基于改进烟花算法的多目标多机器人任务分配

多机器人任务规划是多机器人系统研究的主要问题之一，多目标多机器人任务规划是指同时对多机器人系统的多个指标进行优化。近年来，启发式算法越来越多地被用来解决多目标问题。本文提出了一种基于改进烟花算法的多目标多机器人任务分配方法，并详细讨论了多目标解的排序方法和选择策略。为了验证该方法的性能，对7个实例进行了实验，并对该方法和其他四种多目标算法，Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2)，Pareto Envelope-based Selection Algorithm (PESA ) 和一种改进的Strength Pareto Genetic Algorithm 2 (SPGA2)在S-metric指标上进行了比较。实验结果表明，在解集质量、解集覆盖度方面，基于改进烟花算法的多目标多机器人任务分配方法具有明显的优势。 

Multi-objective Multi-robot Mission Planning Based on Improved Fireworks Algorithm

Multi-robot mission planning is one of the main problems in multi-robot system research. Multi-objective multi-robot mission planning refers to optimizing multiple indicators of the multi-robot system at the same time. In recent years, heuristic algorithms have increasingly been used to solve multi-objective prob-lems. In this paper, a multi-objective multi-robot mission planning method based on improved fireworks algorithm was proposed. In addition, the sorting method and selection strategy of the multi-objective so-lutions were discussed in detail. In order to verify the performance of the method, seven instances were tested, and the method was compared with other four multi-objective algorithms on the S-metric index. The other four multi-objective algorithms were Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2)，Pareto Envelope-based Selection Algorithm (PESA) and an Improved Strength Pareto Genetic Algorithm 2 (SPGA2). Experimental results shown that the proposed multi-objective multi-robot mission planning method based on improved fireworks algorithm has obvious advantages both in Pareto solution set quality and solution set scale.