Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) is a monthly peer-reviewed scopus indexed journal originally founded in 1986. It is sponsored by the Ministry of Education, china and Northeastern University, china. Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) has always adhered to the correct purpose of running the journal, and has been committed to gathering and disseminating excellent academic achievements, inspiring technological innovation, and promoting the development of disciplines in my country.Aiming at major national needs and international frontiers, this journal has published a large number of original and high-level research result. The journal was selected into the "China Science and Technology Journal Excellence Action Plan Project" in December 2019.In the future, it will strive to build an open innovation, collaborative integration.

Aiming at the problems of low efficiency of path planning, a new node storage structure is introduced and the search method is optimized to improve the deterministic iterative path planning algorithm. First, the number of antibodies is determined based on the connectable starting path point, when generating the initial antibody with the inspiration of the optimal angle vaccine. Then, the connectable path point of the starting point is treated as the root node to rebuild the new path with path filtering by means of the optimal path fit value. The initial optimal antibody is used as the scree

A chaotic coyote optimization algorithm based on inverse time-decay operator(ICCOA) is proposed to solve the coyote optimization algorithm(COA), such as the poor performance and low diversity. Firstly, the inverse time decay weight factor is added in the process of individual iterative updating, so as to maintain the balance between global search and local development ability and improve the search speed of the algorithm. Secondly, add the chaotic interference mechanism based on Tent chaotic map, some poor individuals in the population were mapped to produce new individuals, thus increasing

By integrating renewable energy sources (RESs) with electric vehicles (EVs) in microgrids, we are able to reduce carbon emissions as well as alleviate the dependence on fossil fuels. In order to improve the economy of an integrated system and fully exploit the potentiality of EVs’ mobile energy storage while achieving a reasonable configuration of RESs, a cooperative optimization method is proposed to cooperatively optimize the economic dispatching and capacity allocation of both RESs and EVs in the context of a regional multi-microgrid system. An across-time-and-space energy transmis

In this paper, we investigate the ensemble controllability and reachability for a family of Boolean control networks (BCNs). First, BCNs are converted to discrete-time linear dynamics by the semi-tensor product. Then the ensemble controllability of BCNs is studied via a free control sequence and input Boolean network, respectively. Some necessary and sufficient conditions are obtained to judge the ensemble controllability. The existence of the input Boolean network for the ensemble controllability is also discussed. When there are unknown inputs in BCNs, necessary and sufficient conditions

Reservoir computing is a computational framework suited for temporal/sequential data processing. It is derived from several recurrent neural network models, including echo state networks and liquid state machines. A reservoir computing system consists of a reservoir for mapping inputs into a high-dimensional space and a readout for pattern analysis from the high-dimensional states in the reservoir. The reservoir is fixed and only the readout is trained with a simple method such as linear regression and classification. Thus, the major advantage of reservoir computing compared to other recurr