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Chapter 3
DOI: 10.4018/978-1-5225-6164-4.ch003
ABSTRACT
A set of metaheuristics has proved its efficiency in solving rapidly NP-hard problems. Several combi-
natorial 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.
Bat Algorithm With
Generalized Fly for
Combinatorial Production
Optimization Problems:
Case Studies
Latifa Dekhici
University of Sciences and the Technology of Oran, Algeria
Khaled Guerraiche
National School of Electrical Engineering and Energy of Oran, Algeria
Khaled Belkadi
University of Sciences and the Technology of Oran, Algeria