Method for improving standard shuffled frog leaping algorithm

Abstract

The invention discloses a method for improving a standard shuffled frog leaping algorithm.The method comprises the steps of initializing parameters; calculating the adaptive value of each frog individual, and finding the adaptive value and position of the global optimum frog individual of a frog population; conducting optimum drawdown ranking on the frog population; conducting dividing for obtaining frog sub-populations; finding the positions of the optimum and the worst frog individual of each frog sub-population; conducting updating operation on the position of the worst frog individual of each frog sub-population; calculating the adaptive value of the frog individual with the position updated in each frog sub-population, and finding the global optimum adaptive value and the position of the frog population at this moment; implementing prediction of the global optimum adaptive value of the frog population obtained after iteration is completed next time, and furthermore adjusting the movement step-length variable coefficient dj and skip among steps; judging whether the ending conditions are met or not.By means of the method, the defects that at the later stage, the convergence rate of the standard shuffled frog leaping algorithm is severely lowered, convergence precision is insufficient, and the algorithm is prone to getting into local optimum are overcome.

Description

technical field [0001] The technical scheme of the present invention relates to a computer-aided design data processing method, specifically a method for improving the standard hybrid leapfrog algorithm. Background technique [0002] Many data processing problems in many technical fields need to be modeled to solve, and finally will be transformed into function optimization problems. Therefore, the application of function optimization is becoming more and more common and important. In 2003, Eusuff et al. combined memetic algorithm and particle swarm optimization algorithm to propose a brand-new function optimization algorithm—hybrid leapfrog algorithm. The number is small, the robustness is strong, the implementation is simple and the execution efficiency is high, especially its hybrid mechanism is very helpful for jumping out of the local optimum and ensuring that the algorithm converges to the global optimum. This algorithm has been used in the computer field and many pro...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a method for improving the standard hybrid leapfrog algorithm, which combines the population mixing mechanism in the standard hybrid leapfrog algorithm with the internal iteration mechanism of the subpopulation, and dynamically adjusts the moving step length , with the aid of MicrosoftVisualC++ computer software to implement the improved algorithm, it overcomes the shortcomings of the standard hybrid leapfrog algorithm in the prior art that the convergence speed is seriously slowed down in the later stage of evolution, the convergence accuracy is insufficient, and it is easy to fall into local optimum.

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