Fick’s Law Algorithm with Gaussian Mutation: Design and Analysis

In recent years, an increasing number of meta-heuristic algorithms have been employed to tackle real-life problems, leading to the successive emergence of numerous meta-heuristic algorithms. Fick’s Law Algorithm (FLA), introduced in 2022, is one such newly proposed meta-heuristic algorithm. FLA has exhibited strong performance in several optimization scenarios; nonetheless, similar to the majority of meta-heuristic algorithms, it is prone to falling into local optima. To mitigate this issue and enhance the algorithm’s capability to escape local optima, we improve FLA using Gaussian mutation, giving rise to the Fick’s law algorithm with Gaussian mutation (GM-FLA). In order to validate the performance of the proposed GM-FLA, we conduct comparative experiments between GM-FLA, SCA, WOA, and FLA using the benchmark functions from CEC2013 in dimensions 10D and 50D. Convergence curves were also plotted. The experimental results are demonstrated that GM-FLA exhibits promising performance across all dimensions and possesses favorable convergence characteristics. Therefore, the proposed GM-FLA effectively avoids local optima and demonstrates the capability to escape from local optima.