The field of parallel metaheuristics is continuously evolving as a result of new technologies
and needs that researchers have been encountering. In the last decade, new models of …

Solving optimization problems with parallel algorithms has a long tradition in OR. Its future
relevance for solving hard optimization problems in many fields, including finance, logistics …

Medical imaging currently plays a crucial role throughout the entire clinical applications from
medical scientific research to diagnostics and treatment planning. However, medical …

The parallel computing power offered by graphic processing units (GPUs) has been recently
exploited to support general purpose applications–by exploiting the availability of general …

Satisfiability (SAT) problem in engineering and computer science is to find the set of
assignment values of input variables for the given Boolean function that evaluate this …

Abstract Multi-Objective Evolutionary Algorithms (MOEAs) have been widely used to
address regression test optimization problems, including test case selection and test suite …

Graphic processing units (GPUs) emerged recently as an exciting new hardware
environment for a truly parallel implementation and execution of Nature and Bio-inspired …

In many cases there is still a large gap between the performance of current optimization
technology and the requirements of real-world applications. As in the past, performance will …

As multi-core computing is now standard, it seems irresponsible for constraints researchers
to ignore the implications of it. Researchers need to address a number of issues to exploit …

Abstract The Set-Union Knapsack Problem (SUKP) is a complex combinatorial optimisation
problem with applications in resource allocation, portfolio selection, and logistics. This paper …