This paper introduces the Adaptive Adjacency Distance Matrix-based Competitive Swarm Optimizer (AADMCSO), an enhanced version of the CSO algorithm that incorporates an adaptive competition strategy. AADMCSO dynamically adjusts its selection process using an adaptive factor, prioritizing neighbor matrix-based selection during the early exploration phase to maintain population diversity. As optimization progresses, the algorithm gradually increases the likelihood of random selection, which promotes convergence toward the optimal solution. This adaptive mechanism improves the efficiency of individual selection in the CSO algorithm and enables it to more effectively locate optimal solutions. Experimental results on 10- and 20-dimensional problems from the CEC2022 benchmark demonstrate that AADMCSO outperforms ten other optimization algorithms, achieving superior overall performance.