{"title":"流车间调度问题的一种具有多样性的混合粒子群优化算法","authors":"Shin-Ying Huang, Chuen-Lung Chen","doi":"10.1109/ICICIC.2009.21","DOIUrl":null,"url":null,"abstract":"This paper proposed a hybrid particle swarm optimization algorithm (Shadow hybrid PSO, SHPSO) to solve the flow-shop scheduling problem (FSSP). SHPSO adopts the idea of Kuoa's HPSO model[4] by not only combines the random-key (RK) encoding scheme, individual enhancement (IE) scheme, but also adds the diversification mechanism such as ARPSO model and competitive shadow particles to prevent premature convergence. Computation experiments results of Taillard's [10] seven representative instances of FSSP show that the SHPSO perform close to HPSO for FSSP to minimize makespan. Further recommendations and improved ideas will be discussed later in this paper.","PeriodicalId":240226,"journal":{"name":"2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Hybrid Particle Swarm Optimization Algorithm with Diversity for Flow-Shop Scheduling Problem\",\"authors\":\"Shin-Ying Huang, Chuen-Lung Chen\",\"doi\":\"10.1109/ICICIC.2009.21\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposed a hybrid particle swarm optimization algorithm (Shadow hybrid PSO, SHPSO) to solve the flow-shop scheduling problem (FSSP). SHPSO adopts the idea of Kuoa's HPSO model[4] by not only combines the random-key (RK) encoding scheme, individual enhancement (IE) scheme, but also adds the diversification mechanism such as ARPSO model and competitive shadow particles to prevent premature convergence. Computation experiments results of Taillard's [10] seven representative instances of FSSP show that the SHPSO perform close to HPSO for FSSP to minimize makespan. Further recommendations and improved ideas will be discussed later in this paper.\",\"PeriodicalId\":240226,\"journal\":{\"name\":\"2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC)\",\"volume\":\"112 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-12-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICIC.2009.21\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICIC.2009.21","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Hybrid Particle Swarm Optimization Algorithm with Diversity for Flow-Shop Scheduling Problem
This paper proposed a hybrid particle swarm optimization algorithm (Shadow hybrid PSO, SHPSO) to solve the flow-shop scheduling problem (FSSP). SHPSO adopts the idea of Kuoa's HPSO model[4] by not only combines the random-key (RK) encoding scheme, individual enhancement (IE) scheme, but also adds the diversification mechanism such as ARPSO model and competitive shadow particles to prevent premature convergence. Computation experiments results of Taillard's [10] seven representative instances of FSSP show that the SHPSO perform close to HPSO for FSSP to minimize makespan. Further recommendations and improved ideas will be discussed later in this paper.