{"title":"约束优化遗传算法及其在约束WTA问题中的应用","authors":"Ling Wu, Xu-Tong Yu, Faxing Lu","doi":"10.1109/IHMSC.2015.60","DOIUrl":null,"url":null,"abstract":"In the actual weapon-target allocation (WTA) problem, weapons can not be randomly paired with incoming targets since their launching zones are limited. Though genetic algorithm (GA) with various modifications has been developed for WTA problems, the spatial constraints are always ignored. In the paper a genetic algorithm (GA) based approach is developed to solve the WTA problem subject to spatial constraints, where each chromosome is encoded as a binary matrix with \"forbidden bits\" to address problem constraints, and corresponding crossover and mutation operators are designed to guarantee each chromosome a valid solution to the WTA problem through the whole evolving process. Simulation results verify the feasibility of the proposed approach.","PeriodicalId":6592,"journal":{"name":"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics","volume":"10 1","pages":"141-144"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Constraint Optimization GA and Its Application to Constrained WTA Problem\",\"authors\":\"Ling Wu, Xu-Tong Yu, Faxing Lu\",\"doi\":\"10.1109/IHMSC.2015.60\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the actual weapon-target allocation (WTA) problem, weapons can not be randomly paired with incoming targets since their launching zones are limited. Though genetic algorithm (GA) with various modifications has been developed for WTA problems, the spatial constraints are always ignored. In the paper a genetic algorithm (GA) based approach is developed to solve the WTA problem subject to spatial constraints, where each chromosome is encoded as a binary matrix with \\\"forbidden bits\\\" to address problem constraints, and corresponding crossover and mutation operators are designed to guarantee each chromosome a valid solution to the WTA problem through the whole evolving process. Simulation results verify the feasibility of the proposed approach.\",\"PeriodicalId\":6592,\"journal\":{\"name\":\"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics\",\"volume\":\"10 1\",\"pages\":\"141-144\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IHMSC.2015.60\",\"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 7th International Conference on Intelligent Human-Machine Systems and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IHMSC.2015.60","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Constraint Optimization GA and Its Application to Constrained WTA Problem
In the actual weapon-target allocation (WTA) problem, weapons can not be randomly paired with incoming targets since their launching zones are limited. Though genetic algorithm (GA) with various modifications has been developed for WTA problems, the spatial constraints are always ignored. In the paper a genetic algorithm (GA) based approach is developed to solve the WTA problem subject to spatial constraints, where each chromosome is encoded as a binary matrix with "forbidden bits" to address problem constraints, and corresponding crossover and mutation operators are designed to guarantee each chromosome a valid solution to the WTA problem through the whole evolving process. Simulation results verify the feasibility of the proposed approach.