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Scalable Multi-objective Test Problem Toolkit","{\"openalex\":\"W1608317386\",\"doi\":\"10.1007/978-3-540-31880-4_20\"}","2026-05-19T21:51:49.212011","2026-05-19T21:51:49.23772628","doi","10.1007/978-3-540-31880-4_20","arxivId","openalexId","W1608317386","abstractText","publishedDate","2005-01-01","publishedYear",2005,"venue","Lecture notes in computer science",376,"authors","keywords",[153,154,155],"pdfUrl","topVenue","Advanced Multi-Objective Optimization Algorithms","Probabilistic and Robust Engineering Design","Optimal Experimental Design Methods","paper_W2050914643","A Fast Way of Calculating Exact Hypervolumes","{\"openalex\":\"W2050914643\",\"doi\":\"10.1109/tevc.2010.2077298\"}","2026-05-16T15:26:11.386006475","2026-05-19T07:33:15.183532892","10.1109/tevc.2010.2077298","W2050914643","We describe a new algorithm WFG for calculating hypervolume exactly. WFG is based on the recently-described observation that the exclusive hypervolume of a point p relative to a set S is equal to the difference between the inclusive hypervolume of p and the hypervolume of S with each point limited by the objective values in p . WFG applies this technique iteratively over a set to calculate its hypervolume. Experiments show that WFG is substantially faster (in five or more objectives) than all previously-described algorithms that calculate hypervolume exactly.","2011-05-13",2011,"IEEE Transactions on Evolutionary Computation",440,[153,169,170],"Advanced Optimization Algorithms Research","Metaheuristic Optimization Algorithms Research","paper_W2153654820","A faster algorithm for calculating hypervolume","{\"openalex\":\"W2153654820\",\"doi\":\"10.1109/tevc.2005.851275\"}","2026-05-14T12:38:17.278946565","2026-05-21T16:27:33.223781618","10.1109/tevc.2005.851275","W2153654820","We present an algorithm for calculating hypervolume exactly, the Hypervolume by Slicing Objectives (HSO) algorithm, that is faster than any that has previously been published. HSO processes objectives instead of points, an idea that has been considered before but that has never been properly evaluated in the literature. We show that both previously studied exact hypervolume algorithms are exponential in at least the number of objectives and that although HSO is also exponential in the number of objectives in the worst case, it runs in significantly less time, i.e., two to three orders of magnitude less for randomly generated and benchmark data in three to eight objectives. Thus, HSO increases the utility of hypervolume, both as a metric for general optimization algorithms and as a diversity mechanism for evolutionary algorithms.","2006-01-31",2006,1064,[153,184,185],"https://ro.ecu.edu.au/cgi/viewcontent.cgi?article=3022&context=ecuworks","Evolutionary Algorithms and Applications","Software Engineering Research","paper_W2143185749","A review of multiobjective test problems and a scalable test problem toolkit","{\"openalex\":\"W2143185749\",\"doi\":\"10.1109/tevc.2005.861417\"}","2026-05-14T04:30:16.334013497","2026-05-21T06:41:29.190959475","10.1109/tevc.2005.861417","W2143185749","When attempting to better understand the strengths and weaknesses of an algorithm, it is important to have a strong understanding of the problem at hand. This is true for the field of multiobjective evolutionary algorithms (EAs) as it is for any other field. Many of the multiobjective test problems employed in the EA literature have not been rigorously analyzed, which makes it difficult to draw accurate conclusions about the strengths and weaknesses of the algorithms tested on them. In this paper, we systematically review and analyze many problems from the EA literature, each belonging to the important class of real-valued, unconstrained, multiobjective test problems. To support this, we first introduce a set of test problem criteria, which are in turn supported by a set of definitions. Our analysis of test problems highlights a number of areas requiring attention. Not