2021年最新SCI期刊影响因子查询系统
JOURNAL OF HEURISTICS 期刊详细信息
基本信息
期刊名称 | JOURNAL OF HEURISTICS JOURNAL OF HEURISTICS |
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期刊ISSN | 1381-1231 |
期刊官方网站 | http://link.springer.com/journal/10732 |
是否OA | 否 |
出版商 | Springer Netherlands |
出版周期 | Bimonthly |
始发年份 | |
年文章数 | 32 |
最新影响因子 | 2.247(2021) |
中科院SCI期刊分区
大类学科 | 小类学科 | Top | 综述 |
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工程技术4区 | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机:人工智能4区 | 否 | 否 |
COMPUTER SCIENCE, THEORY & METHODS 计算机:理论方法4区 |
CiteScore
CiteScore排名 | CiteScore | SJR | SNIP | ||
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学科 | 排名 | 百分位 | 2.00 | 0.528 | 1.288 |
Mathematics Control and Optimization |
25 / 92 | 73% |
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Decision Sciences Management Science and Operations Research |
50 / 148 | 66% |
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Computer Science Artificial Intelligence |
76 / 189 | 60% |
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Computer Science Computer Networks and Communications |
97 / 274 | 64% |
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Computer Science Information Systems |
106 / 269 | 60% |
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Computer Science Software |
156 / 360 | 56% |
补充信息
自引率 | 1.40% |
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H-index | 55 |
SCI收录状况 |
Science Citation Index Expanded |
官方审稿时间 | Submission to first decision 11 days |
网友分享审稿时间 | 数据统计中,敬请期待。 |
PubMed Central (PML) | http://www.ncbi.nlm.nih.gov/nlmcatalog?term=1381-1231%5BISSN%5D |
投稿指南
期刊投稿网址 | https://www.editorialmanager.com/heur/default.aspx |
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收稿范围 | The following areas are of interest to the journal: Adaptive Memory Programming Rafael Martí Universitat de València, Spain rafael.marti@uv.es Tabu search (TS), scatter search (SS) and path reliniking (PR) have become the focus of numerous comparative studies and practical applications. Fruitful discoveries about preferred strategies for solving difficult optimization problems have surfaced as a result. Advanced TS, SS and PR methods exploit a collection of memory components that are referred to as adaptive memory programming (AMP). AMP procedures embody a framework that goes beyond simple mechanisms to incorporate intensification and diversification strategies. The AMP area welcomes submissions that describe innovative applications or the development of new methodology for the use of memory within heuristic search procedures. Artificial Intelligence and Constraint Programming Pascal Van Hentenryck Brown University, USA pvh@cs.brown.edu This area covers heuristic search based on artificial intelligence techniques and their application to artificial intelligence planning, scheduling, design, constraint satisfaction, and game theory. It includes heuristics and metaheuristics for both systematic and local search in artificial intelligence. Of particular interest are heuristics based on constraint programming, a field that emerges from artificial intelligence and is an orthogonal and complementary approach to mathematical programming. This includes heuristics based on hybrid methods, e.g., those that combine local search and constraint programming or integrate constraint and mathematical programming. Combinatorial Optimization Edmund Burke University of Nottingham, UK E.Burke@cs.nott.ac.uk Alain Hertz GERAD, Canada Alain.Hertz@gerad.ca This area will act as an international forum for heuristic and metaheuristic research and development for combinatorial optimization problems. We welcome papers that present high quality, innovative and original research ideas and applications from across a wide spectrum of theoretical and applied research issues. Themes that are relevant to this area include (but are not limited to) graph theoretic problems and applications, sequencing and scheduling, engineering design, routing, cutting and packing, and set covering. In particular, we welcome inter-disciplinary approaches that attempt to work at the interface of relevant disciplines (such as Operational Research, Computer Science, Artificial Intelligence, Engineering and Management). Evolutionary Computation Juergen Branke University of Warwick, UK mail@branke.net Carlos A. Coello CINVESTAV-IPN, Mexico ccoello@cs.cinvestav.mx This area welcomes manuscripts on all aspects of evolutionary computation. Submissions covering advances in the theory, practice, and application of all evolutionary techniques, either individually or collectively, are encouraged. Topics of interest include, but are not limited to evolutionary approaches to optimization and machine learning, evolution of emergent properties, characterization of problems suitable for evolutionary algorithms, implementation issues of evolutionary algorithms, parallel computation of evolutionary algorithms, and applications of evolutionary computation to various problems in science, engineering, and business. Manuscripts on hybrid procedures, combining existing heuristic techniques including methodologies from other areas of operation research and computer science, are of great interest. Also of great interest are implementations of robust artificial systems that use evolutionary computation as a key component of their architecture. In particular, manuscripts discussing methodological innovations that can by applied to a wide range of problems are strongly encouraged. Additionally, survey papers describing state-of-the-art developments in a given field (including the perspective from evolutionary techniques) are welcomed. Large-Scale Optimization and Decomposition Methods Éric Taillard HEIG-VD, University of Applied Sciences of Western Switzerland eric.taillard@heig-vd.ch This area seeks papers that apply heuristic search methods to large-scale optimization problems that might require different types of decomposition approaches. This includes problems found in practice that must be decomposed as a sequence of simpler problems as well as large instances that are decomposed into smaller parts that are subsequently optimized independently. Typical methodologies applied to problems in this area include Large Neighborhood Search and Matheuristics, i.e., hybrids of metaheuristics and mathematical programming techniques. Logistics and Supply Chain Emrah Demir Cardiff University DemirE@cardiff.ac.uk This area seeks papers that creatively apply heuristic search methods to important decision problems in logistics and supply chain problems. This includes problems from fields such as facility location, transportation and scheduling. Many decision problems in logistics and supply chain management may be formulated as optimization problems. Typically, these problems are too difficult to be solved exactly within a reasonable amount of time and heuristics become the methodology of choice. In cases where simply obtaining a feasible solution is not satisfactory, but where the quality of solution is critical, it becomes important to investigate efficient procedures to obtain the best possible solutions within time limits that are deemed practical. We seek well-written papers that span the full spectrum from theory to practice regarding heuristics and metaheuristics applications to logistics and supply chain problems. Papers may make a methodological contribution; contribute by developing and analyzing novel models motivated by current industrial problems and case studies. Contributions must be significant, relevant, and conceptually sound and must meet the rigorous standards of the journal. Metaheuristic Methodologies Holger H. Hoos University of British Columbia hoos@cs.ubc.ca Marc Sevaux University of South-Brittany, France marc.sevaux@univ-ubs.fr The Metaheuristics Methodologies area seeks to publish papers that espouse new general-purpose metaheuristics or provide analysis and comparison of existing methods. In both cases, the papers should provide a clear exposition of the relationship between the research reported in the manuscript and the extant body of research literature. The work can be either theoretical or empirical. If the work is empirical, it is important that sound scientific methods be employed. In particular, it is important the test data sets used in the work be made available to the research community. Additionally, concise statements of hypothesis must be provided along with the evidence that supports them. Both statistical and qualitative significance should be demonstrated. Real-World Applications Anthony Cox, Jr. Cox and Associates, USA TCoxDenver@aol.com This area welcomes manuscripts dealing with practical applications. Papers in this area can deal with new applications for which no previous solution methods exist or for which previous methods have proved unsatisfactory. Of particular interest are those papers that show how the application of heuristic methods has resulted in measurable benefits such as cost reduction or revenue increase. Officially cited as: J Heuristics |
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