Many real discrete optimization problems (DOPs) are NP -hard and contain a huge number of variables and/or constraints that make the models intractable for currently available solvers. Large DOPs can be solved due to their special structure using decomposition approaches. An important example of decomposition approaches is tree decomposition with local decomposition algorithms using the special...

Ant colony optimization (ACO) algorithms have proved to be able to adapt to dynamic optimization problems (DOPs) when they are enhanced to maintain diversity and transfer knowledge. Several approaches have been integrated with ACO to improve its performance for DOPs. Among these integrations, the ACO algorithm with immigrants schemes has shown good results on the dynamic travelling salesman pro...

Multimodal tumor imaging with targeted nanoparticles potentially offers both enhanced specificity and sensitivity, leading to more precise cancer diagnosis and monitoring. We describe the synthesis and characterization of phenol-substituted, lipophilic orange and far-red fluorescent dyes and a simple radioiodination procedure to generate a dual (optical and nuclear) imaging probe. MALDI-ToF ana...

The population-based incremental learning (PBIL) algorithm is a combination of evolutionary optimization and competitive learning. The integration of PBIL with associative memory schemes has been successfully applied to solve dynamic optimization problems (DOPs). The best sample together with its probability vector are stored and reused to generate the samples when an environmental change occur...

We have observed a bimodal distribution of ellipsoidal unilamellar vesicles (ULVs) in a phospholipid mixture composed of dioleoyl phosphatidylserine (DOPS) and dipalmitoyl and dihexanoyl phosphatidylcholine, DPPC and DHPC, respectively. Dynamic light scattering and transmission electron microscopy data indicate a bimodal size distribution of these nanoparticles with hydrodynamic radii of approx...

Dynamic environments are those which possess the potentiality of changes over time. These changes can appear on parameters and different environmental agents. In this regard, some problems, called Dynamic Optimization Problems (DOPs), are defined both theoretically and practically in the real world. The most important challenge in solving these kinds of problems is related to how to become comp...

Cellular network data traffic can be offload onto opportunistic networks. This paper proposes a Distance-based Opportunistic Publish/Subscribe (DOPS) content dissemination model, which is composed of three layers: application layer, decision-making layer and network layer. When a user wants new content, he/she subscribes on a subscribing server. Users having the contents decide whether to deliv...

Many real-world optimisation problems are of dynamic nature, requiring an optimisation algorithm which is able to continuously track a changing optimum over time. To achieve this, we propose two population-based algorithms for solving dynamic optimisation problems (DOPs) with continuous variables: the self-adaptive differential evolution algorithm (jDE) and the differential ant-stigmergy algori...

Dynamic optimization problems (DOPs) have attracted considerable attention due to the wide range of problems they can be applied to. Lots of efforts have been expended in modeling dynamic situations, proposing algorithms, and analyzing the results (too often in a visual way). Numeric performance measurements and their statistical validation have been however barely used in the literature. Most ...