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中文摘要：
针对离散粒子群算法求解旅行商问题,根据组合优化问题和离散量的特点,改进离散粒子群算法更新的运动方程。对离散粒子群算法分别加入逆转变异优化策略、受蚁群启示的变异优化策略和近邻搜索变异优化策略3种优化变异优化策略,使其成为新的混合离散粒子群算法,最后对3种混合离散粒子群算法进行比较,并剖析仿真结果的本质。结果表明：3种优化策略在不同程度上都提高了离散粒子群算法的总体效果和收敛性能,其中,加入逆转变异优化策略的混合粒子群算法实现简单,时间代价较小;加入近邻搜索变异优化策略的混合粒子群算法不论是在最优值或稳定性方面表现最突出。
英文摘要：
Updating kenetic equations for discrete particle swarm optimization algorithm were improved to tackle travel salesman problem based on combinatorial optimization problem and discrete variable. Three mutant strategies were designed, which are named reversion mutant strategy, enlighten by ant colony mutant strategy and close neighbor search mutant strategy. Those mutant strategies were added individually and became new hybrid discrete particle swarm optimization algorithms. Those algorithms were compared and the simulation results were analyzed deeply. The result shows that general effects and convergences of discrete particle swarm optimization algorithm increase by those three mutant strategies to different extents. Reversion mutant strategy for discrete particle swarm optimization algorithm achieves simply and costs less time. Close neighbor search mutant strategy for discrete particle swarm optimization algorithm is the most outstanding with the best value and stability.
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