Asynchronous Teams for probe selection problems

AbstractThe selection of probe sets for hybridization experiments directly affects the efficiency and cost of the analysis. We propose the application of the Asynchronous Team (A-Team) technique to determine near-optimal probe sets. An A-Team is comprised of several different heuristic algorithms that communicate with each other via shared memories. The A-Team method has been applied successfully to several problems including the Set Covering Problem, the Traveling Salesman Problem, and the Point-to-Point Connection Problem, and lends itself well to the Probe Selection Problem. We designed and developed a C + + program to run instances of the Minimum Cost Probe Set and Maximum Distinguishing Probe Set problems. A program description and our results are presented in the paper

Asynchronous Teams and Tasks in a Message Passing Environment

As the discipline of scientific computing grows, so too does the "skills gap" between the increasingly complex scientific applications and the efficient algorithms required. Increasing demand for computational power on the march towards exascale requires innovative approaches. Closing the skills gap avoids the many pitfalls that lead to poor utilisation of resources and wasted investment. This thesis tackles two challenges: asynchronous algorithms for parallel computing and fault tolerance. First I present a novel asynchronous task invocation methodology for Discontinuous Galerkin codes called enclave tasking. The approach modifies the parallel ordering of tasks that allows for efficient scaling on dynamic meshes up to 756 cores. It ensures high levels of concurrency and intermixes tasks of different computational properties. Critical tasks along domain boundaries are prioritised for an overlap of computation and communication. The second contribution is the teaMPI library, forming teams of MPI processes exchanging consistency data through an asynchronous "heartbeat". In contrast to previous approaches, teaMPI operates fully asynchronously with reduced overhead. It is also capable of detecting individually slow or failing ranks and inconsistent data among replicas. Finally I provide an outlook into how asynchronous teams using enclave tasking can be combined into an advanced team-based diffusive load balancing scheme. Both concepts are integrated into and contribute towards the ExaHyPE project, a next generation code that solves hyperbolic equation systems on dynamically adaptive cartesian grids

Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies

This paper proposes the use of multiagent cooperation for solving global optimization problems through the introduction of a new multiagent environment, MANGO. The strength of the environment lays in itsflexible structure based on communicating software agents that attempt to solve a problem cooperatively. This structure allows the execution of a wide range of global optimization algorithms described as a set of interacting operations. At one extreme, MANGO welcomes an individual non-cooperating agent, which is basically the traditional way of solving a global optimization problem. At the other extreme, autonomous agents existing in the environment cooperate as they see fit during run time. We explain the development and communication tools provided in the environment as well as examples of agent realizations and cooperation scenarios. We also show how the multiagent structure is more effective than having a single nonlinear optimization algorithm with randomly selected initial points

Stability analysis tool for tuning unconstrained decentralized model predicitive controllers

Some processes are naturally suitable to be controlled in a decentralized framework: centralized control solutions are often infeasible in dealing with large scale plants and they are technologically prohibitive when the processes are too fast for the available computational resources. In these cases, the resulting control problem is usually split in many smaller subproblems and the global requirements are guaranteed by means of a proper coordination. The unconstrained decentralized case is here considered and a coordination strategy is proposed for improving the global control performances. This paper present a tool for setting up and tuning a nominally stable decentralized Model Predictive Controller. Numerical examples are proposed for testing and validating the developed technique

Agent Teams and Evolutionary Computation: Optimizing Semi- Parametric Spatial Autoregressive Models

Classical spatial autoregressive models share the same weakness as the classical linear regression models, namely it is not possible to estimate non-linear relationships between the dependent and independent variables. In the case of classical linear regression a semi-parametric approach can be used to address this issue. Therefore an advanced semi- parametric modelling approach for spatial autoregressive models is introduced. Advanced semi-parametric modelling requires determining the best configuration of independent variable vectors, number of spline-knots and their positions. To solve this combinatorial optimization problem an asynchronous multi-agent system based on genetic-algorithms is utilized. Three teams of agents work each on a subset of the problem and cooperate through sharing their most optimal solutions. Through this system more complex relationships between the dependent and independent variables can be derived. These could be better suited for the possibly non-linear real-world problems faced by applied spatial econometricians.

Multi-agent systems for power engineering applications - part 2 : Technologies, standards and tools for building multi-agent systems

This is the second part of a 2-part paper that has arisen from the work of the IEEE Power Engineering Society's Multi-Agent Systems (MAS) Working Group. Part 1 of the paper examined the potential value of MAS technology to the power industry, described fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications, and presented a comprehensive review of the power engineering applications for which MAS are being investigated. It also defined the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part 2 of the paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented. Given the significant and growing interest in this field, it is imperative that the power engineering community considers the standards, tools, supporting technologies and design methodologies available to those wishing to implement a MAS solution for a power engineering problem. The paper describes the various options available and makes recommendations on best practice. It also describes the problem of interoperability between different multi-agent systems and proposes how this may be tackled

A survey of parallel hybrid applications to the permutation flow shop scheduling problem and similar problems

Parallel algorithms have focused an increased interest due to advantages in computation time and quality of solutions when applied to industrial engineering problems. This communication is a survey and classification of works in the field of hybrid algorithms implemented in parallel and applied to combinatorial optimization problems similar to the to the permutation flowshop problem with the objective of minimizing the makespan, Fm|prmu|Cmax according to the Graham notation, the travelling salesman problem (TSP), the quadratic assignment problem (QAP) and, in general, those whose solution can be expressed as a permutation

Enhancing Creativity as Innovation via Asynchronous Crowdwork

Synchronous, face-to-face interactions such as brainstorming are considered essential for creative tasks (the old normal). However, face-to-face interactions are difficult to arrange because of the diverse locations and conflicting availability of people - a challenge made more prominent by work-from-home practices during the COVID-19 pandemic (the new normal). In addition, face-to-face interactions are susceptible to cognitive interference. We employ crowdsourcing as an avenue to investigate creativity in asynchronous, online interactions. We choose product ideation, a natural task for the crowd since it requires human insight and creativity into what product features would be novel and useful. We compare the performance of solo crowd workers with asynchronous teams of crowd workers formed without prior coordination. Our findings suggest that, first, crowd teamwork yields fewer but more creative ideas than solo crowdwork. The enhanced team creativity results when Second, cognitive interference, known to inhibit creativity in face-to-face teams, may not be significant in crowd teams. Third, teamwork promotes better achievement emotions for crowd workers. These findings provide a basis for trading off creativity, quantity, and worker happiness in setting up crowdsourcing workflows for product ideation. </p

Whose (Partially Distributed) Team Are You On? Interviews About Us vs. Them in Corporate Settings

This study explores, through semi-structured interviews, team member perceptions of effectiveness in Partially Distributed Team work. A partially distributed team (PDT) is a virtual team, in which some members are collocated, yet others are geographically dispersed, and communication between them is primarily by electronic media. Researchers have identified an overarching problem in partially distributed teams, referred to as “Us-vs.-Them”, where members identify and collaborate better with collocated members than they do with distant members. We asked fourteen industry professionals to reflect on recent PDT experiences, in an attempt to find out what exacerbated and mitigated this problem. We confirmed prior findings such as importance of relationship building between members and salience of organizational influences, especially organizational culture and power distribution. We discuss additional findings based on team members’ insight, to help transform the “Us-vs.-Them” in-group/ out-group dynamics into an “Us” team cohesion