We study the combinatorial assignment domain, which includes auctions and course allocation. The main challenge in this domain is that bundle space grows exponentially number of items. To address this, several papers have recently proposed machine learning-based preference elicitation algorithms aim to elicit only most important information from agents. However, shortcoming prior work it does n...

Real-world problems are becoming highly complex and therefore have to be solved with combinatorial optimization (CO) techniques. Motivated by the strong increase in publications on CO, 8393 articles from this research field subjected a bibliometric analysis. The corpus of literature is examined using mathematical methods novel algorithm for keyword In addition most relevant countries, organizat...

Crossover for evolutionary algorithms applied to permutation problems is a difficult and widely discussed topic. In this paper we use ideas from ant colony optimization to design a new permutation crossover operator. One of the advantages of the new crossover operator is the ease to introduce problem specific heuristic knowledge. Empirical tests on a travelling salesperson problem show that the...

The paper presents Discrete PSO algorithm for document clustering problems. This algorithm is hybrid of PSO with GA operators. The proposed system is based on population-based heuristic search technique, which can be used to solve combinatorial optimization problems, modeled on the concepts of cultural and social rules derived from the analysis of the swarm intelligence (PSO) with GA operators ...

This paper briefly describes three well-established frameworks for handling uncertainty in optimization problems. Our focus is mainly on combinatorial optimization and on the development of approximation algorithms under the discussed frameworks. In particular, we give a brief overview of Stochastic Programming, Robust Optimization, and Probabilistic Combinatorial Optimization, and list approxi...

Shifted combinatorial optimization is a new nonlinear optimization framework, which is a broad extension of standard combinatorial optimization, involving the choice of several feasible solutions at a time. It captures well studied and diverse problems ranging from congestive to partitioning problems. In particular, every standard combinatorial optimization problem has its shifted counterpart, ...