<p>As a class of advanced algorithms for solving complex optimization problems, meta-heuristic algorithms have been utilized to tackle various optimization problems in recent years. A new meta-heuristic optimization algorithm, the Molecular Force Search Optimizer (MFSO), is proposed based on the law of molecular interactions. MFSO simulates the interaction laws between molecules, divides the search space into the equilibrium stage (exploitation), the attractive stage (exploration), and the repulsive stage (exploitation), and designs a balance circle and a distance ratio factor to dynamically transition the exploration and utilization stages in the search space. To verify the advancement and effectiveness of MFSO, 12 benchmark functions from the CEC 2022 test suite were used to evaluate it, and it was compared with 10 intelligent meta-heuristic optimization algorithms in recent years in different dimensions of 10, 30, and 50 dimensions. The experimental results prove that in the comparison of the average and optimal values of different dimensions, MFSO achieved the best results in 8, 6 and 7 test functions, respectively, demonstrating that MFSO has superior optimization capabilities in terms of convergence speed and solution accuracy. Meanwhile, the statistical test results of the Wilcoxon test and the Friedman rank test confirmed the significant advantages of MFSO in most cases. Furthermore, in six practical engineering problems, MFSO significantly outperformed other comparison algorithms in five of them, demonstrating its potential in solving complex practical optimization problems. In summary, the MFSO has established its practical value and advantages in solving various intricate optimization problems through its exceptional performance.</p>