{"title":"基于入侵杂草优化的萤火虫杂交算法求解工程设计问题","authors":"H. Kasdirin, N. M. Yahya, M. Tokhi","doi":"10.1109/EAIS.2015.7368801","DOIUrl":null,"url":null,"abstract":"This study presents a hybrid invasive weed firefly optimization (HIWFO) algorithm to solve engineering optimization design problems. The unconstrained and engineering constrained design problems with continuous design variables are used to illustrate the effectiveness and robustness of the proposed algorithm. Firefly algorithm (FA) has deficit on getting trapped at local optimum and invasive weed optimization (IWO) is effective with accurate global search ability. Therefore, the idea of hybridization between IWO and FA has obtained a more robust optimization technique, especially trying to compensate for the deficiencies of the individual algorithms. In the proposed algorithm, the firefly method is embedded into the invasive weed optimization to enhance the local search capability of IWO algorithm that already has very good exploration capability. The performance and evaluation of the proposed method are tested with four well-known unconstrained problems and two engineering design problems. A comparative assessment with the original FA and IWO carried out on the unconstrained problem clearly demonstrates the effectiveness of the hybrid algorithm. Moreover, in dealing with the practical design problems, the HIWFO algorithm is also compared to other algorithm methods to illustrate its effectiveness. From the simulation results, it can be concluded that the HIWFO algorithm has superior searching quality and robustness than other mentioned approaches.","PeriodicalId":325875,"journal":{"name":"2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Hybridizing firefly algorithm with invasive weed optimization for engineering design problems\",\"authors\":\"H. Kasdirin, N. M. Yahya, M. Tokhi\",\"doi\":\"10.1109/EAIS.2015.7368801\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study presents a hybrid invasive weed firefly optimization (HIWFO) algorithm to solve engineering optimization design problems. The unconstrained and engineering constrained design problems with continuous design variables are used to illustrate the effectiveness and robustness of the proposed algorithm. Firefly algorithm (FA) has deficit on getting trapped at local optimum and invasive weed optimization (IWO) is effective with accurate global search ability. Therefore, the idea of hybridization between IWO and FA has obtained a more robust optimization technique, especially trying to compensate for the deficiencies of the individual algorithms. In the proposed algorithm, the firefly method is embedded into the invasive weed optimization to enhance the local search capability of IWO algorithm that already has very good exploration capability. The performance and evaluation of the proposed method are tested with four well-known unconstrained problems and two engineering design problems. A comparative assessment with the original FA and IWO carried out on the unconstrained problem clearly demonstrates the effectiveness of the hybrid algorithm. Moreover, in dealing with the practical design problems, the HIWFO algorithm is also compared to other algorithm methods to illustrate its effectiveness. From the simulation results, it can be concluded that the HIWFO algorithm has superior searching quality and robustness than other mentioned approaches.\",\"PeriodicalId\":325875,\"journal\":{\"name\":\"2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)\",\"volume\":\"133 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EAIS.2015.7368801\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EAIS.2015.7368801","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hybridizing firefly algorithm with invasive weed optimization for engineering design problems
This study presents a hybrid invasive weed firefly optimization (HIWFO) algorithm to solve engineering optimization design problems. The unconstrained and engineering constrained design problems with continuous design variables are used to illustrate the effectiveness and robustness of the proposed algorithm. Firefly algorithm (FA) has deficit on getting trapped at local optimum and invasive weed optimization (IWO) is effective with accurate global search ability. Therefore, the idea of hybridization between IWO and FA has obtained a more robust optimization technique, especially trying to compensate for the deficiencies of the individual algorithms. In the proposed algorithm, the firefly method is embedded into the invasive weed optimization to enhance the local search capability of IWO algorithm that already has very good exploration capability. The performance and evaluation of the proposed method are tested with four well-known unconstrained problems and two engineering design problems. A comparative assessment with the original FA and IWO carried out on the unconstrained problem clearly demonstrates the effectiveness of the hybrid algorithm. Moreover, in dealing with the practical design problems, the HIWFO algorithm is also compared to other algorithm methods to illustrate its effectiveness. From the simulation results, it can be concluded that the HIWFO algorithm has superior searching quality and robustness than other mentioned approaches.