Nature-inspired swarm metaheuristics become one of the most powerful methods for optimization. In discrete optimization, the efficiency of an algorithm depends on how it is adapted to the problem. This paper aims to provide a discretization of the Firefly Algorithm (FF) for the scheduling of a specific manufacturing system, which is the mono processors two-stage hybrid flow shop (HFS). This kin...

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 ...

This study poses the feature correspondence problem as a hypergraph node labeling problem. Candidate feature matches and their subsets (usually of size larger than two) are considered to be the nodes and hyperedges of a hypergraph. A hypergraph labeling algorithm, which models the subset-wise interaction by an undirected graphical model, is applied to label the nodes (feature correspondences) a...

In this paper, we propose novel algorithms for inferring the Maximum a Posteriori (MAP) solution of discrete pairwise random field models under multiple constraints. We show how this constrained discrete optimization problem can be formulated as a multi-dimensional parametric mincut problem via its Lagrangian dual, and prove that our algorithm isolates all constraint instances for which the pro...

Personnel scheduling deals with the attribution of a number of duty shifts to a number of workers respecting several types of requirements. In this work, the problem of scheduling physicians in health care departments is studied. This problem is NP-hard, and we propose a flexible Mixed Integer Linear Programming formulation that allows easy modifications for representing different situations an...

This research paper presents an adaptation of the cat swarm optimization (CSO) to solve the traveling salesman problem (TSP). This evolutionary algorithm appeared in 2007 by Chu and Tsai for optimization problems in the continuous case. To solve TSP, which is a discrete problem, we will describe the various operators and operations performed in two different modes of this algorithm, which is th...

In this work, we consider a method of searching of the direction of a wireless network development (the places of new access points or base stations etc.) optimized with criteria of coverage of important territories and minimum cost of equipment and additional needed infrastructure which does not need the execution of special field testings and determination of the exact geometry of elements of...

Multiple vector optimization proposes to optimize a vector-valued function rather than a real-valued one. The motivation of that is quite natural, since most of the real-life problems involve more than one measure to be used as objective function. Multiple objective discrete optimization (MODO) problem is defined as minx∈X f(x) = [f1(x), . . . fp(x)] T , where X ⊆ R is a bounded (discrete) set ...

Discrete optimization problems arise in many different areas and are studied under many different names. In many such problems the quantity to be optimized can be expressed as a sum of functions of a restricted form. Here we present a unifying theory of complexity for problems of this kind. We show that the complexity of a finite-domain discrete optimization problem is determined by certain alg...