Agent-Based Natural Domain Modeling for Cooperative Continuous Optimization

International audienceWhile multi-agent systems have been successfully applied to combinatorial optimization, very few works concern their applicability to continuous optimization problems. In this article we propose a framework for modeling a continuous optimization problems as multi-agent system,which we call NDMO, by representing the problem as an agent graph, and complemented with optimization solving behaviors. Some of the results we obtained with our implementation on several continuous optimization problems are presented

Flexible and Emergent Workflows using Adaptive Agents

International audienceMost of existing workflow systems are rigid since they require to completely specify processes before their enactment and they also lack flexibility during their execution. This work proposes to view a workflow as a set of cooperative and adaptive agents interleaving its design and its execution leading to an emergent workflow. We use the theory of Adaptive Multi-Agent Systems (AMAS) to provide agents with adaptive capabilities and the whole multi-agent system with emergent "feature". We provide a meta-model linking workflow and AMAS concepts, and the specification of agent behavior and the resulting collaborations. A simulator has been implemented with the Make Agent Yourself platform

A Review on GPU Based Parallel Computing for NP Problems

Now a days there are different number of optimization problems are present. Which are NP problems to solve this problems parallel metaheuristic algorithm are required. Graph theories are most commonly studied combinational problems. In this paper providing the new move towards solve this combinational problem with GPU based parallel computing using CUDA architecture. Comparing those problem with relevant to the transfer rate, effective memory utilization and speedup etc. to acquire the paramount possible solution. By applying the different algorithms on the optimization problem to catch the efficient memory exploitation, synchronized execution, saving time and increasing speedup of execution. Due to this the speedup factor is enhance and get the best optimal solution

Extracting collective trends from Twitter using social-based data mining

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40495-5_62Proceedings 5th International Conference, ICCCI 2013, Craiova, Romania, September 11-13, 2013,Social Networks have become an important environment for Collective Trends extraction. The interactions amongst users provide information of their preferences and relationships. This information can be used to measure the influence of ideas, or opinions, and how they are spread within the Network. Currently, one of the most relevant and popular Social Network is Twitter. This Social Network was created to share comments and opinions. The information provided by users is specially useful in different fields and research areas such as marketing. This data is presented as short text strings containing different ideas expressed by real people. With this representation, different Data Mining and Text Mining techniques (such as classification and clustering) might be used for knowledge extraction trying to distinguish the meaning of the opinions. This work is focused on the analysis about how these techniques can interpret these opinions within the Social Network using information related to IKEA® company.The preparation of this manuscript has been supported by the Spanish Ministry of Science and Innovation under the following projects: TIN2010-19872, ECO2011-30105 (National Plan for Research, Development and Innovation) and the Multidisciplinary Project of Universidad Aut´onoma de Madrid (CEMU-2012-034

Investigation into Mobile Learning Framework in Cloud Computing Platform

Abstract—Cloud computing infrastructure is increasingly used for distributed applications. Mobile learning applications deployed in the cloud are a new research direction. The applications require specific development approaches for effective and reliable communication. This paper proposes an interdisciplinary approach for design and development of mobile applications in the cloud. The approach includes front service toolkit and backend service toolkit. The front service toolkit packages data and sends it to a backend deployed in a cloud computing platform. The backend service toolkit manages rules and workflow, and then transmits required results to the front service toolkit. To further show feasibility of the approach, the paper introduces a case study and shows its performance

Combining social-based data mining techniques to extract collective trends from twitter

