Abstract
A set of metaheuristics has proved its efficiency in solving rapidly NP-hard problems. Several combinatorial and continuous optimization areas drew profit from these powerful alternative techniques. This chapter intends to describe a discrete version of bat algorithm (BA) combined to generalized walk evolutionary (GEWA), also called bat algorithm with generalized fly or walk (BAG) in order to solve discrete industrial optimization. The first case of study is the well-known hybrid flow shop scheduling. The second one concerns the operating theatre that represents a critical manufacturing system, as the products delivered are patients. The last problem is the redundancy optimization (ROP) for series-parallel multi-state power system (MSS). Its resolution involves the selection of components with an appropriate level of redundancy to maximize system reliability with constrained cost. A universal moment generating function (UMGF) is used to estimate reliabilities. The modified bat algorithm on specific benchmarks was compared with the original one, and other results taken from the literature of each case study.
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