Eagle perching optimizer for the online solution of constrained optimization

The paper proposes a novel nature-inspired optimization technique called Eagle Perching Optimizer (EPO). It is an addition to the family of swarm-based meta-heuristic algorithms. It mimics eagles’ perching nature to find prey (food). The EPO is based on the exploration and exploitation of an eagle when it descends from the height such that it formulates its trajectory in a way to get to the optimal solution (prey). The algorithm takes bigger chunks of search space and looks for the optimal solution. The optimal solution in that chunk becomes the search space for the next iteration, and this process is continuous until EPO converges to the optimal global solution. We performed the theoretical analysis of EPO, which shows that it converges to the optimal solution. The simulation includes three sets of problems, i.e., uni-model, multi-model, and constrained real-world problems. We employed EPO on the benchmark problems and compared the results with state-of-the-art meta-heuristic algorithms. For the real-world problems, we used a cantilever beam, three-bar truss, and gear train problems to test the robustness of EPO and later made the comparison. The comparison shows that EPO is comparable with other known meta-heuristic algorithms.