Virtual Multi-Task Biological Search Algorithm Based On Improved Grey Wolf Algorithm

This research presents a novel virtual multi-mission search algorithm that synergizes an enhanced Grey Wolf Optimization (GWO) protocol with a Sine-Cosine Approach (SCA). As optimization algorithms gain traction in addressing complex, multifaceted problems, it becomes crucial to enhance their efficacy and adaptability. The classic GWO algorithm is inspired by the social behaviors and hunting strategies of grey wolves. Although the traditional GWO method has shown remarkable results in many optimization problems, its limitations in handling global search and avoiding local minima should not be ignored. With this in mind, we propose an innovative strategy, SCA, that leverage the periodic properties of sine and cosine functions to enhance global exploration and local development. With SCA, a single optimizer can dynamically adjust search parameters, thereby avoiding local optimizations more stably and enhancing overall optimization performance. This improved GWO strategy addresses the virtual multi-objective optimization problem with a task delegation strategy to ensure that each task receives balanced attention. The cooperative search mechanism among different Wolf groups promotes information sharing and efficient coordination among multiple tasks, which reflects the cooperative optimization ability of the optimization process. In summary, this integrated strategy not only improves the efficiency of optimization, but also demonstrates the application possibility of advanced algorithms in dealing with complex practical problems.