Social Networks have become an important environment for Collective Trends extraction. The interactions amongst users provide information of their preferences and relationships. This information can be used to measure the influence of ideas, or opinions, and how they are spread within the Network. Currently, one of the most relevant and popular Social Networks is Twitter. This Social Network was created to share comments and opinions. The information provided by users is especially useful in different fields and research areas such as marketing. This data is presented as short text strings containing different ideas expressed by real people. With this representation, different Data Mining techniques (such as classification or clustering) will be used for knowledge extraction to distinguish the meaning of the opinions. Complex Network techniques are also helpful to discover influential actors and study the information propagation inside the Social Network. This work is focused on how clustering and classification techniques can be combined to extract collective knowledge from Twitter. In an initial phase, clustering techniques are applied to extract the main topics from the user opinions. Later, the collective knowledge extracted is used to relabel the dataset according to the clusters obtained to improve the classification results. Finally, these results are compared against a dataset which has been manually labelled by human experts to analyse the accuracy of the proposed method.The preparation of this manuscript has been supported by the Spanish Ministry of Science and Innovation under the following projects: TIN2010-19872 and ECO2011-30105 (National Plan for Research, Development and Innovation), as well as the Multidisciplinary Project of Universidad Autónoma de Madrid (CEMU2012-034). The authors thank Ana M. Díaz-Martín and Mercedes Rozano for the manual classification of the Tweets

Recent progress on formal and computational model for A. Smiths Invisible Hand paradigm

The recent economic crisis has boosted a very strong demand for quite new tools to analyze and predict the behavior of quasi-free1 markets. The paper presents our effort to build a formal theory of A. Smith's Invisible Hand [5] paradigm (ASIH) and simulation model for a selected case. It proves that ASIH is not only an economic idea, which conflict on ways to govern [16], but something that really exists, for which formal a theory can be built. Moreover, ASIH can be measured [17], and in the future probably utilized for quasi-free market analysis and prediction. In advance, we want to state, that ASIH according to our theory, can generate both correct and incorrect decisions. For this, we use the theory of computational Collective Intelligence [18] and a molecular model of computations [1], [2]. Our theory assumes that ASIH is an unconscious meta-inference process spread on the platform of brains of agents. This meta-process is: distributed, parallel, and non-deterministic, and is run on a computational platform of market agents' brains. The ASIH inference process emerges spontaneously in certain circumstances and can vanish when market situation changes. Since the ASIH platform is made up of brains of agents, conclusions of this inference process affect the behavior of agents and therefore the behavior of the entire market. Our research unveils that ASIH is in fact a family of similar meta-processes; thus ASIHs for different economic eras are different because corresponding models of brains of market agents are different. The paper will present and explain, on the basis of a simulation model, a case of powerful ASIH response at the end of the 15th century due to a blockade (taxes and the Dardanelles sea-route cutoff) of spice trade by Turks and Arabs. ASIH also responded to the discovery of America, the emergence of a sailing route around Africa, the establishment of plantations (sugarcane, spices) and modern galleons2 technology. This case demonstrates how powerful and with far-reaching consequences, ASIH can b

Adjustability of a discrete particle swarm optimization for the dynamic TSP

This paper presents a detailed study of the discrete particle swarm optimization algorithm (DPSO) applied to solve the dynamic traveling salesman problem which has many practical applications in planning, logistics and chip manufacturing. The dynamic version is especially important in practical applications in which new circumstances, e.g., a traffic jam or a machine failure, could force changes to the problem specification. The DPSO algorithm was enriched with a pheromone memory which is used to guide the search process similarly to the ant colony optimization algorithm. The paper extends our previous work on the DPSO algorithm in various ways. Firstly, the performance of the algorithm is thoroughly tested on a set of newly generated DTSP instances which differ in the number and the size of the changes. Secondly, the impact of the pheromone memory on the convergence of the DPSO is investigated and compared with the version without a pheromone memory. Moreover, the results are compared with two ant colony optimization algorithms, namely the (Formula presented.)–(Formula presented.) ant system (MMAS) and the population-based ant colony optimization (PACO). The results show that the DPSO is able to find high-quality solutions to the DTSP and its performance is competitive with the performance of the MMAS and the PACO algorithms. Moreover, the pheromone memory has a positive impact on the convergence of the algorithm, especially in the face of dynamic changes to the problem’s definition