{"title":"一种量子启发的数值优化人工蜂群算法","authors":"Amira Bouaziz, A. Draa, S. Chikhi","doi":"10.1109/ISPS.2013.6581498","DOIUrl":null,"url":null,"abstract":"A Quantum-inspired Artificial Bee Colony algorithm (QABC) for numerical optimisation is proposed in this paper. A hybridisation is made between two paradigms: Artificial Bee Colony (ABC) optimisation on one hand and Quantum Computing (QC) principles on the other hand. Some quantum concepts including the quantum bit, states superposition and quantum interference are used to enhance the diversity and computing capabilities of standard ABC algorithm. The experimental results obtained from testing the proposed algorithm on a set of numerical benchmark functions have shown that the QABC is competitive to quantum swarms and evolutionary approaches. It outperforms conventional evolutionary algorithms and quantum-inspired particle swarm algorithm.","PeriodicalId":222438,"journal":{"name":"2013 11th International Symposium on Programming and Systems (ISPS)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"A Quantum-inspired Artificial Bee Colony algorithm for numerical optimisation\",\"authors\":\"Amira Bouaziz, A. Draa, S. Chikhi\",\"doi\":\"10.1109/ISPS.2013.6581498\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A Quantum-inspired Artificial Bee Colony algorithm (QABC) for numerical optimisation is proposed in this paper. A hybridisation is made between two paradigms: Artificial Bee Colony (ABC) optimisation on one hand and Quantum Computing (QC) principles on the other hand. Some quantum concepts including the quantum bit, states superposition and quantum interference are used to enhance the diversity and computing capabilities of standard ABC algorithm. The experimental results obtained from testing the proposed algorithm on a set of numerical benchmark functions have shown that the QABC is competitive to quantum swarms and evolutionary approaches. It outperforms conventional evolutionary algorithms and quantum-inspired particle swarm algorithm.\",\"PeriodicalId\":222438,\"journal\":{\"name\":\"2013 11th International Symposium on Programming and Systems (ISPS)\",\"volume\":\"112 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-04-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 11th International Symposium on Programming and Systems (ISPS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISPS.2013.6581498\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 11th International Symposium on Programming and Systems (ISPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPS.2013.6581498","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Quantum-inspired Artificial Bee Colony algorithm for numerical optimisation
A Quantum-inspired Artificial Bee Colony algorithm (QABC) for numerical optimisation is proposed in this paper. A hybridisation is made between two paradigms: Artificial Bee Colony (ABC) optimisation on one hand and Quantum Computing (QC) principles on the other hand. Some quantum concepts including the quantum bit, states superposition and quantum interference are used to enhance the diversity and computing capabilities of standard ABC algorithm. The experimental results obtained from testing the proposed algorithm on a set of numerical benchmark functions have shown that the QABC is competitive to quantum swarms and evolutionary approaches. It outperforms conventional evolutionary algorithms and quantum-inspired particle swarm algorithm.
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