Adaptive cuckoo and fireworks hybrid algorithm-based pressure container optimization design method

Abstract

The invention discloses an adaptive cuckoo and fireworks hybrid algorithm-based pressure container optimization design method, and belongs to the field of improvement and application of intelligent optimization algorithms. On one hand, the search step length of cuckoo can be adjusted according to a gap between current and optimal environmental adaptation values of a bird nest, and a discovery probability of bird eggs is calculated according to a standard deviation of adaptation values of individuals, so that the search efficiency of a population is improved; and on the other hand, a ''segmented value-taking'' method is adopted for the explosion radius in a fireworks algorithm, the number of sparks is determined according to a search range of an explosion point, and the individuals can perceive a gap between the current explosion point and the optimal value, so that the fireworks algorithm can perform a jumping cross-regional search. Two sub-populations that evolve independently are fused with each other through a fixed number of generations, so that information communication among different individuals can be enhanced. The method combines the advantages of two intelligent algorithms and has a good optimization effect in pressure container optimization design.

Description

technical field [0001] The invention belongs to the field of improvement and application of an intelligent optimization algorithm, and relates to two novel self-adaptive optimization algorithms and a cuckoo-firework mixed algorithm of multi-population parallel evolution. Background technique [0002] Pressure vessel design has always been a difficult problem in the application of optimization algorithms. Due to various factors such as the complex structure of the vessel, many influencing factors, and irregular feasible regions, the ideal structural parameters cannot be obtained by using traditional optimization algorithms. Therefore, some intelligent algorithms are gradually applied to this problem, such as genetic algorithm and differential evolution algorithm; however, with the development of modern industrial technology, technicians have higher requirements for the accuracy and error of container design, while the basic intelligent algorithm It has been unable to meet peo...

AI Technical Summary

Problems solved by technology

However, the basic cuckoo algorithm and fireworks algorithm are both greedy and random search algorithms, which can better solve high-dimensional, unimodal function optimization problems, but the parameters of the basic intelligent algorithm are fixed during the evolution process. The individual's self-awareness and learning ability are low. For multi-modal and multi-variable objective functions, a single algorithm cannot jump out of the local optimal solution, and the fixed parameter settings in the algorithm also reduce the individual's adaptability to different search environments. For some objective functions, the optimization accuracy is not high

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