Priyanka Sinha^1,, Pritam Roy²

This study introduces an adaptive intelligent agent-based flog leaping optimizer to tackle the economic load dispatch problem in power systems, specifically addressing valve-point effects. Unlike conventional non-traditional algorithms, it offers a more dynamic and deterministic problem-solving strategy, characterized by its simplicity, usability, convergence efficiency, solution quality, and robustness. To enhance the performance of the shuffled frog leaping algorithm (SFLA), which may suffer from slow exploration in later iterations and susceptibility to local optima, this paper proposes the fusion of Adaptive multi-agent-based evolutionary reinforcement learning with the leaping algorithm. This hybrid approach capitalizes on the complementary strengths of both algorithms. By leveraging this synergy, this method demonstrates superior performance, achieving optimal results with reduced global and local iterations and it also limits the stochastic approach. The proposed hybrid methodology and its variations are rigorously evaluated using two distinct test systems, including 13 and 40 thermal unit systems with incremental fuel cost functions considering valve-point effects. The experimental results demonstrate the efficacy and promise of the proposed approach, outperforming several benchmark techniques commonly used in the field.

Economic load dispatch, Evolutionary Algorithm, Reinforcement learning