A multi-strategy based golden jackal optimization algorithm is proposed to address the problems of poor population quality, slow convergence speed and easy to fall into local extremes faced by the golden jackal optimization algorithm in solving constrained optimization problems. First, in order to increase the diversity of the population and improve the quality of the initial solution, a chaotic elite collaborative initialization strategy is used to generate an elite population; then， an energy regulation mechanism is introduced to coordinate the global search and local optimization; finally, a fusion mutation method is designed for the individual differences in the population in order to prevent the problem of local extremes. The improved algorithm is proved to have better convergence performance and faster convergence speed through the comparison test of standard test functions. In addition, experiments on the CEC2021 test function and the pressure vessel design optimization problem further demonstrate the effectiveness and practicality of the improved golden jackal optimization algorithm in single-objective constraints and multi-objective constraints problems through convergence analysis, robustness test, and validation of Wilcoxon′s rank sum statistics.