Synergistic Swarm Optimization Algorithm

This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm (SSOA). The SSOA combines the principles of swarm intelligence and synergistic cooperation to search for optimal solutions efficiently. A synergistic cooperation mechanism is employed, where particles exchange information and learn from each other to improve their search behaviors. This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities. Furthermore, adaptive mechanisms, such as dynamic parameter adjustment and diversification strategies, are incorporated to balance exploration and exploitation. By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation, the SSOA method aims to achieve superior convergence speed and solution quality performance compared to other optimization algorithms. The effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design problems. The experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems, making it a promising tool for a wide range of applications in engineering and beyond. Matlab codes of SSOA are available at: https://www. mathworks.com/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.