Abstract: In antiaircraft weapon-target optimal assignment, the firepower resources are easy to waste and a combat opportunity could be missed. An air defense firepower improved optimal assignment model is constructed by combining damage probability threshold, flying time and threat degree. On this basis, a multi group parallel cuckoo algorithm (MPCSA) is proposed to solve the multi-dimensional optimization problem of air defense firepower. Multiple populations are used for global exploration and local development at the same time, and the migration operator is used to exchange information among different populations. In order to further improve the global search ability, Cauchy mutation operator is introduced to construct a new global search model. In the process of algorithm local development, the greedy method is applied to local development. The simulated results show that the weapon-target optimal assignment model can be used to effectively seize the opportunity for combat and avoid the waste of firepower resources. The proposed optimal algorithm can effectively balance the global exploration and local development, and the global exploration ability is improved while ensuring higher convergence speed. Key

Key words: weapon-targetassignment, damageprobabilitythreshold, flyingtime, cuckoosearchalgorithm, multiplepopulationsparallelsearch, Cauchymutationoperator, dimension-by-dimensiongreedysearch