The Distributed Constraint Optimization Problem (DCOP) formulation is a powerful tool to model multi-agent coordination problems that are distributed by nature. While DCOPs assume variables discrete and the environment does not change over time, agents often interact in more dynamic complex environment. To address these limiting assumptions, researchers have proposed Dynamic (D-DCOPs) how dynam...

The DIRECT algorithm is a deterministic sampling method for bound constrained Lipschitz continuous optimization. We prove a subsequential convergence result for the DIRECT algorithm that quantifies some of the convergence observations in the literature. Our results apply to several variations on the original method, including one that will handle general constraints. We use techniques from nons...

A Collaboration Engineering Methodology (CEM) comprises a set of defined, standardized, docu-mented, and discoverable objectives, deliverables, key actions, tools/templates, principles and policies for establishing effective, efficient, satisfying col-laborative work practices for highvalue organi-zational tasks. First-generation CEMs address design and development CE solutions. Existing CEMs, ...

The paper studies the connections and differences between bilevel problems (BL) and generalized semi-infinite problems (GSIP). Under natural assumptions (GSIP) can be seen as a special case of a (BL). We consider the so-called reduction approach for (BL) and (GSIP) leading to optimality conditions and Newton-type methods for solving the problems. We show by a structural analysis that for (GSIP)...

This paper presents an artificial beehive algorithm for optimization in continuous search spaces based on a model aimed at individual bee behavior. The algorithm defines a set of behavioral rules for each agent to determine what kind of actions must be carried out. Also, the algorithm proposed includes some adaptations not considered in the biological model to increase the performance in the se...

To fabricate a virtual shape into the real world, the physical strength of the shape is an important consideration. We introduce a framework to consider both the strength and complexity of 3D frame structures. The key to the framework is a stress-oriented analysis and a semi-continuous condition in the shape representation that can both strengthen and simplify a structure at the same time. We f...

The work presents a new evolutionary algorithm designed for continuous optimization. The algorithm is based on evolution of probability density functions, which focus on the most promising zones of the domain of each variable. Several mechanisms are included to self-adapt the algorithm to the feature of the problem. By means of an experimental study, we have observed that our algorithm obtains ...

A detailed comparison is presented of five well-known crossover operators on a range of continuous optimization tasks to test longheld beliefs concerning disruption, with respect to real-coded GAs. It is suggested that, contrary to traditional assumptions, disruption is useful.

This editorial note presents the motivations, objectives, and structure of the special issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. In addition, it provides the link to an associated Website where complementary material to the special issue is available.