An enhanced blood-sucking leech optimization for training feedforward neural networks

The input, hidden and output layers cultivate a hierarchical framework of the feedforward neural networks (FNNs) characterized by unidirectional information flow and feedback feedback-free loop connection, the network highlights attributes of fortified scalability and adaptability, elevated parallel computation and training efficiency, uncluttered structure and easy implementation. The blood-sucking leech optimization (BSLO) is predicated on the foraging patterns of blood-sucking leeches in rice paddies, which incorporates exploration, exploitation, switching mechanism of directional leeches, recherche mechanism of directionless leeches, and re-tracking mechanism to accomplish global coarse discovery and local elaborated extraction, and ascertain the fantastic solution. To expedite solution efficiency and reinforce mining precision, this paper proposes an enhanced BSLO with the simplex method (SBSLO) to train the FNNs, the objective is to quantify the discrepancy between anticipated output and realistic output, assess training efficacy and classification accuracy of prediction samples, and establish the fantastic connection weights and bias thresholds. Simplex method not only strengthens directional exploration precision and bolsters population diversity to mitigate premature convergence and facilitate escape from local optimum but also advances constraint processing capability and emphasizes noteworthy robustness and generalization to reinforce convergence procedure and elevate solution quality. The stability and dependability of the SBSLO are validated by seventeen sample datasets, and the SBSLO is compared with KOA, NRBO, HLOA, IAO, WO, PKO, EGO, HEOA, APO, FLO, PO and BSLO. The experimental results demonstrate that the SBSLO amalgamates the collective cooperative exploration of the BSLO with the refined directional exploitation of the simplex method to leverage complementary advantages, alleviate local search stagnation, boost training efficiency and prediction precision, strengthen stability and robustness, and foster convergence speed and solution quality.