Enhanced Growth Optimizer and Its Application to Multispectral Image Fusion.

The growth optimizer (GO) is an innovative and robust metaheuristic optimization algorithm designed to simulate the learning and reflective processes experienced by individuals as they mature within the social environment. However, the original GO algorithm is constrained by two significant limitations: slow convergence and high memory requirements. This restricts its application to large-scale and complex problems. To address these problems, this paper proposes an innovative enhanced growth optimizer (eGO). In contrast to conventional population-based optimization algorithms, the eGO algorithm utilizes a probabilistic model, designated as the virtual population, which is capable of accurately replicating the behavior of actual populations while simultaneously reducing memory consumption. Furthermore, this paper introduces the Lévy flight mechanism, which enhances the diversity and flexibility of the search process, thus further improving the algorithm's global search capability and convergence speed. To verify the effectiveness of the eGO algorithm, a series of experiments were conducted using the CEC2014 and CEC2017 test sets. The results demonstrate that the eGO algorithm outperforms the original GO algorithm and other compact algorithms regarding memory usage and convergence speed, thus exhibiting powerful optimization capabilities. Finally, the eGO algorithm was applied to image fusion. Through a comparative analysis with the existing PSO and GO algorithms and other compact algorithms, the eGO algorithm demonstrates superior performance in image fusion. [ABSTRACT FROM AUTHOR]