In the pursuit of enhancing Wireless Sensor Networks (WSNs), this study introduces a novel amalgamation of the Enhanced Shuffled Frog Leaping Algorithm (ESFLA) with a multi-solution evolution paradigm. By intricately examining diverse algorithmic facets, including partitioning strategies, fitness functions, and convergence mechanisms, the research endeavors to elevate the efficiency, robustness, and longevity of WSNs. Rigorous experimentation across 15 input datasets, meticulously categorized based on network density, unveils profound insights into the algorithm's performance. Significantly, the proposed ESFLA-MSU achieves exceptional outcomes, eclipsing traditional methods. A pioneering fitness function optimally redistributes workloads, culminating in extended network lifespans, a striking reduction in energy consumption by up to 28.5%, and remarkable load balancing improvements of up to 35.7%. Comparative analyses of partitioning strategies underscore ESFLA's adaptability, while multi-solution evolution integration accelerates convergence, with an expedited rate of up to 46.3%.