Many problem-specific heuristic frameworks have been developed to solve combinatorial optimization problems, but these do not generalize well other problem domains. Metaheuristic aim be more generalizable compared traditional heuristics, however their performances suffer from poor selection of low-level heuristics (operators) during the search process. An example in a metaheuristic framework is...

This paper surveys the recent attempts, both from machine learning and operations research communities, at leveraging to solve combinatorial optimization problems. Given hard nature of these problems, state-of-the-art algorithms rely on handcrafted heuristics for making decisions that are otherwise too expensive compute or mathematically not well defined. Thus, looks like a natural candidate ma...

The Traveling Salesman Problem (TSP) is one of the most famous problems in combinatorial optimization. In this paper, we are going to examine how the techniques of Guided Local Search (GLS) and Fast Local Search (FLS) can be applied to the problem. GLS sits on top of local search heuristics and has as a main aim to guide these procedures in exploring eciently and e€ectively the vast search spa...

The linear ordering problem consists of finding an acyclic tournament in a complete weighted digraph of maximum weight. It is one of the classical NP-hard combinatorial optimization problems. This paper surveys a collection of heuristics and metaheuristic algorithms for finding near-optimal solutions and reports about extensive computational experiments with them. We also present the new benchm...

Combinatorial optimization is a fundamental problem found in many fields. In many real life situations, the constraints and the objective function forming the optimization problem are naturally distributed amongst different sites in some fashion. A typical approach for solving such problem is to collect all of this information together and centrally solve the problem. However, this requires all...

Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd algorithmics for hard problems i...

Abstract In multiobjective optimization, the result of an optimization algorithm is a set efficient solutions from which decision maker selects one. It common that not all can be computed in short time and search has to stopped prematurely analyze found so far. A are well-spread objective space preferred provide with great variety solutions. However, just few exact algorithms literature exist a...

Disasters are large-scale disruptions for the society that cause damage to human lives, infrastructure, and ecosystem. Multiple emergencies emerge, which need be handled resolved efficiently minimize impact of disaster. Therefore, an effective efficient emergency response system optimally allocates resources services is essential. This paper proposes a methodology model this situation as optimi...

The Shortest Common Supersequence Problem (SCSP) is a well-known hard combinatorial optimization problem that formalizes many real world problems. This paper presents a novel randomized search strategy, called probabilistic beam search (PBS), based on the hybridization between beam search and greedy constructive heuristics. PBS is competitive (and sometimes better than) previous state-of-the-ar...