Solving complex problems in today’s engineering world is highly challenging. To solve these problems with maximization and minimization process optimization algorithm is necessary. These optimization algorithms were used in different applications and are inspired from the different part of nature’s problem-solving capacity. Some of them are solely effective but when they are hybrid the advantages of these algorithm increase tremendously. For this purpose, this study proposed a hybrid Multiple Adaptative Differential Evolution (MadDE) and Earthquake dynamics-based Optimization (EDBO) algorithm. This hybrid improves search diversity, convergence speed, and solution precision in challenging optimisation tasks by utilising EBO′s potent exploration prompted by seismic activity and MadDE′s efficient exploitation through adaptive mutation. The iteration event is executed in such a way that even iterations were explored and odd iterations were exploited. The proposed EDBO_MadDE algorithm was found to have a better score (95.024) than the existing algorithms. It ranked 2nd among algorithms of CEC2021 like DEDMNA, J21, MLS_SHADE, NL_SHADE, MadDE, EDBO and had a better rank and score than these algorithms. The mean finish time of the Hybrid EDBO-MadDE algorithm, which calculates 200,000 F18 evaluations before reaching a solution, is also presented for D = 10 and D = 20, which is 19.01496 and 51.767845 respectively. Overall, this algorithm performed better in solving shifted, rotated, hybrid functions and complex real-time problems.