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

Hamline law review

Service delivery optimization has an important impact on organizational profitability, where changes in allocation of resources (e.g. humans, equipment and materials) to services increases profit. Simulation and optimization techniques generally suffer from three main drawbacks; firstly, the limited knowledge and skill of researchers in modeling social complexities. Secondly, having assumed that a fairly realistic model of the problem is simulated, finding optimal solutions requires an exhaustive search that is almost impossible in problems with a large search space. Thirdly, mathematical optimization techniques often require the acquisition of knowledge in a central unit, which is problematic e.g. for privacy reasons. This article introduces a new technique, which combines Agent Based Modeling (ABM) and Distribution Constraint Optimization (DCOP) to overcome these difficulties. Our empirical results present a successful model for finding optimized resourced allocation settings in comparison with two different ABM simulated models on a sample of a real-life service delivery problem</p

Energetic and exergetic performance evaluation of an AC and a solar powered DC compressor

This study represents experimental performance analyses of an alternative current (AC) and a direct current (DC) refrigeration compressors implemented in a 79 liter refrigerator. Experiments were carried out at continuously running (ON) and periodically running (ON/OFF) operation modes. Data was analyzed and a comparison in terms of cooling capacity, power input, coefficient of performance (COP), Carnot COP, and exergy efficiency was conducted. The comparison showed that DC compressors can be much more efficient than AC compressors in refrigeration units. © Springer-Verlag Berlin Heidelberg 2013

Learning User Preferences in Distributed Calendar Scheduling

Within the field of software agents, there has been increasing interest in automating the process of calendar scheduling in recent years. Calendar (or meeting) scheduling is an example of a timetabling domain that is most naturally formulated and solved as a continuous, distributed problem. Fundamentally, it involves reconciliation of a given user&apos;s scheduling preferences with those of others that the user needs to meet with, and hence techniques for eliciting and reasoning about a user&apos;s preferences are crucial to finding good solutions. In this paper, we present work aimed at learning a user&apos;s time preference for scheduling a meeting. We adopt a passive machine learning approach that observes the user engaging in a series of meeting scheduling episodes with other meeting participants and infers the user&apos;s true preference model from accumulated data. After describing our basic modeling assumptions and approach to learning user preferences, we report the results obtained in an initial set of proof of principle experiments. In these experiments, we use a set of automated CMRADAR calendar scheduling agents to simulate meeting scheduling among a set of users, and use information generated during these interactions as training data for each user&apos;s learner

Site selection for offshore wind farms along the Indian coast

1401-1406This study deals with the location of the potential sites for offshore wind farms and also deals with the feasibility of installing offshore wind farms through scientific examination along the coast of India. Offshore wind energy is almost unexplored along the Indian coast. Potential and feasible regions need to be found and studied in detail. In this regard, few of the essential primary parameters such as bathymetry, wind velocity, proximity to the coast, ports, harbours, marine protected areas and marine sanctuaries were considered. Suitable sites for offshore wind farms were demarcated in a GIS environment. Weekly climatology (1999-2009) of wind speed was used to explore the seasonal wind potential. GIS analysis has brought out potential wind farms regions of 32,000 km2 in north east Arabian Sea (Off Mumbai and Off Ratnagiri) where bathymetry is in the range of 20 m to 75 m. Wind velocity ranges between 1.9 m/s to 10.2 m/s in these regions. The second potential site has been identified at off Mangalore with 6490 km2. The third prospective site is at Off Hooghly estuary.</span

SERmagazine

The public health system is plagued by inefficient use of resources. Frequently, the results are lengthy patient treatment waiting times. While many solutions for patient scheduling in health systems exist, few address the problem of coordination between independent autonomous departments. In this study, we describe the use of a distributed dynamic constraint optimisation algorithm (Support Based Distributed Optimisation) for the generation and optimisation of schedules across autonomous units. We model the problem of scheduling radiotherapy patients across several independent oncology units as a dynamic distributed constraint optimisation problem. Such an approach minimises the sharing of private information such as department operation details as well as patient privacy information while taking into consideration patient preferences as well as resource utilisation to find a pareto-optimal solution

Efficient Distributed Linear Programming with Limited Disclosure

Part 7: Privacy-Preserving Data Applications IIInternational audienceIn today’s networked world, resource providers and consumers are distributed globally and locally. However, with resource constraints, optimization is necessary to ensure the best possible usage of such scarce resources. Distributed linear programming (DisLP) problems allow collaborative agents to jointly maximize profits (or minimize costs) with a linear objective function while conforming to several shared as well as local linear constraints. Since each agent’s share of the global constraints and the local constraints generally refer to its private limitations or capacities, serious privacy problems may arise if such information is revealed. While there have been some solutions proposed that allow secure computation of such problems, they typically rely on inefficient protocols with enormous communication cost. In this paper, we present a secure and extremely efficient protocol to solve DisLP problems where constraints are arbitrarily partitioned and no variable is shared between agents. In the entire protocol, each agent learns only a partial solution (about its variables), but learns nothing about the private input/output of other agents, assuming semi-honest behavior. We present a rigorous security proof and communication cost analysis for our protocol and experimentally validate the costs, demonstrating its robustness