{"title":"一种新的启发式优化:搜索与救援算法及求解函数优化问题","authors":"M. Ozdemir","doi":"10.2139/ssrn.3902584","DOIUrl":null,"url":null,"abstract":"Heuristic techniques are optimization methods that inspired by nature. Although there are many heuristics in the literature, a new heuristic technique is presented by researchers every day by observing nature-based or living behaviors in nature. In this study, a new heuristic optimization technique inspired by human behavior is proposed. In order to prove the validity of this method called Search and Rescue Optimization Algorithm (AKOA), the technique applied to find the global minimums of function optimization test problems in the literature. As a result of the experiments performed on 21 minimization problems, it has been observed that AKOA is quite competitive when compared to Dynamic Random Search Technique and Random Selection Walk Technique.","PeriodicalId":106276,"journal":{"name":"CompSciRN: Algorithms (Topic)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A New Heuristic Optimization: Search and Rescue Algorithm and Solving the Function Optimization Problems\",\"authors\":\"M. Ozdemir\",\"doi\":\"10.2139/ssrn.3902584\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Heuristic techniques are optimization methods that inspired by nature. Although there are many heuristics in the literature, a new heuristic technique is presented by researchers every day by observing nature-based or living behaviors in nature. In this study, a new heuristic optimization technique inspired by human behavior is proposed. In order to prove the validity of this method called Search and Rescue Optimization Algorithm (AKOA), the technique applied to find the global minimums of function optimization test problems in the literature. As a result of the experiments performed on 21 minimization problems, it has been observed that AKOA is quite competitive when compared to Dynamic Random Search Technique and Random Selection Walk Technique.\",\"PeriodicalId\":106276,\"journal\":{\"name\":\"CompSciRN: Algorithms (Topic)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-02-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CompSciRN: Algorithms (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3902584\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CompSciRN: Algorithms (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3902584","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A New Heuristic Optimization: Search and Rescue Algorithm and Solving the Function Optimization Problems
Heuristic techniques are optimization methods that inspired by nature. Although there are many heuristics in the literature, a new heuristic technique is presented by researchers every day by observing nature-based or living behaviors in nature. In this study, a new heuristic optimization technique inspired by human behavior is proposed. In order to prove the validity of this method called Search and Rescue Optimization Algorithm (AKOA), the technique applied to find the global minimums of function optimization test problems in the literature. As a result of the experiments performed on 21 minimization problems, it has been observed that AKOA is quite competitive when compared to Dynamic Random Search Technique and Random Selection Walk Technique.