Abhishek Kumar , Guohua Wu , Mostafa Z. Ali , Rammohan Mallipeddi , Ponnuthurai Nagaratnam Suganthan , Swagatam Das
{"title":"一个来自现实世界的非凸约束优化问题的测试套件和一些基线结果","authors":"Abhishek Kumar , Guohua Wu , Mostafa Z. Ali , Rammohan Mallipeddi , Ponnuthurai Nagaratnam Suganthan , Swagatam Das","doi":"10.1016/j.swevo.2020.100693","DOIUrl":null,"url":null,"abstract":"<div><p><span>Real-world optimization problems have been comparatively difficult to solve due to the complex nature of the objective function with a substantial number of constraints. To deal with such problems, several metaheuristics<span> as well as constraint handling approaches have been suggested. To validate the effectiveness and strength, performance of a newly designed approach should be benchmarked by using some complex real-world problems, instead of only the toy problems with synthetic objective functions, mostly arising from the area of numerical analysis. A list of standard real-life problems appears to be the need of the time for benchmarking new algorithms in an efficient and unbiased manner. In this study, a set of 57 real-world Constrained Optimization Problems (COPs) are described and presented as a benchmark suite to validate the COPs. These problems are shown to capture a wide range of difficulties and challenges that arise from the real life optimization scenarios. Three state-of-the-art </span></span>constrained optimization methods are exhaustively tested on these problems to analyze their hardness. The experimental outcomes reveal that the selected problems are indeed challenging to these algorithms, which have been shown to solve many synthetic benchmark problems easily.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.swevo.2020.100693","citationCount":"196","resultStr":"{\"title\":\"A test-suite of non-convex constrained optimization problems from the real-world and some baseline results\",\"authors\":\"Abhishek Kumar , Guohua Wu , Mostafa Z. Ali , Rammohan Mallipeddi , Ponnuthurai Nagaratnam Suganthan , Swagatam Das\",\"doi\":\"10.1016/j.swevo.2020.100693\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span>Real-world optimization problems have been comparatively difficult to solve due to the complex nature of the objective function with a substantial number of constraints. To deal with such problems, several metaheuristics<span> as well as constraint handling approaches have been suggested. To validate the effectiveness and strength, performance of a newly designed approach should be benchmarked by using some complex real-world problems, instead of only the toy problems with synthetic objective functions, mostly arising from the area of numerical analysis. A list of standard real-life problems appears to be the need of the time for benchmarking new algorithms in an efficient and unbiased manner. In this study, a set of 57 real-world Constrained Optimization Problems (COPs) are described and presented as a benchmark suite to validate the COPs. These problems are shown to capture a wide range of difficulties and challenges that arise from the real life optimization scenarios. Three state-of-the-art </span></span>constrained optimization methods are exhaustively tested on these problems to analyze their hardness. The experimental outcomes reveal that the selected problems are indeed challenging to these algorithms, which have been shown to solve many synthetic benchmark problems easily.</p></div>\",\"PeriodicalId\":48682,\"journal\":{\"name\":\"Swarm and Evolutionary Computation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.2000,\"publicationDate\":\"2020-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/j.swevo.2020.100693\",\"citationCount\":\"196\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Swarm and Evolutionary Computation\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210650219308946\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swarm and Evolutionary Computation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210650219308946","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
A test-suite of non-convex constrained optimization problems from the real-world and some baseline results
Real-world optimization problems have been comparatively difficult to solve due to the complex nature of the objective function with a substantial number of constraints. To deal with such problems, several metaheuristics as well as constraint handling approaches have been suggested. To validate the effectiveness and strength, performance of a newly designed approach should be benchmarked by using some complex real-world problems, instead of only the toy problems with synthetic objective functions, mostly arising from the area of numerical analysis. A list of standard real-life problems appears to be the need of the time for benchmarking new algorithms in an efficient and unbiased manner. In this study, a set of 57 real-world Constrained Optimization Problems (COPs) are described and presented as a benchmark suite to validate the COPs. These problems are shown to capture a wide range of difficulties and challenges that arise from the real life optimization scenarios. Three state-of-the-art constrained optimization methods are exhaustively tested on these problems to analyze their hardness. The experimental outcomes reveal that the selected problems are indeed challenging to these algorithms, which have been shown to solve many synthetic benchmark problems easily.
期刊介绍:
Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.