Many real-world optimization problems are combinatorial optimization problems subject to dynamic environments. In such dynamic combinatorial optimization problems (DCOPs), the objective, decision variables and/or constraints may change over time, and so solving DCOPs is a challenging task. Metaheuristics are a good choice of tools to tackle DCOPs because many metaheuristics are inspired by natu...

The ultimate goal of designing a symmetric-key cryptographic primitive often can be formulated as an optimization problem. So far, these problems mainly have been solved with trivial algorithms such as brute force or random search. We show that a more advanced and equally versatile class of search algorithms, called metaheuristics, can help to tackle optimization problems related to design of s...

A growing attention has been devoted in recent years to optimisation in multiobjective contexts. As part of this increasing interest, metaheuristics are being adapted to handle multiple objectives, in an effort that has been motivated by the success of metaheuristics in single-objective contexts. Also as a result of significant recent attention, a number of object-oriented approaches for single...

This chapter describes various approaches to the use of evolutionary algorithms and other metaheuristics in interactive multiobjective optimization. We distinguish the traditional approach to interactive analysis with the use of single objective metaheuristics, the semi-a posteriori approach with interactive selection from a set of solutions generated by a multiobjective metaheuristic, and spec...

The combination of components from different algorithms is currently one of the most successful trends in optimization. The hybridization of metaheuristics such as ant colony optimization, evolutionary algorithms, and variable neighborhood search with techniques from operations research and artificial intelligence plays hereby an important role. The resulting hybrid algorithms are generally lab...

This paper describes the design and implementation of the Parallel Metaheuristics Framework (PMF), a C++ framework for the construction of single and multiobjective metaheuristics utilizing Intel’s Threading Building Blocks library to allow easy parallelization of computationally intensive algorithms. The framework demonstrates a generic approach to the construction of metaheuristics, striving ...

We consider the assignment of program tasks to processors in distributed computing systems such that system cost is minimized and resource constraints are satisfied. Several formulations for this task assignment problem (TAP) have been proposed in the literature. Most of these TAP formulations, however, are NP-complete and thus finding exact solutions is computationally intractable. Recently, s...

Nature-inspired metaheuristics for optimization have proven successful, due to their fine balance between exploration and exploitation of a search space. This balance can be further refined by hybridization. In this paper, we conduct experiments with some of the most promising nature-inspired metaheuristics, for assessing their performance when using them to replace backpropagation as a learnin...

The rapid increase of dimensions and complexity of real life problems makes it more difficult to find optimal solutions by traditional optimization methods. This challenge requires intelligent and sophisticated algorithms to make the right decisions given a set of inputs and a variety of possible actions. In the problem solving arena, this definition is transformed into the term of artificial i...

In this work we discuss some ideas and opinions related with teaching Metaheuristics in Business Schools. The main purpose of the work is to initiate a discussion and collaboration about this topic, with the final objective to improve the teaching and publicity of the area. The main topics to be discussed are the environment and focus of this teaching. We also present a SWOT analysis which lead...