Abstract: Loss of diversity will lead to premature convergence in particle swarm optimization, which can lead to insufficient diversity and convergence of the swarm. To balance iversity and convergence, a multi-strategy competitive swarm optimization algorithm based on fusion index partition (FIMSCO) is presented. Firstly, a swarm partition technique using the fusion index is suggested, and the diversity and convergence of the sub-swarms are evaluated simultaneously, which enhances the search efficiency of the particles in the sub-swarms. While internal learning in sub-swarms, particle performance is merged with algorithm and a new winner particle learning method is proposed, which can guide the sub-swarms toward the more favorable balancing between the convergence and the diversity. And then the participation of the particles and the optimization efficiency of the algorithm are improved. While learning among the sub-swarms, to prevent the whole swarm from falling into the local optima, an improved triple-competition mechanism is introduced, which can promote the information exchange between the sub-swarms and can assist the improvement of the convergence accuracy. Finally, the real-time stagnation detection and the mutation strategy are designed to prevent the swarm from falling into the local optima. The convergence of the proposed algorithm is proved theoretically. Experimental results reveal that the proposed FIMSCSO exhibits excellent convergence accuracy and optimization efficiency compared with other algorithms.