Improving performance via population growth and local search: The case of the artificial bee colony algorithm

We modify an artificial bee colony algorithm as follows: we make the population size grow over time and apply local search on strategically selected solutions. The modified algorithm obtains very good results on a set of large-scale continuous optimization benchmark problems. This is not the first time we see that the two aforementioned modifications make an initially non-competitive algorithm obtain state-of-the-art results. In previous work, we have shown that the same modifications substantially improve the performance of particle swarm optimization and ant colony optimization algorithms. Altogether, these results suggest that population growth coupled with local search help obtain high-quality results. © 2012 Springer-Verlag.SCOPUS: cp.kinfo:eu-repo/semantics/publishe

The cyclic di-GMP phosphodiesterase gene Rv1357c/BCG1419c affects BCG Pellicle production and in Vivo maintenance

Bacteria living in a surface-attached community that contains a heterogeneous population, coated with an extracellular matrix, and showing drug tolerance (biofilms) are often linked to chronic infections. In mycobacteria, the pellicle mode of growth has been equated to an in vitro biofilm and meets several of the criteria mentioned above, while tuberculosis infection presents a chronic (latent) phase of infection. As mycobacteria lack most genes required to control biofilm production by other microorganisms, we deleted or expressed from the hsp60 strong promoter the only known c-di-GMP phosphodiesterase (PDE) gene in Mycobacterium bovis BCG. We found changes in pellicle production, cellular protein profiles, lipid production, resistance to nitrosative stress and maintenance in lungs and spleens of immunocompetent BALB/mice. Our results show that pellicle production and capacity to remain within the host are linked in BCG. 2015 � 2015 International Union of Biochemistry and Molecular Biology

Analysing Robot Swarm Decision-Making with Bio-PEPA

We present a novel method to analyse swarm robotics systems based on Bio-PEPA. Bio-PEPA is a process algebraic language originally developed to analyse biochemical systems. Its main advantage is that it allows different kinds of analyses of a swarm robotics system starting from a single description. In general, to carry out different kinds of analysis, it is necessary to develop multiple models, raising issues of mutual consistency. With Bio-PEPA, instead, it is possible to perform stochastic simulation, fluid flow analysis and statistical model checking based on the same system specification. This reduces the complexity of the analysis and ensures consistency between analysis results. Bio-PEPA is well suited for swarm robotics systems, because it lends itself well to modelling distributed scalable systems and their space-time characteristics. We demonstrate the validity of Bio-PEPA by modelling collective decision-making in a swarm robotics system and we evaluate the result of different analyses. © 2012 Springer-Verlag.SCOPUS: cp.kinfo:eu-repo/semantics/publishe

Incremental particle swarm-guided local search for continuous optimization

We present an algorithm that is inspired by theoretical and empirical results in social learning and swarm intelligence research. The algorithm is based on a framework that we call incremental social learning. In practical terms, the algorithm is a hybrid between a local search procedure and a particle swarm optimization algorithm with growing population size. The local search procedure provides rapid convergence to good solutions while the particle swarm algorithm enables a comprehensive exploration of the search space. We provide experimental evidence that shows that the algorithm can find good solutions very rapidly without compromising its global search capabilities. © 2008 Springer Berlin Heidelberg.SCOPUS: cp.k5th International Workshop on Hybrid Metaheuristics, HM 2008; Malaga; Spain; 8 October 2008 through 9 October 2008.info:eu-repo/semantics/publishe