Simulated Annealing

This book presents state of the art contributes to Simulated Annealing (SA) that is a well-known probabilistic meta-heuristic. It is used to solve discrete and continuous optimization problems. The significant advantage of SA over other solution methods has made it a practical solution method for solving complex optimization problems. Book is consisted of 13 chapters, classified in single and multiple objectives applications and it provides the reader with the knowledge of SA and several applications. We encourage readers to explore SA in their work, mainly because it is simple and can determine extremely very good results.

An introduction to simulated annealing. This book brings together for the first time many of the theoretical foundations for improvements to algorithms for global optimization that until now existed only in scattered research articles.

In February 1992, I defended my doctoral thesis: Engineering Optimiza tion - selected contributions (IMSOR, The Technical University of Den mark, 1992, p. 92). This dissertation presents retrospectively my central contributions to the theoretical and applied aspects of optimization. When I had finished my thesis I became interested in editing a volume related to a new expanding area of applied optimization. I considered several approaches: simulated annealing, tabu search, genetic algorithms, neural networks, heuristics, expert systems, generalized multipliers, etc. Finally, I decided to edit a volume related to simulated annealing. My main three reasons for this choice were the following: (i) During the last four years my colleagues at IMSOR and I have car ried out several applied projects where simulated annealing was an essential. element in the problem-solving process. Most of the avail able reports and papers have been written in Danish. After a short review I was convinced that most of these works deserved to be pub lished for a wider audience. (ii) After the first reported applications of simulated annealing (1983- 1985), a tremendous amount of theoretical and applied work have been published within many different disciplines. Thus, I believe that simulated annealing is an approach that deserves to be in the curricula of, e.g. Engineering, Physics, Operations Research, Math ematical Programming, Economics, System Sciences, etc. (iii) A contact to an international network of well-known researchers showed that several individuals were willing to contribute to such a volume.

From my B.E.E degree at the University of Minnesota and right through my S.M. degree at M.I.T., I had specialized in solid state devices and microelectronics. I made the decision to switch to computer-aided design (CAD) in 1981, only a year or so prior to the introduction of the simulated annealing algorithm by Scott Kirkpatrick, Dan Gelatt, and Mario Vecchi of the IBM Thomas 1. Watson Research Center. Because Prof. Alberto Sangiovanni-Vincentelli, my UC Berkeley advisor, had been a consultant at IBM, I re ceived a copy of the original IBM internal report on simulated annealing approximately the day of its release. Given my background in statistical mechanics and solid state physics, I was immediately impressed by this new combinatorial optimization technique. As Prof. Sangiovanni-Vincentelli had suggested I work in the areas of placement and routing, it was in these realms that I sought to explore this new algorithm. My flJ'St implementation of simulated annealing was for an island-style gate array placement problem. This work is presented in the Appendix of this book. I was quite struck by the effect of a nonzero temperature on what otherwise appears to be a random in terchange algorithm.

Optimization problems occurring regularly in chemistry, vary from selecting the best wavelength design for optimal spectroscopic concentration predictions to geometry optimization of atomic clusters and protein folding. Numerous optimization tactics have been explored to solve these problems. While most optimizers maintain the ability to locate global optima for simple problems, few are robust against local optima convergence with regard to difficult or large scale optimization problems. Simulated annealing (SA) has shown a great tolerance to local optima convergence and is often called a global optimizer. The optimizaton algorithm has found wide use in numerous areas such as engineering, computer science, communication, image recognition, operation research, physics, and biology. Recently, SA and variations thereof have shown considerable success in solving numerous chemical optimization problems. The main thrust of this book is to demonstrate the use of SA in a wide range of chemical problems. The potentiality of SA, GSA and other modifications of SA to serve specific needs in a variety of chemical disciplines are covered. A detailed discussion on SA and GSA is given in Chapter 1, presenting the theoretical framework from which a computer program can be written by the reader. The remainder of the book describes applications of SA type algorithms to a diverse set of chemical problems. The final chapter contains an algorithm for GSA written in the MatLab programming environment. This program can be easily adapted to any optimization problem and with only slight modifications, can be altered to perform SA. A general flowchart is also given.

This monograph represents a summary of our work in the last two years in applying the method of simulated annealing to the solution of problems that arise in the physical design of VLSI circuits. Our study is experimental in nature, in that we are con cerned with issues such as solution representations, neighborhood structures, cost functions, approximation schemes, and so on, in order to obtain good design results in a reasonable amount of com putation time. We hope that our experiences with the techniques we employed, some of which indeed bear certain similarities for different problems, could be useful as hints and guides for other researchers in applying the method to the solution of other prob lems. Work reported in this monograph was partially supported by the National Science Foundation under grant MIP 87-03273, by the Semiconductor Research Corporation under contract 87-DP- 109, by a grant from the General Electric Company, and by a grant from the Sandia Laboratories.

This book presents state of the art contributes to Simulated Annealing (SA) that is a well-known probabilistic meta-heuristic. It contains 9 chapters explaining SA key concepts, constraint optimisation, hybrid SA combining with other techniques, and several applications: aircraft design, molecule reconstruction, spacecraft trajectory, thermodynamics, truck scheduling and marine protected design scenarios.

The digital age is ripe with emerging advances and applications in technological innovations. Mimicking the structure of complex systems in nature can provide new ideas on how to organize mechanical and personal systems. The Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms is an essential scholarly resource on current algorithms that have been inspired by the natural world. Featuring coverage on diverse topics such as cellular automata, simulated annealing, genetic programming, and differential evolution, this reference publication is ideal for scientists, biological engineers, academics, students, and researchers that are interested in discovering what models from nature influence the current technology-centric world.