct high-dimensional UCI datasets,the multi-modal Parkinson's speech datasets,and the COVID-19 symptom dataset are used to validate the proposed method.It has been demonstrated that the proposed NL-BGWOA performs well across most of high-dimensional datasets,which shows a high accuracy rate of up to 0.9895.Furthermore,the experimental results on the medical datasets also demonstrate the advantages of the proposed method in actual FS problem,including accuracy,size of feature subsets,and fitness with best values of 0.913,5.7,and 0.0873,respectively.The results reveal that the proposed NL-BGWOA has comprehensive superiority in solving the FS problem of high-dimensional data.

Multiuser Detection for MIMO-OFDM system in Underwater Communication Using a Hybrid Bionic Binary Spotted Hyena Optimizer

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.

An Effective Hybridization of Quantum-based Avian Navigation and Bonobo Optimizers to Solve Numerical and Mechanical Engineering Problems

Hybrid metaheuristic algorithms play a prominent role in improving algorithms' searchability by combining each algorithm's advantages and minimizing any substantial shortcomings. The Quantum-based Avian Navigation Optimizer Algorithm (QANA) is a recent metaheuristic algorithm inspired by the navigation behavior of migratory birds. Different experimental results show that QANA is a competitive and applicable algorithm in different optimization fields. However, it suffers from shortcomings such as low solution quality and premature convergence when tackling some complex problems. Therefore, instead of proposing a new algorithm to solve these weaknesses, we use the advantages of the bonobo optimizer to improve global search capability and mitigate premature convergence of the original QANA. The effectiveness of the proposed Hybrid Quantum-based Avian Navigation Optimizer Algorithm (HQANA) is assessed on 29 test functions of the CEC 2018 benchmark test suite with different dimensions, 30, 50, and 100. The results are then statistically investigated by the Friedman test and compared with the results of eight well-known optimization algorithms, including PSO, KH, GWO, WOA, CSA, HOA, BO, and QANA. Ultimately, five constrained engineering optimization problems from the latest test suite, CEC 2020 are used to assess the applicability of HQANA to solve complex real-world engineering optimization problems. The experimental and statistical findings prove that the proposed HQANA algorithm is superior to the comparative algorithms.