Research On Cognition Evolutionary Computation For Mission Planning For Cooperative Attacking

Cooperative attacking of cruise missiles is an important style of cooperative operation. The target of cruise missiles engagement has evolved from single target, multiple targets to system of systems target. In this condition, cooperation among single missile or even among missiles groups becomes a significant manner to finish complex missions. How to coordinate operations of multiple missiles to maximize the combat effectiveness has become a crucial problem with great theoretical significance and practical value.As the engagement process becomes more complex, the traditional methods, i.e. experts based method, case based method and operational research theory based method, are difficult to address the need of tactical mission planning for Multiple Missiles Static Cooperative Attacking System of Systems Target (MMSCA-SoST). This dissertation will take the operational effectiveness evaluation as a knowledge generation process and the simulation optimization as a knowledge based problem solving activity, and to combine the study. In the thought of human-computer cooperation, take cognitive psychology, creative computation and data mining as complementary to simulation optimization theory which is a complex military problem solving model. Considering the actual background of cruise missile cooperative attacking, and aiming at the defects of existing research results, studies made in the dissertation are as follows:(1) The basic mat hematic model for MMSCA-SoST problem and the general solving framework are presented. The concepts relevant to MMSCA-SoST are defined and the mathematical model of MMSCA-SoST is established. MMSCA-SoST is a combination optimization problem with multi-constraints and strong coupling which comprised of four sub problems, i.e. target selection problem, scheduling problem, assignment problem and parameters optimization problem. The basic framework of simulation models is established as the foundation of simulation systems construction to fulfill the needs of simulation based problem solving. The complexity of MMSCA-SoST itself and the complexity induced by simulation are analyzed. A novel simulation based problem solving model, i.e. "simulation + data mining + cognition behaviors based intelligent algorithm" , is presented. According to the effectiveness analysis of cooperative operation of multiple missiles, a hierarchical MMSCA-SoST solving framework based on the effectiveness generation model is presented in which MMSCA-SoST is divided into two sub problems, i.e. missiles group mission planning problem and missiles group operation planning problem. The framework can prevents us from bogging down by resolving the over-complicated model.(2) Cognition Evolutionary Algorithm (CEA) towarding simulation optimization is put forward. Inspired by the process of human creative thinking, cognition evolutionary algorithm, a novel intelligent algorithm based on cognition science and computational creativity, is proposed, which simulates the creative thinking based problem solving process and behaviors. The algorithm is comprised of six components, i.e. divergent thinking, convergent thinking, memory, and execution, learning and value measures. Taking the problem-solving as knowledge based creative thinking process, knowledge evolution and knowledge based creative thinking skills play important roles in the algorithm. We explore the impact of the parameters of cognition evolutionary algorithm through an extended path optimization problem. The results show that the novel algorithm can reduce object score evaluation times for solving optimization problems which are compute-intensive and knowledge-intensive compared to other classic intelligent algorithms.(3) The two-level effectiveness knowledge models of cruise missiles cooperative attacking as well as the corresponding simulation data mining algorithms are presented. Missiles group mission level knowledge representation based on Bayesian Networks (BN) is proposed. A Structure learning method of Bayesian network is presented to solve the problem of structure learning with uncertain prior information. An improved MDL measure named SMDL is proposed to fuse the prior information in learning process. Simulated annealing method is used to solve the problem. The experimental results on the Asia network show' that the proposed algorithm is more accurate than classical ones with fewer samples. A BN based knowledge model, i.e. BOEM, is proposed to model missiles group operation level knowledge. The specification and the modeling process of BOEM is detail studied. The concept of probability rule surrogate model based simulation is given to rapidly generate data samples for BOEM construction. Continuous variables discretization, the key component of BOEM learning algorithm, is studied, and a discretization algorithm based on reasoning information is proposed.(4) Cooperative attacking hierarchical cognition evolutionary computing model (CHCEM) and its algorithms are presented to resolve MMSCA-SoST based on two-level effectiveness knowledge model and CEA. The behaviors of components in hierarchical MMSCA-SoST solving framework are implemented based on the integration of CEA and effectiveness knowledge following the logic process defined in the same framework. CHCEM which is a computable model is established. The algorithms for resolving MGMP and MGOP are putting forward.(5) The process of the proposed method is given and the validity is proven with a case study of submarine-launched anti-ship missiles against surface fleet.