The importance of high performance algorithms for tackling difficult optimization problems cannot be understated, and in many cases the most effective methods are metaheuristics. When designing a metaheuristic, simplicity should be favored, both conceptually and in practice. Naturally, it must also lead to effective algorithms, and if possible, general purpose ones. If we think of a metaheurist...

Nowadays, mobile communications are experiencing a strong growth, being more and more indispensable. One of the key issues in the design of mobile networks is the frequency assignment problem (FAP). This problem is crucial at present and will remain important in the foreseeable future. Real-world instances of FAP typically involve very large networks, which can be handled only by heuristic meth...

The metaheuristics algorithm is increasingly important in solving many kinds of real-life optimization problems but the implementation involves programming difficulties. As a result, many researchers have relied on software framework to accelerate the development life cycle. However, the available software frameworks were mostly designed for rapid development rather than flexible programming. T...

Nature-inspired metaheuristics found various applications in different fields of science, including the problem of artificial neural networks (ANN) training. However, very versatile opinions regarding the performance of metaheuristics applied to ANN training may be found in the literature. Both nature-inspired metaheuristics and ANNs are widely applied to various geophysical and environmental p...

Metaheuristics are a class of promising algorithms for combinatorial optimization. A basic implementation of a metaheuristic typically requires rather little development effort. With a significantly larger investment in the design, implementation, and fine-tuning, metaheuristics can often produce state-of-the-art results. We say that, according to the specific context of applications, either a ...

Deformable models are segmentation techniques that adapt a curve with the goal of maximizing its overlap with the actual contour of an object of interest within an image. Such a process requires that an optimization framework be defined whose most critical issues include: choosing an optimization method which exhibits robustness with respect to noisy and highly-multimodal search spaces; selecti...

Published as a chapter in Approximation Algorithms and Metaheuristics, a book edited by T. F. Gonzalez.

Abstract Production scheduling is one of the most critical activities in manufacturing. Under context Industry 4.0 paradigm, shop becomes even more complex. Metaheuristics present potential to solve these harder problems but demand substantial computational power. The use high-performance parallel architectures, cloud computing and edge computing, may support develop better metaheuristics, enab...

Correction to: S. Greco et al. (Eds.): Metaheuristics for Combinatorial Optimization, AISC 1332, https://doi.org/10.1007/978-3-030-68520-1 The book was inadvertently published with the incorrect volume number (1336) and this has been corrected now (1332).