Martin Josef Geiger

Walter Habenicht

Marc Sevaux

Kenneth Sörensen

Most developed economics show the tendency of an increasing importance of modern services such as tourism, logistical services, finance, and others. In many cases, complex optimization problems can be found in this context, and the successful operation of modern services often depends on the ability to solve the obtained optimization models. Metaheuristics on the other hand present an interesting problem-resolution paradigm that has attracted considerable interest in past years. The book combines a set of selected and peer-reviewed articles, presenting novel results of metaheuristics for modern services. In particular, applications in the area of transportation and logistics are considered, while other areas include production and financial services. Novel methodological approaches as well as improved results are obtained, resulting in a considerable contribution to the state-of-the-art of research in metaheuristics.

Metaheuristics in the Service Industry

André Rossi

Maria Soto

Johann Laurent

Embedded systems are everywhere in contemporary life and aresupposed to make our lives more comfortable. In industry, embeddedsystems are used to manage and control complex systems (e.g.nuclear power plants, telecommunications and flight control) andthey are also taking an important place in our daily activities(e.g. smartphones, security alarms and traffic lights). In the design of embedded systems, memory allocation and dataassignment are among the main challenges that electronic designershave to face. In fact, they impact heavily on the main cost metrics(power consumption, performance and area) in electronic devices.Thus designers of embedded systems have to pay careful attention inorder to minimize memory requirements, thus improving memorythroughput and limiting the power consumption by the system’smemory. Electronic designers attempt to minimize memoryrequirements with the aim of lowering the overall systemcosts. A state of the art of optimization techniques for memory managementand data assignment is presented in this book.

Optimization Methods

Memory Allocation Problems in Embedded Systems

Jin-Kao Hao

Matthias Ehrgott

Carlos M. Fonseca

Xavier Gandibleux

Multi-criterionoptimizationreferstooptimizationproblemswithtwoormore- jectives expressing con?icting goals that are formulated within a mathematical programming framework. The problems addressed may involve linear or nonl- ear objective functions and/or constraints, continuous or discrete variables, and may or may not be a?ected by uncertainty in the data. This branch of multiple criteria decision making (MCDM) ?nds application in numerous domains: en- neering design, health, transportation,telecommunications, bioinformatics, etc. The concept of a unique optimal solution does not apply as soon as multiple objectives are optimized simultaneously. The models and methods introduced in multi-criterion optimization deal with the concept of a set of e?cient (also called Pareto optimal) solutions. E?cient solutions imply trade-o?s between the di?erentcriteria. Thecomputationofthee?cientsolutionsetmaybehardwhen the size of the problem is large, when the problem is computationally complex, when the data are not crisp. It is then often impossible to guarantee the com- tation of exact solutions. In that case, approximate solutions, i. e. , sub-optimal solutionscomputedwithlimitedandcontrolledresources,suchasavailabletime, are of interest. This is the domain of multi-objective metaheuristics, of which evolutionary multi-criterion optimization (EMO) is de?nitely the most pro- nent representative. The success of EMO is due to the simplicity of its concepts and the generality of its methods, and is clearly expressed by the many impr- sive success stories reported in the literature. Research activities in EMO have boomed since the mid-1990s. Three g- erations of work are identi?able throughout the years.

5th International Conference, EMO 2009, Nantes, France, April 7-10, 2009, Proceedings

Evolutionary Multi-Criterion Optimization

Vincent T'Kindt

The success of metaheuristics on hard single-objective optimization problems is well recognized today. However, many real-life problems require taking into account several conflicting points of view corresponding to multiple objectives. The use of metaheuristic optimization techniques for multi-objective problems is the subject of this volume. The book includes selected surveys, tutorials and state-of-the-art research papers in this field, which were first presented at a free workshop jointly organized by the French working group on Multi-objective Mathematical Programming (PM2O) and the EURO working group on Metaheuristics in December 2002. It is the first book which considers both various metaheuristics and various kind of problems (e.g. combinatorial problems, real situations, non-linear problems) applied to multiple objective optimization. Metaheuristics used include: genetic algorithms, ant colony optimization, simulated annealing, scatter search, etc. Problems concern timetabling, vehicle routing, and more. Methodological aspects, such as quality evaluation, are also covered.

Metaheuristics for Multiobjective Optimisation

This book constitutes the refereed proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2009, held in Nantes, France in April 2009. The 39 revised full papers presented together with 5 invited talks were carefully reviewed and selected from 72 submissions. The papers are organized in topical sections on theoretical analysis, uncertainty and noise, algorithm development, performance analysis and comparison, applications, MCDM Track, Many objectives, alternative methods, as well as EMO and MCDA.

Marc Sevaux

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