The following areas are of interest to the journal:
Adaptive Memory Programming
Universitat de València, Spain
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
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.
University of Nottingham, UK
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).
University of Warwick, UK
Carlos A. Coello
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
HEIG-VD, University of Applied Sciences of Western Switzerland
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
Teodor Gabriel Crainic
Université du Québec à Montréal
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.
Holger H. Hoos
University of British Columbia
University of South-Brittany, France
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.
Anthony Cox, Jr.
Cox and Associates, USA
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