Recently, an improved (l + k) constrainted differential evolution (ICDE) has been proposed and proven to be robust and effective for solving constrainted optimization problems. However, so far, the ICDE has been developed mainly for continuous design variables, and hence it becomes inappropriate for solving layout truss optimization problems which contain both discrete and continuous variables....

in this paper a discrete big bang-big crunch algorithm is applied to optimal design of reinforced concrete planar frames under the gravity and lateral loads. optimization is based on aci 318-08 code. columns are assumed to resist axial loads and bending moments, while beams resist only bending moments. second-order effects are also considered for the compression members, and columns are checked...

In cognitive radio network model consisting of secondary users and primary users, in order to solve the difficult multi-objective spectrum allocation issue about maximizing network efficiency and users’ fairness to access network, this paper proposes a new discrete multi-objective combinatorial optimization mechanism— HJ-DQPSO based on Hooke Jeeves (HJ) and Quantum Particle Swarm Optimization (...

In view of the existing seismic signal analysis model there are some problems is Analysis of the result is bad, the accuracy is not high. This paper puts forward an algorithm based on discrete wavelet and generalized the ICA model of seismic signal analysis. First for continuous Wavelet transform exists redundant of problem, on standard small wave transform algorithm of transform domain in the ...

The development and research of discrete optimization models with logical, resource and other constraints to solve complex products design problems are continued in the article. These models are based on the SAT problem and its generalizations. Particular attention is paid to the questions of design of series of products using special sets of components (”kernels”) that have been suggested by t...

The use of surrogate models is a standard method to deal with complex, realworld optimization problems. The first surrogate models were applied to continuous optimization problems. In recent years, surrogate models gained importance for discrete optimization problems. This article, which consists of three parts, takes care of this development. The first part presents a survey of modelbased meth...

Current state-of-the-art discrete optimization methods struggle behind when it comes to challenging contrast-enhancing discrete energies (i.e., favoring different labels for neighboring variables). This work suggests a multiscale approach for these challenging problems. Deriving an algebraic representation allows us to coarsen any pair-wise energy using any interpolation in a principled algebra...

Gradient-based parameter optimization is commonly used for training neural networks and optimizing the performance of other complex systems that only contain continuously differentiable functions. However, there is a large class of important parameter optimization problems involving systems containing discretevalued functions that do not permit the direct use of gradient-based methods. Examples...