Computational Intelligence in Reliability Engineering New Metaheuris
This volume contains chapters presenting applications of different metaheuristics (ant colony optimization, great deluge algorithm, cross-entropy method and particle swarm optimization) in reliability engineering. It also includes chapters devoted to cell
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Alice E. Smith Department of Industrial and Systems Engineering, Auburn University
1.1 Introduction This chapter introduces a relatively new meta-heuristic for combinatorial optimization, the ant colony. The ant colony algorithm is a multiple solution global optimizer that iterates to find optimal or near optimal solutions. Like its siblings genetic algorithms and simulated annealing, it is inspired by observation of natural systems, in this case, the behavior of ants in foraging for food. Since there are many difficult combinatorial problems in the design of reliable systems, applying new meta-heuristics to this field makes sense. The ant colony approach with its flexibility and exploitation of solution structure is a promising alternative to exact methods, rules of thumb and other meta-heuristics. The most studied design configuration of the reliability systems is a series system of s independent k-out-ofn :G subsystems as illustrated in Figure 1. A subsystem i is functioning properly if at least ki of itsn i components are operational and a series-parallel system is where k i = one for all subsystems. In this problem, multiple component choices are used in parallel in each subsystem. Thus, the problem is to select the optimal combination of components and redundancy levels to meet system level constraints while maximizing system reliability. Such a redundancy allocation problem (RAP) is NP-hard [6] and has been well studied (see Tillman, et al. [45] and Kuo & Prasad [25]).
Y.-C. Liang and A.E. Smith: The Ant Colony Paradigm for Reliable Systems Design, Computational Intelligence in Reliability Engineering (SCI) 40, 1–20 (2007) © Springer-Verlag Berlin Heidelberg 2007 www.springerlink.com
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Yun-Chia Liang and Alice E. Smith
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Fig. 1. Typical series-parallel system configuration. Exact optimization approaches to the RAP include dynamic programming [2, 20, 35], integer programming [3, 22, 23, 33], and mixed-integer and nonlinear programming [31, 46]. Because of the exponential increase in search space with problem size, heuristics have become a common alternative to exact methods. Meta-heuristics, in particular, are global optimizers that offer flexibility while not being confined to specific problem types or instances. Genetic algorithms (GA) have been applied by Painton & Campbell [37], Levitin et al. [26], and Coit & Smith [7, 8]. Ravi et al. propose simulated annealing (SA) [39], fuzzy logic [40], and a modified great deluge algorithm [38] to optimize the complex system reliability. Kulturel-Konak et al. [24] use a Tabu search (TS) algorithm embedded with an adaptive version of the penalty function in [7] to solve RAPs. Three types of benchmark problems which consider the objectives of system cost minimization and system reliability maximization respectively were used to evaluate the algorithm performance. Liang and Wu [27] employ a variable neighborhood descent (VND) algorithm for the RAP. Four neighborhood search methods are defined
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