Guided learning strategy: A novel update mechanism for metaheuristic algorithms design and improvement

Meta-heuristic algorithms (MH) are naturally inspired global optimization algorithms. They are often relatively simple and can solve problems in a short period of time, with certain benefits. However, as the problem becomes more complex, the solution that the algorithm can obtain is often not the optimal solution to the problem, which limits its application scenarios. Therefore, improving the performance and solving accuracy of existing algorithms is crucial for expanding their application ability. In traditional optimization algorithms, there are often two concepts, namely exploration and exploitation. Exploration refers to a wide range of discrete search, used to avoid falling into local optima, and exploitation refers to a small range of focused exploration, used to improve algorithm accuracy. How to balance exploration and exploitation is the key to enhancing algorithm performance and problem adaptability. This paper proposes a brand new strategy named Guided Learning Strategy (GLS) to solve above problem. GLS obtains the dispersion degree of the population by calculating the standard deviation of the historical locations of individuals in recent generations, and infers what guidance the algorithm currently needs. When the algorithm is biased towards exploration, it will guide the algorithm to exploit. Otherwise, it will guide the algorithm to explore. It is precisely because this strategy can identify the current needs of the algorithm and provide assistance that it can improve the performance of most algorithms. This article improves three types of algorithms based on evolution (LSHADE_SPACMA), Stochastic Fractal Search (SFS), and Marine Predators Algorithm (MPA) with better performance, and tests their performance on 57 constrained engineering problems and CEC2020. The effectiveness of this strategy has been confirmed and proved for optimization problem. The source codes of the proposed GLS (GLS_MPA) can be accessed by https://github.com/luchenghao2022/Guided-Learning-Strategy