Global optimization algorithms, including the particle swarm optimization algorithm, have been widely used in solving complex optimization problems. However, particle swarm optimization frequently encounters the issue of premature convergence to local optima, which can result in suboptimal solutions. To enhance optimization capability, a multi-swarm asynchronous particle swarm optimizer (MSAPSO) with enhanced exploration and exploitation is proposed. MSAPSO divides the population into multiple subswarms, each of which executes its own mechanisms across three distinct phases. Moreover, asynchronous search strategies, including GPSO mechanism, asymptotic optimal guidance mechanism, exploration mechanism, and a Lévy-like mechanism, are designed to guide the velocity and position updates of particles in each subswarm asynchronously. This increases the exploration of more diverse search spaces while maintaining convergence towards potential solutions, significantly improving optimization performance. Experimental results on a variety of benchmark datasets demonstrate high search accuracy and competitive convergence speed, validating the efficacy of the proposed method.