Hindawi Publishing Corporation Abstract and Applied Analysis Volume 2013, Article ID 213853, 11 pages http://dx.doi.org/10.1155/2013/213853 Research Article Simulated Annealing-Based Krill Herd Algorithm for Global Optimization Gai-Ge Wang, 1,2 Lihong Guo, 1 Amir Hossein Gandomi, 3 Amir Hossein Alavi, 4 and Hong Duan 5 1 Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China 2 Graduate School of Chinese Academy of Sciences, Beijing 100039, China 3 Department of Civil Engineering, University of Akron, Akron, OH 44325-3905, USA 4 Department of Civil and Environmental Engineering, Engineering Building, Michigan State University, East Lansing, MI 48824, USA 5 School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China Correspondence should be addressed to Lihong Guo; guolh@ciomp.ac.cn Received 27 December 2012; Accepted 1 April 2013 Academic Editor: Mohamed Tawhid Copyright © 2013 Gai-Ge Wang et al. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Recently, Gandomi and Alavi proposed a novel swarm intelligent method, called krill herd (KH), for global optimization. To enhance the performance of the KH method, in this paper, a new improved meta-heuristic simulated annealing-based krill herd (SKH) method is proposed for optimization tasks. A new krill selecting (KS) operator is used to reine krill behavior when updating krill’s position so as to enhance its reliability and robustness dealing with optimization problems. he introduced KS operator involves greedy strategy and accepting few not-so-good solutions with a low probability originally used in simulated annealing (SA). In addition, a kind of elitism scheme is used to save the best individuals in the population in the process of the krill updating. he merits of these improvements are veriied by fourteen standard benchmarking functions and experimental results show that, in most cases, the performance of this improved meta-heuristic SKH method is superior to, or at least highly competitive with, the standard KH and other optimization methods. 1. Introduction In management science, mathematics, and economics, the process of optimization is the selection of the best solution from some set of feasible alternatives. More generally, opti- mization consists of inding the optimal values of some objec- tive function within a given domain. In general, a great many optimization techniques have been developed and applied to solve optimization problems [1]. A general classiication way for these optimization techniques is considering the nature of these techniques, and these optimization techniques can be categorized into two main groups: deterministic methods and modern intelligent algorithms. Deterministic methods using gradient such as hill climbing follow a rigorous step and will repeat the process of optimization if the iterations start with the same initial starting point. Eventually, they will reach the same set of solutions. On the other hand, modern intelligent algorithms without adopting gradient always have some randomness, and the process of optimization cannot be repeatable even with the same initial value. However, generally, the inal solutions, though slightly diferent, will arrive at the same optimal values within a given accuracy [2]. he growth of stochastic optimization methods as a blessing from the mathematical and computing theorem has opened up a new facet to complete the optimization of a func- tion. Recently, nature-inspired metaheuristic methods per- form eiciently and efectively in solving modern nonlinear numerical global optimization problems. To some extent, all metaheuristic methods make an attempt at making balance between diversiication (global search) and intensiication (local search) [2, 3]. Inspired by nature, these strong metaheuristic methods have ever been applied to solve a variety of complicated problems, such as task-resource assignment [4], constrained