Over the past few decades, the field of metaheuristics optimization has undergone rapid development. More than 650 different metaheuristic algorithms have been created so far. Among these, the Electric Eel Foraging Optimization (EEFO) stands out as a recently developed bio-inspired algorithm. To extend its application to binary optimization, particularly the 0-1 knapsack problem (KP01), we introduce a binary version of EEFO, termed EB-EEFO. The proposed algorithm employs two kinds of transfer functions, S-shaped and V-shaped, to shift from the continuous to binary search space. Additionally, we propose an enhanced repair and improvement (RI) method to address infeasible solutions, aiming to enhance solution diversity by reducing the inherent greedy nature of the traditional RI method. To Study the competitiveness of the new RI method, we develop the binary versions of five different algorithms using the RI method found in the literature, including, EEFO (B-EEFO), B-RIME, One-to-One-Based Optimizer (B-OOBO), Sinh Cosh Optimizer (B-SCHO), and Newton-Raphson-Based Optimizer (B-NRBO). The performance of these algorithms is evaluated using a dataset of 25 instances of KP01. The concluding remarks commend the performance of the EB-EEFO.