Surrogate-Based Agents for Constrained Optimization

Multi-agent systems have been used to solve complex problems by decomposing them into autonomous subtasks. Drawing inspiration from both multi-surrogate and multi-agent techniques, we define in this article optimization subtasks that employ different approxi-mations of the data in subregions through the choice of surrogate, which creates surrogate-based agents. We explore a method of design space partitioning that assigns agents to subregions of the design space, which drives the agents to locate optima through a mixture of optimization and exploration in the subregions. These methods are illustrated on two constrained optimization problems, one with uncertainty and another with small, discon-nected feasible regions. It is observed that using a system of surrogate-based optimization agents is more effective at locating the optimum compared to optimization with a single surrogate over the entire design space. Nomenclature c = centroid f = objective function F = objective function values associated with design of experiments in database g = constraint G = constraint values associated with design of experiments in database t = time x = design variables X = design of experiments in database I

Sensing technologies for intelligent environments : a review

Sensors are fundamental components for making any environment intelligent. Depending on the applications, different sensors are required to implement specific objectives. This chapter will review different applications and consequently the requirements for different sensors and sensing technologies used in intelligent environment with a special emphasis on smart homes.31 page(s