Improved Branch-and-Bound for Low Autocorrelation Binary Sequences

The Low Autocorrelation Binary Sequence problem has applications in telecommunications, is of theoretical interest to physicists, and has inspired many optimisation researchers. Metaheuristics for the problem have progressed greatly in recent years but complete search has not progressed since a branch-and-bound method of 1996. In this paper we find four ways of improving branch-and-bound, leading to a tighter relaxation, faster convergence to optimality, and better empirical scalability.Comment: Journal paper in preparatio

A Memetic Algorithm for the Low Autocorrelation Binary Sequence Problem ABSTRACT

Finding binary sequences with low autocorrelation is a very hard problem with many practical applications. In this paper we analyze several metaheuristic approaches to tackle the construction of this kind of sequences. We focus on two different local search strategies, steepest descent local search (SDLS) and tabu search (TS), and their use both as stand-alone techniques and embedded within a memetic algorithm (MA). Plain evolutionary algorithms are shown to perform worse than stand-alone local search strategies. However, a MA endowed with TS turns out to be a stateof-the-art algorithm: it consistently finds optimal sequences in considerably less time than previous approaches reported in the literature

A memetic algorithm for the low autocorrelation binary sequence problem, in: D. Thierens, et al

Finding binary sequences with low autocorrelation is a very hard problem with many practical applications. In this paper we analyze several metaheuristic approaches to tackle the construction of this kind of sequences. We focus on two different local search strategies, steepest descent local search (SDLS) and tabu search (TS), and their use both as stand-alone techniques and embedded within a memetic algorithm (MA). Plain evolutionary algorithms are shown to perform worse than stand-alone local search strategies. However, a MA endowed with TS turns out to be a stateof-the-art algorithm: it consistently finds optimal sequences in considerably less time than previous approaches reported in the literature