Editorial: special issue on parallel nature-inspired optimization

Abstract

Nature-inspired optimization techniques have proven to efficiently solve a wide range of problems related to parallel computing and more generally to computer science. In addition, the intrinsic parallelization and distribution capabilities of nature-inspired techniques can been exploited in order to provide powerful optimization solutions. However many research challenges remain to be addressed, such as the design and implementation of efficient nature-inspired optimization algorithms for massively parallel and distributed architectures and their application to real-world problems. This special issue is composed of extended versions of selected best papers presented at the 14th International Workshop on Nature Inspired Distributed Computing (NIDISC 2011). These articles provide a good theoretical and practical overview of nature-inspired optimization techniques and their application to metaheuristics and parallel/distributed computing. In the following, we propose a brief overview of the different contributions included in this special issue. In the first paper by E.A. Macedo et al., “Multiple Biological Sequence Alignment in Heterogeneous Multicore Clusters with User-Selectable Task Allocation Policies” a parallel version of a heuristic iterative algorithm, DIALIGN-TX, is proposed to tackle the Multiple Sequence Alignment (MSA) problem, a well-known NP-Hard bioinformatics problem. The authors empirically demonstrate the execution time reduction obtained with the proposed parallel implementation as well as the impact of the choice of the allocation policy. The second paper by P. Korosec et al., “Multi-core Implementation of the Differential Ant-Stigmergy Algorithm,” also proposes to parallelize a state-of-the-art optimization algorithm, the Differential Ant-Stigmergy algorithm (DASA) which is a