Heuristic engineering optimization method based on convergence trajectory control

Abstract

The invention puts forward a huristic engineering optimization method based on convergence trajectory control. The method comprises following steps: bringing forward a convergence trajectory equation function set for an optimization method; establishing a convergence prediction calculation method for the optimization method; dynamically adjusting parameters to ensure that actual convergence value is the same as the design convergence value of the optimization method. The huristic engineering optimization method based on convergence trajectory control has following beneficial effects: without parameter setting, high optimization efficiency is obtained and good search quality is achieved; generally speaking, the invention is a novel huristic engineering technology and innovative and of great scientific meaning and value for enrichment and improvement of and huristic engineering optimization method, theory and application.

Description

technical field [0001] The invention relates to the field of engineering optimization. Background technique [0002] People often encounter optimization problems in many fields such as engineering technology, scientific research, and economic management. It refers to finding a set of parameter values ​​to maximize or minimize the objective function under certain constraints. For these optimization problems, traditional analytical algorithms such as Newton's method, conjugate gradient method and Lagrange multiplier method can quickly find the local optimum. However, with the expansion of human understanding and transformation of the world, the actual optimization problems have become more complex, usually with the characteristics of high dimensionality, strong constraints, prominent discretization and nonlinear relationships, and difficult modeling. Analytical local optimization algorithms It can no longer meet people's needs, and it is urgent to find intelligent optimizatio...

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Problems solved by technology

[0005] The above-mentioned improved technologies may alleviate the application difficulties of heuristic optimization methods in solving a certain type of engineering problems to a certain extent, but there are still many limitations, especially when solving large-scale and complex engineering optimization problems. For example, complex engineering problems usually have Analytical algorithm modeling is difficult, and the automatic adjustment of parameters according to the quality of the optimized solution has the phenomenon of premature algorithm, etc.

Another obvious shortcoming of the current improved heuristic optimization method is that the improvement of its search quality usually requires a long calculation time (number of iterations), such as a small time budget (especially when solving large-scale optimization problems) , the search quality of the modified heuristic optimization method is even worse than that of the conventional heuristic optimization technique

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