Reliability Optimization of Complex Systems Using Cuckoo Search Algorithm

Reliability Optimization of Complex Systems Using Cuckoo Search Algorithm

Anuj Kumar (University of Petroleum and Energy Studies, India), Sangeeta Pant (University of Petroleum and Energy Studies, India) and S. B. Singh (G. B. Pant University of Agriculture and Technology, India)
DOI: 10.4018/978-1-5225-1639-2.ch005
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Abstract

In this chapter, authors briefly discussed about the classification of reliability optimization problems and their nature. Background of reliability and optimization has also been provided separately so that one can clearly understand the basic terminology used in the field of reliability optimization. Classification of various optimization techniques have also been discussed by the authors. Few metaheuristic techniques related to reliability optimization problems like Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been discussed in brief. Thereafter, authors have discussed about Cuckoo Search Algorithm (CSA) which is the main focus of this chapter. Finally, Cuckoo Search Algorithm has been applied for solving reliability optimization problems of two complex systems namely complex bridge system and life support system in space capsule. Simulation results and conclusion have been presented in the last followed by the references.
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Introduction

Almost every one of us is acquainted with the term reliability in day-to-day life. When someone assigns attribute ‘reliable’ to a component or a system (a system may be consist of collection of several components) it precisely mean to say that the same will render service for a good or at least reasonable period of time. Overall corporate success depends on the reliability of a company’s products process and services. Reliability is always a top customer concern and is increasingly vocalized by customers as a major factor in purchasing decisions. All these factors and many more, demanded high reliability in the design and operations of components/systems/equipments in various reliability models of practical utility, at the same time researchers often across with the situations of minimizing the cost. Due to those facts the problem of reliability optimization has gained importance in the last few decades (Kumar & Singh, 2008), (Ram et al., 2013) and various metaheuristic algorithms like Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Cuckoo Search Algorithm (CSA) and Genetic Algorithm (GA) etc. have become popular over the years (Geem et al., 2001), (Qin et al., 2006).

Reliability optimization problems can be classified; according to the types of their decision variables; into three typical problems:

  • 1.

    Reliability allocation problems

  • 2.

    Redundancy allocation problems

  • 3.

    Reliability-redundancy allocation problems.

Reliability allocation is a continuous nonlinear programming problem while redundancy allocation problem can be viewed as a pure integer nonlinear programming problem, and reliability-redundancy allocation is a mixed integer programming problem which is nonlinear in nature (Pant et al., 2015). In general, obtaining optimal reliability design is a tedious task because of the NP-hard nature of reliability optimization problems. Further, it is more tedious and time consuming to find the optimal solutions of those problems through exact/heuristics algorithms because these optimization problems generate a very large search space. Therefore, metaheuristic algorithms are more suitable for solving reliability optimization problems. Recently, many metaheuristics have been employed to solve reliability optimization problems (Kuo & Wan, 2007), (Pant & Singh, 2011), (Pant et al., 2015), (Ravi et al., 1997). The main reason behind the popularity of metaheuristic is that they are inspired by very simple physical phenomena, animals’ behaviors, or evolutionary concepts. Most of them have derivation free mechanism and finally, they make a difference with conventional optimization techniques due to their superior ability of avoiding local optima. Cuckoo search Algorithm is an evolutionary algorithm inspired by the life of a bird family, called Cuckoos and the main inspiration behind this algorithm is the obligate brood parasitism of some cuckoos species in combination with L´evy Flights (Yang & Deb, 2010). In this chapter, authors have applied Cuckoo search algorithm for reliability optimization of complex bridge system and life support system in space capsule.

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