Integrated Whale Swarm and Neuro-Evolutionary Computing for Large-Scale Sparse Optimization Problems

Sparse optimization problems at a large scale present considerable difficulties in diverse fields, such as machine learning, data mining, and signal processing. The aim is to identify the most efficient solutions within expansive search spaces, while constraining the number of non-zero variables. The present study aims to tackle this obstacle by introducing an innovative methodology that integrates whale swarm optimization (WSO) with neuro-evolutionary computing (NEC). The WSO algorithm draws inspiration from the social behavior of humpback whales and presents a proficient approach for investigating and utilizing the search space. The integration of evolutionary algorithms with neural networks by NEC has demonstrated potential in effectively addressing intricate problems. Through the simultaneous evolution of neural networks and optimization processes, the algorithm is capable of adapting and enhancing its search strategy progressively. The study aims to improve the efficiency of WSO in addressing optimization problems characterized by sparsity at a significant scale by incorporating techniques from neuro-evolutionary computing. The method under consideration entails the integration of neural networks into the WSO algorithm with the aim of directing and augmenting the exploration procedure. The study evaluates the efficacy of the hybrid algorithm in relation to established methodologies based on criteria such as the quality of solutions, rate of convergence, and computational efficiency. The results obtained from the experiment provide evidence to support the efficacy of the proposed approach. The hybrid algorithm effectively utilizes the sparsity structure inherent in the problems, resulting in expedited convergence and enhanced precision of the solutions.