Pierre Hansen – författare

Global optimization aims at solving the most general problems of deterministic mathematical programming: to find the global optimum of a nonlinear, nonconvex, multivariate function of continuous and/or integer variables subject to constraints which may be themselves nonlinear and nonconvex. In addition, once the solutions are found, proof of its optimality is also expected from this methodology. Therefore, with these difficulties in mind, global optimization is becoming an increasingly powerful and important methodology. Essays and Surveys in Global Optimization is the most recent examination of its mathematical capability, power, and wide ranging solutions to many fields in the applied sciences.

The field of metaheuristics has been fast evolving. Techniques such as simulated annealing, tabu search, genetic algorithms, scatter search, greedy randomized adaptive search, variable neighbourhood search, ant systems, and their hybrids are currently among the most efficient and robust optimization strategies to find high-quality solutions to many real-life optimization problems. A very large number of successful applications of metaheuristics are reported in the literature and spread throughout many books, journals, and conference proceedings. A series of international conferences entirely devoted to the theory, applications, and computational developments in metaheuristics has been attracting an increasing number of participants, from universities and the industry. For this text, specialists have written surveys on the following subjects: simulated annealing (E. Aarts and J. Korst, The Netherlands): noising methods (I. Charon and O. Hudry, France); strategies for the parallel implementation of metaheuristics (V.-D. Cung and C. Roucairol, France, and S.L. Martins and C.C. Ribeiro, Brazil); greedy randomized adaptive search procedures (P. Festa, Italy, and M.G.C.Resende, USA); tabu search (M. Gendreau, Canada); variable neighborhood search (P. Hansen and N. Mladenovic, Canada), ant colonies (V. Maniezzo and A. Carbonaro, Italy); and evolutionary algorithms (H. Mohlenbein and Th. Mahnig, Germany). Several further essays address issues or variants of metaheuristics, as well as innovative or successful applications of metaheuristics to classical or new combinatorial optimization problems.

Partitioning data sets into disjoint groups is a problem arising in many domains. The theory of cluster analysis aims to find groups that are both homogeneous (entities in the same group that are similar) and well separated (entities in different groups that are dissimilar). There has been rapid expansion in the axiomatic foundations and the computational complexity of such problems and in the design and analysis of exact or heuristic algorithms to solve them. Applications have burgeoned in psychology, computer vision, target tracking, and other areas. This book contains papers presented at the workshop Partioning Data Sets held at DIMACS in April 1993. Some of the papers cover the main paradigms of the field of cluster analysis methods and algorithms. Other topics include partitioning problems arising from multitarget tracking and surveillance and from computer and human vision. The multiplicity of approaches, methods, problems, and algorithms make for lively and informative reading.

Finding exact solutions to many combinatorial optimization problems in busi­ ness, engineering, and science still poses a real challenge, despite the impact of recent advances in mathematical programming and computer technology. New fields of applications, such as computational biology, electronic commerce, and supply chain management, bring new challenges and needs for algorithms and optimization techniques. Metaheuristics are master procedures that guide and modify the operations of subordinate heuristics, to produce improved approx­ imate solutions to hard optimization problems with respect to more simple algorithms. They also provide fast and robust tools, producing high-quality solutions in reasonable computation times. The field of metaheuristics has been fast evolving in recent years. Tech­ niques such as simulated annealing, tabu search, genetic algorithms, scatter search, greedy randomized adaptive search, variable neighborhood search, ant systems, and their hybrids are currently among the most efficient and robust optimization strategies to find high-quality solutions to many real-life optimiza­ tion problems. A very large nmnber of successful applications of metaheuristics are reported in the literature and spread throughout many books, journals, and conference proceedings. A series of international conferences entirely devoted to the theory, applications, and computational developments in metaheuristics has been attracting an increasing number of participants, from universities and the industry.

Global optimization aims at solving the most general problems of deterministic mathematical programming: to find the global optimum of a nonlinear, nonconvex, multivariate function of continuous and/or integer variables subject to constraints which may be themselves nonlinear and nonconvex. In addition, once the solutions are found, proof of its optimality is also expected from this methodology. Therefore, with these difficulties in mind, global optimization is becoming an increasingly powerful and important methodology. Essays and Surveys in Global Optimization is the most recent examination of its mathematical capability, power, and wide ranging solutions to many fields in the applied sciences.