A hybrid meta-heuristic optimization method is introduced to efficiently find the optimal shape of concrete gravity dams including dam-water-foundation rock interaction subjected to earthquake loading. The hybrid meta-heuristic optimization method is based on a hybrid of gravitational search algorithm (GSA) and particle swarm optimization (PSO), which is called GSA-PSO. The operation of GSA-PSO...

We present a comparative assessment of the accuracy of two different approaches for evaluating dispersion interactions: interatomic pairwise corrections and semiempirical meta-generalized-gradient-approximation (meta-GGA)-based functionals. This is achieved by employing conventional (semi)local and (screened-)hybrid functionals, as well as semiempirical hybrid and nonhybrid meta-GGA functionals...

This paper investigates the problem of just-in-time permutation flow shop scheduling with limited buffers and linear job deterioration in an uncertain environment. The fuzzy set theory is applied to describe this situation. A novel mixed-integer nonlinear program is presented to minimize the weighted sum of fuzzy earliness and tardiness penalties. Due to the computational complexities, the prop...

This study explores the degree to which GGA, meta-GGA, hybrid GGA, and hybrid meta-GGA functionals of density functional theory (DFT), when used with the 6-31+G(d) basis set, are able to reproduce the MP2/6-31+G(d) structures and energetics of the species involved in the reactions of halomethyllithium carbenoids with ethylene. While many popular DFT functionals have been parametrized and/or ben...

The hybrid skewed predictor 2bcgskew (Figure 1) was proposed in [4]. The Alpha EV8 branch predictor [3] was derived from this proposal. The original hybrid predictor 2bcgskew combines an e-gskew predictor [2] and a bimodal predictor. 2bcgskew consists of four 2-bit counters banks. Bank BIM is the bimodal predictor, but is also part of the e-gskew predictor. Banks G0 and G1 are two gshare-like p...

In last few decades, the application of biological methods and systems to the study and design of engineering systems and modern technologies have fascinated many researchers. Numerous mathematical and meta-heuristic algorithms for solving optimization problems have been developed and widely used in both theoretical study and practical applications. To realize the application of such meta-heuri...

We present a hybrid solver (called GELATO) that exploits the potentiality of a Constraint Programming (CP) environment (Gecode) and of a Local Search (LS) framework (EasyLocal++). GELATO allows the user to easily develop and use hybrid meta-heuristic combining CP and LS phases (in particular Large Neighborhood Search). We tested some hybrid algorithms on different instances of the Asymmetric Tr...

Abstract—This paper describes how to dimension the electric components of a 48V hybrid system considering real customer use. Furthermore, it provides information about savings in energy and CO2 emissions by a customer-tailored 48V hybrid. Based on measured customer profiles, the electric units such as the electric motor and the energy storage are dimensioned. Furthermore, the CO2 reduction pote...

The performance of 23 density functionals, including one LDA, four GGAs, three meta-GGAs, three hybrid GGAs, eight hybrid meta-GGAs, and ten double-hybrid functionals, was investigated for the computation of activation energies of various covalent main-group single bonds by four catalysts: Pd, PdCl(-), PdCl2, and Ni (all in the singlet state). A reactant complex, the barrier, and reaction energ...

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters, (e.g., the kernel and regularization parameters). In the current work, we propose the combination of Meta-Learning and Search algorithms to deal with the problem of SVM parameter selection. In this...