{"title":"基于进化策略的复杂化学方程求根","authors":"Yongquan Zhou, Delong Guo","doi":"10.1109/ICNC.2008.178","DOIUrl":null,"url":null,"abstract":"In chemistry, there are some algebra questions about equation roots that need to be solved. So far, there are some traditional methods such as graphic method and Newton iteration method and so on. As well known, the traditional methods have some shortcomings such as being easily affected by initial value and not being able to obtain very high precision. In this paper, evolution strategy algorithm is applied to solve the problems of complex chemistry equation roots by transforming them into the function optimization ones, making full use of such characteristics of the evolution strategy as being self-adaptive to gradually approach the optimal solution through optimized selection, recombination, and mutation. Finally, several computer simulation results show that the method in this paper has rapid convergence speed and powerful performance.","PeriodicalId":6404,"journal":{"name":"2008 Fourth International Conference on Natural Computation","volume":"332 1","pages":"488-492"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Finding Roots of Complex Chemistry Equations Based on Evolution Strategies\",\"authors\":\"Yongquan Zhou, Delong Guo\",\"doi\":\"10.1109/ICNC.2008.178\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In chemistry, there are some algebra questions about equation roots that need to be solved. So far, there are some traditional methods such as graphic method and Newton iteration method and so on. As well known, the traditional methods have some shortcomings such as being easily affected by initial value and not being able to obtain very high precision. In this paper, evolution strategy algorithm is applied to solve the problems of complex chemistry equation roots by transforming them into the function optimization ones, making full use of such characteristics of the evolution strategy as being self-adaptive to gradually approach the optimal solution through optimized selection, recombination, and mutation. Finally, several computer simulation results show that the method in this paper has rapid convergence speed and powerful performance.\",\"PeriodicalId\":6404,\"journal\":{\"name\":\"2008 Fourth International Conference on Natural Computation\",\"volume\":\"332 1\",\"pages\":\"488-492\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 Fourth International Conference on Natural Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNC.2008.178\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Fourth International Conference on Natural Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2008.178","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Finding Roots of Complex Chemistry Equations Based on Evolution Strategies
In chemistry, there are some algebra questions about equation roots that need to be solved. So far, there are some traditional methods such as graphic method and Newton iteration method and so on. As well known, the traditional methods have some shortcomings such as being easily affected by initial value and not being able to obtain very high precision. In this paper, evolution strategy algorithm is applied to solve the problems of complex chemistry equation roots by transforming them into the function optimization ones, making full use of such characteristics of the evolution strategy as being self-adaptive to gradually approach the optimal solution through optimized selection, recombination, and mutation. Finally, several computer simulation results show that the method in this paper has rapid convergence speed and powerful performance.