Engineering Optimization and Industrial Applications Xin-She Yang Abstract Design optimization is important in engineering and industrial appli- cations. It is usually very challenging to find optimum designs, which require both efficient optimization algorithms and high-quality simulators that are often time-consuming. To some extent, an optimization process is equivalent to a self- organizing system, and the organized states are the optima that are to be searched for. In this chapter, we discuss both optimization and self-organization in a unified framework, and we use three metaheuristic algorithms, the firefly algorithm, the bat algorithm and cuckoo search, as examples to see how this self-organized process works. We then present a set of nine design problems in engineering and industry. We also discuss the challenging issues that need to be addressed in the near future. Keywords Bat algorithm · Cuckoo search · Firefly algorithm · Optimization · Metaheuristic · Self-organizaion 1 Introduction Optimization is ubiquitous in many applications in engineering and industry. In essence, optimization is a process of searching for the optimal solutions to a par- ticular problem of interest, and this search process can be carried out using multiple agents which essentially form a system of evolving agents. This system can evolve by iterations according to a set of rules or mathematical equations. Consequently, such systems will show some emergent characteristics, leading to self-organizing states which correspond to some optima in the search space. Once the self-organized states are reached, we say the system has converged. Therefore, the design of an efficient optimization algorithm is equivalent to mimicking the evolution of a self- organizing system. In almost all applications in engineering and industry, we are always trying to optimize something—whether to minimize the cost and energy consumption, or to X.-S. Yang (B ) School of Science and Technology, Middlesex University, London NW4 4BT, UK e-mail: X.Yang@mdx.ac.uk S. Koziel, L. Leifsson (eds.), Surrogate-Based Modeling and Optimization, DOI 10.1007/978-1-4614-7551-4_16, © Springer Science+Business Media New York 2013 393