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Aiming at the path planning problem of mobile robot in dynamic environment, this paper proposes a fusion algorithm (IHHO-IDWA) of Improved Harris Hawk Algorithm (IHHO) and Improved Dynamic Window Algorithm (IDWA）. First of all, this paper proposed a fusion adaptive chaos and core population dynamic division strategy, a fusion golden sine strategy and a dynamic cloud optimal disturbance resolution strategy to improve the performance of and solve the lack of search performance in the late stage of the Harris Eagle algorithm. In addition, in view of the problems that the dynamic window algorithm has a long planned path and easy to fall into deadlock, it proposed three improvement strategies to solve them: Firstly, add sub-functions to ensure that the algorithm can plan a shorter path; secondly, propose an adaptive weight strategy, balance the local obstacle avoidance ability and global search performance of the algorithm; thirdly, set the initial heading angle to avoid path redundancy. Finally, through numerical experiments of test functions, CEC2014 functions, and static and dynamic path planning experiments, it is verified that the performance of IHHO and IDWA in this paper has been significantly improved; through the 50×50 large-scale dynamic map, it is verified that the path planned by the fusion algorithm is shorter than that planned by the control group algorithm. 11.51%, proving the superiority of the method in this paper. [ABSTRACT FROM AUTHOR]

Copyright of Information & Control is the property of Gai Kan Bian Wei Hui 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.)

Copyright of Information & Control is the property of Gai Kan Bian Wei Hui 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.)

Copyright of Information & Control is the property of Gai Kan Bian Wei Hui 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]

To solve the vehicle routing problem with time window for the shortest length of vehicle travel and the minimum number of the using vehicles, this paper proposed a cultural wolf pack algorithm. To improve the quality of the wolf pack, this method constructed the initial wolf pack according to the customer's nearest neighbor matrix. Considering the features of this problem and the wolf pack algorithm, it redefined the intelligent behaviors of artificial wolves. The mutation of leader wolf increased the searching range of the optimal solution. According to the favorable information of the current optimal solution, it constructed the knowledge of the belief space to guide the evolution direction of the wolf pack and improve the search efficiency. Simulation results show that the cultural wolf pack algorithm can get less number of vehicles, shorter total distance and better stability than other algorithms. [ABSTRACT FROM AUTHOR]

As to increase the amount of data, feature selection has become a hotspot in the field of machine learning and data mining. This paper proposed a nearest neighbors and farthest neighbors feature selection algorithm( NFFS).The nearest neighboring points of a data point belonged to the same cluster, and the furthest points belonged to a different cluster. Through calculating distances of the nearest cluster and the farthest cluster.it could get an indicator of judging characteristic importance. On the basis.it used the mutual information criterion to get rid of the redundancy between the features. At the same time.it introduced the Gradient boosting method to the tuning parameters of model. This method could improve the classification accuracy. By categorical forecasting on the UCI data sets,the results show that the algorithm can find the optimal feature subset and improve the classification accuracy. [ABSTRACT FROM AUTHOR]