To efficiently solve Multi-dimensional Knapsack Problem （MKP） using Ring Theory-based Evolutionary Algorithm （RTEA）， after analyzing the inadequacies of existing repair operators： RO1 （based on the pseudo-utility ratio of items’ overall resource consumption） and RO3 （based on the value density across individual resource dimensions）， a new weighted repair optimization operator named RO4 was proposed by integrating complementary strategy. Additionally， an inheritance strategy was introduced to improve the global evolutionary operator of RTEA， and a self-adaptive reverse mutation operator suitable for MKP was proposed on the basis of Logistic model， along with a new algorithm IRTEA-RO4 for solving MKP. To validate its efficiency， IRTEA-RO4 was tested on 114 internationally recognized MKP benchmark instances and compared with six state-of-the-art algorithms for solving MKP. Experimental results demonstrate that for small-scale MKP instances， IRTEA-RO4 achieves the highest solution accuracy and fastest computation speed； for large-scale MKP instances， IRTEA-RO4 outperforms the best results of the six existing algorithms by 21% to 125% in solution quality， while also exhibiting superior average performance， enhanced stability， and faster computational speed.