Search

Mosaic warfare is a new subversive military concept put forward by the US military, which is facing the future war, aiming at strategic opponents and relying on the cutting-edge technology. Once it forms actual combat capability, it will generate a series of nonlinear combat effects and form asymmetrical advantage over the major combat opponents. Based on the analysis of the background of mosaic warfare concept, the characteristic advantage of mosaic warfare are analyzed and condensed from the aspects of the improvement of force design efficiency, the optimization of command and control process, and the innovation of operation mode. The winning mechanism of mosaic warfare is analyzed in the aspects of multi-layer circular nesting of OODA, strong anti-attack ability, high reconstruction efficiency and intelligent fast destruction of the opponent’s OODA ring.

Artificial bee colony (ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencies in ABC regarding its local search ability and global search efficiency. Aiming at these deficiencies, an ABC variant named hybrid ABC (HABC) algorithm is proposed. Firstly, the variable neighborhood search factor is added to the solution search equation, which can enhance the local search ability and increase the population diversity. Secondly, inspired by the neuroscience investigation of real honeybees, the memory mechanism is put forward, which assumes the artificial bees can remember their past successful experiences and further guide the subsequent foraging behavior. The proposed memory mechanism is used to improve the global search efficiency. Finally, the results of comparison on a set of ten benchmark functions demonstrate the superiority of HABC.

Since most parameter control methods are based on prior knowledge, it is difficult for them to solve various problems. In this paper, an adaptive selection method used for operators and parameters is proposed and named double adaptive selection (DAS) strategy. Firstly, some experiments about the operator search ability are given and the performance of operators with different donate vectors is analyzed. Then, DAS is presented by inducing the upper confidence bound strategy, which chooses suitable combination of operators and donates sets to optimize solutions without prior knowledge. Finally, the DAS is used under the framework of the multi-objective evolutionary algorithm based on decomposition, and the multi-objective evolutionary algorithm based on DAS (MOEA/D-DAS) is compared to state-of-the-art MOEAs. Simulation results validate that the MOEA/D-DAS could select the suitable combination of operators and donate sets to optimize problems and the proposed algorithm has better convergence and distribution.

In this paper we emphasize the importance of evaluating the intelligence level of intelligent control systems. We bring forward the idea that the evaluating system is composed of a three tier construction: organizing level, coordinating level and executive level, and we put the intelligence calculation in the organizing level. We also put forward the index system of the intelligence evaluating system of the ICS, which comprises 5 first grade indexes (I1), 17 second grade indexes (I2) and 52 third grade indexes (I3). And then, we give the formula to calculate the intelligent quality of the ICS. Finally we cite two examples to show the calculation of the ICS's intelligent quality.