A significant challenge in nature-inspired algorithmics is the identification of specific characteristics of problems that make them harder (or easier) to solve using specific methods. The hope is that, by identifying these characteristics, we may more easily predict which algorithms are best-suited to problems sharing certain features. Here, we approach this problem using fitness landscape ana...

In the past few decades, many nature-inspired optimization algorithms have been developed successfully for solving a wide range of optimization problems. (EDA) are just some representative examples among many others. These meta-heuristic algorithms do not rely on gradient information, and are less likely to be stuck on local optima because of their use of a population of candidate solutions, th...

Automatic satellite image registration for multi-sensor images is becoming increasingly important to aid in flood damage assessment. We consider two images, the one before flood (optical image) and the other during flood (SAR image) in the registration process. The objective is to maximize the similarity metric (of these two images) using information theoretic measures such as Mutual Informatio...

This paper introduces collaborating robots which provide the possibility of enhanced task performance, high reliability and decreased. Collaborating-bots are a collection of mobile robots able to self-assemble and to self-organize in order to solve problems that cannot be solved by a single robot. These robots combine the power of swarm intelligence with the flexibility of self-reconfiguration ...

Load balancing and Consolidation of Virtual Machines is a way which is effective to improve the utilization of resources and energy efficiency in Cloud data centers. Determining when it is best to reallocate Virtual Machines from an overloaded host is an aspect of dynamic Virtual Machine consolidation that directly influences the utilization of resource and Quality of Service which the system i...