Multi-strategy collaborative optimization of gravitational search algorithm

To address the shortcomings of the gravitational search algorithm, such as its tendency to fall into local optima, slow convergence, and low solution accuracy, this paper proposes a gravitational search algorithm based on multi-strategy cooperative optimization. The proposed algorithm balances global exploration and local exploitation. In the early iterations, particle positions are primarily updated using the original gravitational force, preserving the inherent characteristics of the gravitational search algorithm. In the later stages, particles with better fitness values are updated using a globally optimal Lévy random walk strategy to enhance local search capabilities, while particles with poorer fitness values are updated using the sparrow algorithm follower strategy. This approach increases the exploration of the particles in unexplored local areas, further improving the local exploitation abilities of the algorithm. Finally, the lens-imaging opposition-based learning strategy generates opposite solutions for particles at different stages, increasing population diversity, expanding the search range, and enhancing the global search performance of the algorithm. An effectiveness analysis and algorithm comparison tests were carried out on 24 typical complex benchmark functions. The performance analysis results show that the multi-strategy collaborative optimization method effectively leverages both the global and local search abilities of the algorithm, improving the accuracy of its solutions and stability. Compared with other GSA-based algorithms and advanced intelligent algorithms, the proposed algorithm exhibits superior solution accuracy, convergence speed, and stability, making it an efficient GSA-based algorithm. In addition, the proposed algorithm was applied to three engineering design optimization problems to verify its applicability to real-world scenarios.