Enhanced Hybrid Algorithms for Inverse Problem Solutions in Computed Tomography

This article presents a method for solving the inverse problem of computed tomography using an incomplete dataset. The problem focuses on reconstructing spatial objects based on the data collected from transmitters and receivers (referred to as projection vectors). The novelty of the proposed approach lies in combining two types of algorithms, namely heuristic and deterministic. Specifically, Artificial Bee Colony (ABC) and Jellyfish Search (JS) algorithms were utilized and compared as heuristic methods, while the deterministic methods were based on the Hooke–Jeeves (HJ) and Nelder–Mead (NM) approaches. By merging these techniques, a hybrid algorithm was developed, integrating the strengths of both heuristic and deterministic algorithms. The proposed hybrid algorithm proved to be approximately five to six times faster than an approach relying solely on metaheuristics while also providing more accurate results. In the worst-case test, the fitness function value for the hybrid algorithm was approximately 22% lower than that of the purely metaheuristic-based approach. Experimental tests further demonstrated that the hybrid algorithm, whether based on Hooke–Jeeves or Nelder–Mead, was stable and well suited for solving the considered problem. The article includes experimental results that confirm the effectiveness, accuracy, and efficiency of the proposed method.