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中文摘要：
针对置换流水车间调度问题，提出了一种改进微粒群优化的求解算法。首先，由基于启发式信息的贪婪随机自适应算法得到工件加工顺序，个体最优的初始值不再是随机生成的初始值，而是由该工件加工顺序转化而成；然后，对个体最优解进行了交换型局部搜索；最后，通过对Car系列和Rec系列基准的测试，表明了该算法的有效性。
英文摘要：
To solve permutation flow shop scheduling problems, an Improved Particle Swarm Optimization （IPSO） algorithm was proposed. Firstly, each sequence of jobs was generated by greedy randomized adaptive search based on heuristics. The initial best position of each particle was no longer the randomly generated initial position of each particle, it was converted from above sequence of jobs. Then, a swap-based local search was applied for the best position of each particle. Finally, the simulation results based on benchmarks demonstrated the effectiveness of IPSO.
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