Chaotic Quantum Double Delta Swarm Algorithm using Chebyshev Maps: Theoretical Foundations, Performance Analyses and Convergence Issues

Quantum Double Delta Swarm (QDDS) Algorithm is a new metaheuristic algorithm inspired by the convergence mechanism to the center of potential generated within a single well of a spatially co-located double-delta well setup. It mimics the wave nature of candidate positions in solution spaces and draws upon quantum mechanical interpretations much like other quantum-inspired computational intelligence paradigms. In this work, we introduce a Chebyshev map driven chaotic perturbation in the optimization phase of the algorithm to diversify weights placed on contemporary and historical, socially-optimal agents' solutions. We follow this up with a characterization of solution quality on a suite of 23 single-objective functions and carry out a comparative analysis with eight other related nature-inspired approaches. By comparing solution quality and successful runs over dynamic solution ranges, insights about the nature of convergence are obtained. A two-tailed t-test establishes the statistical significance of the solution data whereas Cohen's d and Hedge's g values provide a measure of effect sizes. We trace the trajectory of the fittest pseudo-agent over all function evaluations to comment on the dynamics of the system and prove that the proposed algorithm is theoretically globally convergent under the assumptions adopted for proofs of other closely-related random search algorithms.Comment: 27 pages, 4 figures, 19 table

Apparatus for multiprocessor-based control of a multiagent robot

An architecture for robot intelligence enables a robot to learn new behaviors and create new behavior sequences autonomously and interact with a dynamically changing environment. Sensory information is mapped onto a Sensory Ego-Sphere (SES) that rapidly identifies important changes in the environment and functions much like short term memory. Behaviors are stored in a DBAM that creates an active map from the robot's current state to a goal state and functions much like long term memory. A dream state converts recent activities stored in the SES and creates or modifies behaviors in the DBAM

System and method for image mapping and visual attention

A method is described for mapping dense sensory data to a Sensory Ego Sphere (SES). Methods are also described for finding and ranking areas of interest in the images that form a complete visual scene on an SES. Further, attentional processing of image data is best done by performing attentional processing on individual full-size images from the image sequence, mapping each attentional location to the nearest node, and then summing attentional locations at each node

Architecture for Multiple Interacting Robot Intelligences

An architecture for robot intelligence enables a robot to learn new behaviors and create new behavior sequences autonomously and interact with a dynamically changing environment. Sensory information is mapped onto a Sensory Ego-Sphere (SES) that rapidly identifies important changes in the environment and functions much like short term memory. Behaviors are stored in a database associative memory (DBAM) that creates an active map from the robot's current state to a goal state and functions much like long term memory. A dream state converts recent activities stored in the SES and creates or modifies behaviors in the DBAM

Patients and Caregivers Report Using Medical Marijuana to Decrease Prescription Narcotics Use

In depth qualitative interview data were collected from medical marijuana patients and knowledgeable producers in Michigan about their perceptions and observations on the medical use of marijuana. Patients consistently reported using marijuana to substitute or wean off prescription drugs. All patients and producers who were taking opiate pain killers claimed they reduced overall drug use, especially opiates, by using medical marijuana. Patients and caregivers also claimed medical marijuana was preferred over opiates, eased withdrawal from opiates, and in some cases was perceived as more effective at relieving pain

Sample Results From The Interim Salt Disposition Program Macrobatch 7 Tank 21H Qualification Samples

Savannah River National Laboratory (SRNL) analyzed samples from Tank 21H in support of qualification of Macrobatch (Salt Batch) 7 for the Interim Salt Disposition Program (ISDP). An ARP and several ESS tests were also performed. This document reports characterization data on the samples of Tank 21H as well as simulated performance of ARP/MCU. No issues with the projected Salt Batch 7 strategy are identified, other than the presence of visible quantities of dark colored solids. A demonstration of the monosodium titanate (0.2 g/L) removal of strontium and actinides provided acceptable 4 hour average decontamination factors for Pu and Sr of 3.22 and 18.4, respectively. The Four ESS tests also showed acceptable behavior with distribution ratios (D(Cs)) values of 15.96, 57.1, 58.6, and 65.6 for the MCU, cold blend, hot blend, and Next Generation Solvent (NGS), respectively. The predicted value for the MCU solvent was 13.2. Currently, there are no models that would allow a prediction of extraction behavior for the other three solvents. SRNL recommends that a model for predicting extraction behavior for cesium removal for the blended solvent and NGS be developed. While no outstanding issues were noted, the presence of solids in the samples should be investigated in future work. It is possible that the solids may represent a potential reservoir of material (such as potassium) that could have an impact on MCU performance if they were to dissolve back into the feed solution. This salt batch is intended to be the first batch to be processed through MCU entirely using the new NGS-MCU solvent