only are many test problems poorly constructed but also the important class of nonseparable problems, particularly nonseparable multimodal problems, is poorly represented. Motivated by these findings, we present a flexible toolkit for constructing well-designed test problems. We also present empirical results demonstrating how the toolkit can be used to test an optimizer in ways that existing test suites do not","2006-10-01",2039,[153,170,184],"https://ro.ecu.edu.au/ecuworks/2022",{"_59":199,"_61":200},[],[201],{"_64":202,"_66":203,"_68":204,"_70":205},1784636397489,{"_73":207,"_75":76,"_77":202,"_78":-5,"_79":26,"_80":26,"_81":26,"_82":-5,"_83":-5,"_84":85,"_35":86,"_87":88},[72,206],"[\"portal\",\"industries\"]","industries",[208,209,210,211,212,213,214,215,216,217,218,219],{"_15":281,"_29":282,"_31":-5,"_32":283,"_33":284,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":76,"_228":229,"_230":285,"_232":26,"_233":85,"_234":26,"_235":-5,"_57":282,"_58":284},{"_15":277,"_29":278,"_31":-5,"_32":-5,"_33":279,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":76,"_228":229,"_230":280,"_232":26,"_233":85,"_234":26,"_235":-5,"_57":278,"_58":279},{"_15":271,"_29":272,"_31":-5,"_32":273,"_33":274,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":76,"_228":229,"_230":275,"_232":26,"_233":85,"_234":26,"_235":-5,"_57":272,"_58":274},{"_15":267,"_29":268,"_31":-5,"_32":-5,"_33":269,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":76,"_228":229,"_230":270,"_232":26,"_233":85,"_234":26,"_235":-5,"_57":268,"_58":269},{"_15":263,"_29":264,"_31":-5,"_32":-5,"_33":265,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":76,"_228":229,"_230":266,"_232":26,"_233":85,"_234":26,"_235":-5,"_57":264,"_58":265},{"_15":257,"_29":258,"_31":-5,"_32":259,"_33":260,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":76,"_228":229,"_230":261,"_232":26,"_233":85,"_234":26,"_235":-5,"_57":258,"_58":260},{"_15":252,"_29":253,"_31":-5,"_32":-5,"_33":254,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":76,"_228":229,"_230":255,"_232":26,"_233":256,"_234":26,"_235":-5,"_57":253,"_58":254},{"_15":248,"_29":249,"_31":-5,"_32":-5,"_33":250,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":76,"_228":229,"_230":251,"_232":26,"_233":85,"_234":26,"_235":-5,"_57":249,"_58":250},{"_15":244,"_29":245,"_31":-5,"_32":-5,"_33":246,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":76,"_228":229,"_230":247,"_232":26,"_233":85,"_234":26,"_235":-5,"_57":245,"_58":246},{"_15":240,"_29":241,"_31":-5,"_32":-5,"_33":242,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":76,"_228":229,"_230":243,"_232":26,"_233":85,"_234":26,"_235":-5,"_57":241,"_58":242},{"_15":236,"_29":237,"_31":-5,"_32":-5,"_33":238,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":76,"_228":229,"_230":239,"_232":26,"_233":85,"_234":26,"_235":-5,"_57":237,"_58":238},{"_15":220,"_29":221,"_31":-5,"_32":-5,"_33":222,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":223,"_42":-5,"_44":-5,"_224":225,"_226":-5,"_227":76,"_228":229,"_230":231,"_232":26,"_233":85,"_234":26,"_235":-5,"_57":221,"_58":222},"tax_ind_smartphone","智能手机","Smartphone","manual","taxonomyType","INDUSTRY","code","level","parentId","tax_ind_root","path","科技行业/智能手机","sortOrder","isLeaf","entityCount","children","tax_ind_robot","机器人","Robotics","科技行业/机器人","tax_ind_wearable","智能穿戴","Smart Wearables","科技行业/智能穿戴","tax_ind_energy","新能源与智能制造","New Energy & Smart Manufacturing","科技行业/新能源与智能制造","tax_ind_biotech","生物科技","Biotech","科技行业/生物科技","tax_ind_quantum","量子计算","Quantum Computing","科技行业/量子计算",true,"tax_ind_blockchain","区块链",[262],"Blockchain","科技行业/区块链","Web3","tax_ind_enterprise","企业软件","Enterprise Software","科技行业/企业软件","tax_ind_security","网络安全","Cybersecurity","科技行业/网络安全","tax_ind_semi","半导体",[276],"Semiconductor","科技行业/半导体","芯片","tax_ind_cloud","云计算","Cloud Computing","科技行业/云计算","tax_ind_ai","人工智能",[286,287],"Artificial Intelligence","科技行业/人工智能","AI","AI 技术","env",{"_290":24},"SITE_BASE_URL"]

Luigi Barone 人物

Luigi Barone · 相关文章