The present paper attempts to find the optimal coverage path for multiple robots in a given area including obstacles. For single robot coverage path planning (CPP) problem, an improved ant colony optimization (ACO) algorithm is proposed to construct the best spanning tree and then obtain the optimal path, which contributes to minimizing the energy/time consumption. For the multirobot case, first the DARP (Divide Areas based on Robots Initial Positions) algorithm is utilized to divide the area into separate equal subareas, so much so that it transforms the mCPP problem into several CPP problems, degrading the computation complexity. During the second phase, spanning tree in each subarea is constructed by the aforementioned algorithm. In the last phase, the specific end nodes are exchanged among subareas to achieve ideal-shaped spanning trees, which can also decrease the number of turns in coverage path. And the complete algorithms are proven to be approximately polynomial algorithms. Finally, the simulation confirms the complete algorithms’ advantages: complete coverage, nonbacktracks, minimum length, zero preparation time, and the least number of turns.

The multi-objective weapon-target assignment problem, which aims to generate reasonable assignment to meet the objectives, is a typical optimization problem with complex constraints. In order to get close to the actual air combat, the game process between both sides at war is introduced to construct a threeobjective mathematical model, which includes the damage of the enemy, the cost of missiles, and the damage value of fighting capacity. Considering the NP-complete nature of multi-objective weapon-target assignment problem, an improved intelligent algorithm (named as D-NSGA-III-A) on the basis of non-dominated sorting genetic algorithm III (NSGA-III) is proposed. In this improved algorithm, first, the non-dominated sorting based on dominance degree matrix is proposed to reduce the unnecessary or repetitive comparisons in ranking schemes, so as to further decrease the time consumption. Second, diversity and convergence are taken into account resorting to the niching information and the dominance ratio when selecting individuals. Third, the adaptive operator selection mechanism, which selects the operators adaptively according to the information of generations from a pool where single point crossover and all bits crossover operators are included, is employed to seek a balance between intensification and diversification within the decision space and to improve the quality of Pareto solutions. From the experiments, the combination of above technologies obtains better Pareto solutions and time performance for solving the static multi-objective target assignment (SMWTA) problem than NSGA-III, MP-ACO, NSGA-II, MOPSO, MOEA/D, and DMOEA-εC. INDEX TERMS Adaptive operator selection mechanism, dominant degree matrix, multi-objective optimization, non-dominated sorting genetic algorithm III, weapon target assignment.

Abstract. In order to improve UAV's operational efficiency and survival probability, the optimal path of an UAV should be designed before the UAV performs a mission. This paper applies UAV's constrained conditions to the search strategies of ant colony and use a new evaluation method of path's cost. The algorithm's state transformation rules and pheromone updating rules are improved. These make its convergence speed and global searching ability enhanced remarkably. The simulation results show that this method can get a flight path which can avoid threats effectively in a short time and is a more efficient path planning method

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