Search

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The standard whale optimization algorithm( WOA ) and some derivative algorithms are studied and experimented to solve the problem of poor results of some examples. It is proved that there is a zero searching preference trap in the " encircling" process of WOA. In addition, the unbalanced search characteristics of chaos optimization algorithm( COA) make it difficult to reconcile the chaos initial population and swarm intelligence optimization process. In order to improve the above defects, this paper selected two chaotic systems and bubble net hunting strategy, and designed a set of fusion optimization algorithm. The algorithm adopted the baseline adaptive oscillation group partition strategy based on fitness to guide the group behavior pattern, gave full play to the role of chaotic system, and balanced the exploration and convergence performance. It used the new algorithm to solve the general/improved examples and engineering application cases. It 's obviously that the performance of the algorithm is better than the contrast group algorithm,and there is no searching preference. [ABSTRACT FROM AUTHOR]

The simulated annealing algorithm with adaptive temperature rising strategy or Monte Carlo strategy has the problems of slow convergence and limited global optimal approximation ability in solving complex 'ISP respectively. On the other hand, the existing chaotic optimization algorithm has the defect of logistic mapping, which weakens its ability to jump out of local optima. Therefore, this paper designed a fusion algorithm framework, in which embedded the divided Lorenz chaotic mapping system to enhance the search efficiency of the chaotic algorithm for the neighborhood solution. It introduced the greedy strategy to construct the initial solution approaching the global optimal solution, which made the algorithm had the ability of transition to the neighborhood of the global optimal solution. In addition, it designed the complementary mechanism of oscillatory annealing to improve the sub iterative solution screening process, and enhanced the global search performance of the algorithm. After the implementation of the algorithm, using the international public 1SPLIB point set, through multiple rounds of comparative testing, it verifies that the performance of the new algorithm is better than the comparison group simulated annealing algorithm and logistic chaos optimization algorithm,and has shorter convergence time and stronger global optimal approximation ability. [ABSTRACT FROM AUTHOR]