Aiming at the problem that the heuristic algorithms have unstable path lengths and are easy to fall into local minimum in the process of robot path planning， an Adaptively Adjusted Harris Hawk Optimization （AAHHO） algorithm was proposed. Firstly， the convergence factor adjustment strategy was used to adjust the balance between the global search stage and the local search stage， and the natural constant was used as the base to improve the search efficiency and convergence accuracy. Then， in the global search phase， the elite cooperation guided search strategy was adopted， by three elite Harris hawks cooperatively guiding other individuals to update the positions， so that the search performance was enhanced， and the information exchange among the populations was enhanced through the three optimal positions. Finally， by simulating the intraspecific competition strategy， the ability of the Harris hawks to jump out of the local optimum was improved. The comparative experimental results of function testing and robot path planning show that the proposed algorithm is superior to comparison algorithms such as IHHO（Improve Harris Hawk Optimization） and CHHO（Chaotic Harris Hawk Optimization）， in both function testing and path planning， and it has better effectiveness， feasibility and stability in robot path planning.

In order to deal with the problems that defensive measures are lagging behind the attack and that the payoffs of attacker and defender are unequal, an active strategy selection method based on non-zero-sum game was proposed. Firstly, a network security game graph was presented combined with the actual situation of network security and the relationship between the attacker and the defender. Secondly, a network attack-defense game model was proposed based on non-zero-sum game. The attack-defense cost of single security attribute was calculated combined with the host important degree and success rate of defense measures, and according to attack-defense intention, the total attack-defense cost was quantified. Finally, the best strategy for defender was obtained by analyzing the Nash equilibrium of the game model. A representative example was given to illustrate the efficacy and feasibility of the method on attack prediction and active defense strategy selection.