The concept of Bi-level programming problem (BLPP) is an optimization problem composed of two hierarchical optimization problems that are interconnected. BLPP is one of the most challenging problems faced by the optimization community. It is used to model decentralized decision-making. It is composed of both high-level and low-level objectives. This work suggests a hierarchical Ecosystem based- Optimization (HAEO) algorithm for solving general BLPPs. The proposed method is a hierarchical algorithm framework that solves universal bilevel programming problems simply by simulating how bilevel programming makes decisions. This is different from most traditional algorithms, which are made for specific versions or based on particular assumptions. The solution for general BLPPs can be transformed to solve the upper-level and lower-level problems sequentially by two phases of AEO. AEO phases are designed to deal with both upper and lower-level constrained optimization problems. The suggested technique enables the decision-maker at an upper level to make a suitable choice in consideration of a lower level's response. The proposed algorithm is characterized by its ability to solve the lower-level problem with a minimal number of iterations, resulting in improved algorithm performance and accelerated convergence towards the solution. The suggested method's performance is evaluated by comparing the results to those obtained using a genetic algorithm (GA), the Lingo algorithm, and the particle swarm optimization (PSO) algorithm on four problems from the literature, a supply chain model, and seven benchmark problems. The results indicate that the AEO algorithm outperforms the GA and PSO methods regarding accuracy, stability, and results. So, the suggested algorithm is a competitive approach for solving general BLPP problems.

Artificial Ecosystem based- Optimization; bilevel Programming; Meta-heuristic