The exploration of hybrid metaheuristics — combination of metaheuristics with concepts and processes from other research areas — has been an important trend in combinatorial optimization research. In this work, we developed a hybrid version of the GRASP metaheuristic which incorporates the path-relinking procedure — a memory-based intensification strategy — and a data mining module. Computation...

The multidimensional knapsack problem (MKP) belongs to a very important class of integer optimization problems. In this study, we propose, develop and test metaheuristics based on the neural-networks paradigm for solving the MKP. We show how domain-specific knowledge can be incorporated within the neural-network framework for solving this NP-Hard problem. We provide a mathematical formulation o...

The growing complexity of real-world problems hasmotivated computer scientists to search for efficient problem-solving methods. Metaheuristics based on evolutionary computa-tion and swarm intelligence are outstanding examples of nature-inspired solution techniques. Inspired by the social spiders, wepropose a novel Social Spider Algorithm (SSA) to solve globaloptimization...

This article is about a tutorial on hybrid metaheuristics which was given at the first edition of the conference Theory and Practice of Natural Computing, held in October 2012 in Tarragona, Spain. Hybrid metaheuristics are techniques for (combinatorial) optimization that result from a combination of algorithmic components originating from different optimization methods. The tutorial covers five...

This paper is a major step towards a pioneering software framework for the reusable design and implementation of parallel metaheuristics on Graphics Processing Units (GPU). The objective is to revisit the ParadisEO framework to allow its utilization on GPU accelerators. The focus is on local search metaheuristics and the parallel exploration of their neighborhood. The challenge is to make the G...

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In this survey we discuss different state-of-the-art approaches of combining exact algorithms and metaheuristics to solve combinatorial optimization problems. Some of these hybrids mainly aim at providing optimal solutions in shorter time, while others primarily focus on getting better heuristic solutions. The two main categories in which we divide the approaches are collaborative versus integr...

Over the years, several metaheuristics have been developed to solve hard constrained and unconstrained optimization problems. In general, a metaheuristic is proposed and following researches are made to improve the original algorithm. In this paper, we evaluate a not so new metaheuristic called differential evolution (DE) to solve constrained engineering design problems and compare the results ...

This paper focuses on the selection of a technical tool for the establishment of functional airspace blocks in Europe. This paper shows that the creation of functional airspace blocks is a partitioning problem. Some state-ofthe-art partitioning libraries and two metaheuristics are applied to this problem. Comparisons have been made between these libraries and metaheuristics. Results show that t...

We first present a literature review of heuristics and metaheuristics developed for the problem of coloring graphs. We then present a Greedy Randomized Adaptive Search Procedure (GRASP) for coloring sparse graphs. The procedure is tested on graphs of known chromatic number, as well as random graphs with edge probability 0.1 having from 50 to 500 vertices. Empirical results indicate that the pro...