Search

In order to optimize the performance of the multi-verse algorithm to solve the optimal value of function, this paper proposed a global optimization multiverse algorithm（G-MVO） with an improved search mechanism. Aiming at the defects of the standard algorithm, which was easy to fall into local optimum and premature convergence due to the single search mechanism, this paper proposed three learning strategies to enhance the algorithm performance: through multi-strategy interaction and cooperation to reduce the complexity of the algorithm and improve the accuracy of the solution, designing adaptive parameters to dynamically choose the best strategy and optimizing the performance of the algorithm globally. In order to verify the effectiveness of the G-MVO algorithm, simulation experiments were carried out on 8 benchmark functions of different dimensions. Experimental results show that the proposed algorithm has better solution precision and convergence speed. [ABSTRACT FROM AUTHOR]

In order to solve the shortcoming of meta-heuristic mayfly optimization algorithm ( MOA ), such as low precision, slow convergence velocity and low stability, this paper proposed a new mayfly optimization algorithm, which combined the gold sine and adaptive merge. Firstly, it introduced the adaptive inertia weight factor to enhance the search and development ability of the algorithm to achieve a better balance. Then it introduced the fusion Levy flight strategy and the gold sine factor to further improve the shortcoming of fall into the local optimum, enhanced the population diversity and jumped out of the local optimum. Simulation results show that the solution precision, convergence velocity and optimization ability of the improved algorithms are significantly improved on the test functions. At the same time, in order to verify the reliability and validity of the results, this paper analyzed the data obtained by the improved algorithm on statistical test, mean absolute error and solution success rate. The algorithm has obvious improvement in the stability, reliability and robustness compared with the MOA. Besides, this paper introduced a specific engineer case to verify the applicability of the algorithm in engineering. [ABSTRACT FROM AUTHOR]

Aiming at the characteristics of the traditional cuckoo search algorithm with low convergence accuracy and large limitation of iterative step for complex problems, this paper proposed a dynamic cuckoo search strategy based on rider optimization. The algorithm combined with the idea of rider optimization algorithm, using multiple groups and optimal strategies in a single cycle . The optimal strategy could dynamically enhance the searching and improve the convergence efficiency of the algorithm for complex problems. At the same time, this algorithm adjusted the dynamic parameters of Levy flight motion to improve the early and late performance of the algorithm search. The simulation test results show that the optimization performance of the imp roved algorithm ROCS is better than the comparison algorithms for complex problems, and the optimization efficiency of the algorithm has been significantly improved. [ABSTRACT FROM AUTHOR]

To solve the problem that the ant lion optimization algorithm was prone to local optimal stagnation, low convergence accuracy and slow convergence speed, this paper proposed the ant lion optimization algorithm integrating with Lévy flight and golden sinusoids ( LGSALO). This algorithm used the variation mechanism of Lévy flight to carry out mutation operation on the position update mode in the optimization process, which could improve the population diversity, make the algorithm jump out of the local optimum, improve the global optimization ability, and avoid the premature convergence of the algorithm to some extent. At the same time, the algorithm introduced the golden sine algorithm to improve the optimization mode of the elite ant lion and coordinate the ability of global exploration and local development. The experimental simulation results show that the improved algorithm has good performance of optimization and strong development ability. [ABSTRACT FROM AUTHOR]