The multiple chaos embedded gravitational search algorithm (CGSA-M) is an optimization algorithm that utilizes chaotic graphs and local search methods to find optimal solutions. Despite the enhancements introduced in the CGSA-M algorithm compared to the original GSA, it exhibits a pronounced vulnerability to local optima, impeding its capacity to converge to a globally optimal solution. To alleviate the susceptibility of the algorithm to local optima and achieve a more balanced integration of local and global search strategies, we introduce a novel algorithm derived from CGSA-M, denoted as CGSA-H. The algorithm alters the original population structure by introducing a multi-level information exchange mechanism. This modification aims to mitigate the algorithm’s sensitivity to local optima, consequently enhancing the overall stability of the algorithm. The effectiveness of the proposed CGSA-H algorithm is validated using the IEEE CEC2017 benchmark test set, consisting of 29 functions. The results demonstrate that CGSA-H outperforms other algorithms in terms of its capability to search for global optimal solutions.

Wingsuit flying search is a meta-heuristic algorithm that effectively searches for optimal solutions by narrowing down the search space iteratively. However, its performance is affected by the balance between exploration and exploitation. We propose a four-layered hierarchical population structure algorithm, multi-layered chaotic wingsuit flying search (MCWFS), to promote such balance in this paper. The proposed algorithm consists of memory, elite, sub-elite, and population layers. Communication between the memory and elite layers enhances exploration ability while maintaining population diversity. The information flow from the population layer to the elite layer ensures effective exploitation. We evaluate the performance of the proposed MCWFS algorithm by conducting comparative experiments on IEEE Congress on Evolutionary Computation (CEC) benchmark functions. Experimental results prove that MCWFS is superior to the original algorithm in terms of solution quality and search performance. Compared with other representative algorithms, MCWFS obtains more competitive results on composite problems and real-world problems.

Many optimisation algorithms improve the algorithm from the perspective of population structure. However, most improvement methods simply add hierarchical structure to the original population structure, which fails to fundamentally change its structure. In this paper, we propose an umbrellalike hierarchical artificial bee colony algorithm (UHABC). For the first time, a historical information layer is added to the artificial bee colony algorithm (ABC), and this information layer is allowed to interact with other layers to generate information. To verify the effectiveness of the proposed algorithm, we compare it with the original artificial bee colony algorithm and five representative meta-heuristic algorithms on the IEEE CEC2017. The experimental results and statistical analysis show that the umbrellalike mechanism effectively improves the performance of ABC.