Simulated annealing and Tabu search in the long run: A comparison on QAP tasks☆

AbstractSimulated Annealing (SA) and Tabu Search (TS) are compared on the Quadratic Assignment Problem. A recent work on the same benchmark suite argued that SA could achieve a reasonable solution quality with fewer function evaluations than TS. The discussion is extended by showing that the conclusions must be changed if the task is hard or a very good approximation of the optimal solution is desired, or if CPU time is the relevant parameter. In addition, a recently proposed version of TS (the Reactive Tabu Search) solves the problem of finding the proper list size with an automatic memory-based reaction mechanism

QPACE 2 and Domain Decomposition on the Intel Xeon Phi

We give an overview of QPACE 2, which is a custom-designed supercomputer based on Intel Xeon Phi processors, developed in a collaboration of Regensburg University and Eurotech. We give some general recommendations for how to write high-performance code for the Xeon Phi and then discuss our implementation of a domain-decomposition-based solver and present a number of benchmarks.Comment: plenary talk at Lattice 2014, to appear in the conference proceedings PoS(LATTICE2014), 15 pages, 9 figure

The Reactive Tabu Search

We propose an algorithm for combinatorial optimization where an explicit check for the repetition of configurations is added to the basic scheme of Tabu search. In our Tabu scheme the appropriate size of the list is learned in an automated way by reacting to the occurrence of cycles. In addition, if the search appears to be repeating an excessive number of solutions excessively often, then the search is diversified by making a number of random moves proportional to a moving average of the cycle length. The reactive scheme is compared to a "strict" Tabu scheme that forbids the repetition of configurations and to schemes with a fixed or randomly varying list size. From the implementation point of view we show that the Hashing or Digital Tree techniques can be used in order to search for repetitions in a time that is approximately constant. We present the results obtained for a series of computational tests on a benchmark function, on the 0-1 Knapsack Problem, and on the Quadratic Assignment Problem. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499

The continuous reactive tabu search: Blending combinatorial optimization and stochastic search for global optimization

A novel algorithm for the global optimization of functions (C-RTS) is presented, in which a combinatorial optimization method cooperates with a stochastic local minimizer. The combinatorial optimization component, based on the Reactive Tabu Search recently proposed by the authors, locates the most promising "boxes", in which starting points for the local minimizer are generated. In order to cover a wide spectrum of possible applications without user intervention, the method is designed with adaptive mechanisms: the box size is adapted to the local structure of the function to be optimized, the search parameters are adapted to obtain a proper balance of diversification and intensification. The algorithm is compared with some existing algorithms, and the experimental results are presented for a variety of benchmark tasks

Learning with First, Second, and No Derivatives: a Case Study in High Energy Physics

this paper different algorithms for training multi-layer perceptron architecture are applied to a significant discrimination task in High Energy Physics. The OneStep Secant technique is compared with On-Line Backpropagation, the "Bold Driver" batch version and Conjugate Gradient methods. In addition, a new algorithm (Affine Shaker) is proposed that uses stochastic search based on function values and affine transformations of the local search region. Although the Affine Shaker requires more CPU time to reach the maximum generalization, the technique can be interesting for special-purpose VLSI implementations and for non-differentiable functions. 1 Introductio

Vector Quantization with the Reactive Tabu Search

: A novel application of the Reactive Tabu Search to Vector Quantization (RTS-VQ) is presented. The results obtained on benchmark tasks demonstrate that a performance similar to that of traditional techniques can be obtained even if the code vectors are represented with small integers. The result is of interest for application-specific VLSI circuits. Key Words: Tabu Search, Vector Quantization, Reactive Heuristics. 1. Introduction Sub-symbolic Machine Learning (ML) has become a paradigm for the development of systems that tackle recognition or classification tasks by mimicking the adaptation and learning capabilities of living organisms. While the "knowledge" in symbolic systems is mainly represented through logical rules (e.g., in Expert Systems, Artificial Intelligence), in sub-symbolic systems the knowledge is implicit in the values of the system parameters. In sub-symbolic systems a strong emphasis is put on the selforganization properties: the system evolves toward a better f..