Abstract—The increasing penetration of intermittent Renewable Energy Sources (RES), coupled with the decentralized architecture of modern power distribution networks, has introduced substantial challenges in maintaining system stability and ensuring power quality—particularly under fault conditions and nonlinear operating regimes. This paper proposes a dynamic self-healing model for smart distribution systems based on an Improved Whale Optimization Algorithm (IWOA), with the objective of simultaneously optimizing active power loss, voltage deviation, Total Harmonic Distortion (THD), and Phase Voltage Unbalance Ratio (PVUR). The proposed IWOA incorporates a nonlinear shrinking mechanism, discrete solution mapping, and a normalized, equally weighted multi-objective function structure. These enhancements significantly improve convergence behavior, solution accuracy, and optimization performance in complex combinatorial search spaces. The proposed framework is validated on a modified IEEE 33-bus distribution system featuring unbalanced topologies, harmonic disturbances, and distributed RES integration. Simulation results demonstrate that IWOA outperforms conventional metaheuristics such as Particle Swarm Optimization (PSO), Differential Evolution (DE), and the original WOA in terms of convergence speed, energy efficiency, and power quality enhancement. This study highlights a promising direction for advanced automated optimization strategies in resilient and sustainable energy distribution infrastructures.