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Emergence From Chaos To Order

The algorithm of Continuous Point Location with Adaptive d-ary Search (CPL-AdS) strategy exhibits its efficiency in solving stochastic point location (SPL) problems. However, there is one bottleneck for this CPL-AdS strategy which is that, when the dimension of the feature, or the number of divided subintervals for each iteration, d is large, the decision table for elimination process is almost unavailable. On the other hand, the larger dimension of the features d can generally make this CPL-AdS strategy avoid oscillation and converge faster. This paper presents a generalized universal decision formula to solve this bottleneck problem. As a matter of fact, this decision formula has a wider usage beyond handling out this SPL problems, such as dealing with deterministic point location problems and searching data in Single Instruction Stream-Multiple Data Stream based on Concurrent Read and Exclusive Write parallel computer model. Meanwhile, we generalized the CPL-AdS strategy with an extending formula, which is capable of tracking an unknown dynamic parameter λ* in both informative and deceptive environments. Furthermore, we employed different learning automata in the generalized CPL-AdS method to find out if faster learning algorithm will lead to better realization of the generalized CPL-AdS method. All of these aforementioned contributions are vitally important whether in theory or in practical applications. Finally, extensive experiments show that our proposed approaches are efficient and feasible.

A General CPL-AdS Methodology for Fixing Dynamic Parameters in Dual Environments

Coverage, one of the most important performance metrics for wireless sensor networks, reflects on how well a sensor field is monitored. Coverage problem is a devoted study of a node placement optimization problem in the coverage configuration before network deployment, where the objective is to find the optimal locations to place sensor nodes, such that the number of nodes (or the network cost) can be minimized and the coverage requirements can be satisfied. In this paper, we propose a harmony search (HS)-based deployment algorithm that can locate the optimal number of sensor nodes as well as their optimal locations for maximizing the network coverage and minimizing the network cost. The ability of HS is modified to automatically evolve the appropriate number of sensor nodes as well as their optimal locations. This can be accomplished by integrating the concept of adaptable length encoding in each solution vector to represent a variable number of candidate sensor nodes. Network coverage ratio, number of sensor nodes, and minimum distance between sensor nodes are the chief elements of a new objective function that has been offered to confirm the choice of the optimal number of sensor nodes and their positions. Experimental results show the ability of the proposed algorithm to find the appropriate number of sensor nodes and their locations. Furthermore, a comparative study with a metaheuristic Genetic-based algorithm and a random deployment technique has also been conducted and its results confirm the superiority of the proposed algorithm.

Maximizing Wireless Sensor Network Coverage With Minimum Cost Using Harmony Search Algorithm

Radiative B meson decays into Kπγ and Kππγ final states

Today, World Wide Web has brought us enormous quantity of on-line information. As a result, extracting relevant information from massive data has become a challenging issue. In recent past text summarization is recognized as one of the solution to extract useful information from vast amount documents. Based on number of documents considered for summarization, it is categorized as single document or multi document summarization. Rather than single document, multi document summarization is more challenging for the researchers to find accurate summary from multiple documents. Hence in this study, a novel Cat Swarm Optimization (CSO) based multi document summarizer is proposed to address the problem of multi document summarization. The proposed CSO based model is also compared with two other nature inspired based summarizer such as Harmony Search (HS) based summarizer and Particle Swarm Optimization (PSO) based summarizer. With respect to the benchmark Document Understanding Conference (DUC) datasets, the performance of all algorithms are compared in terms of different evaluation metrics such as ROUGE score, F score, sensitivity, positive predicate value, summary accuracy, inter sentence similarity and readability metric to validate non-redundancy, cohesiveness and readability of the summary respectively. The experimental analysis clearly reveals that the proposed approach outperforms the other summarizers included in the study.

Cat swarm optimization based evolutionary framework for multi document summarization

Learning automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms and are able to control the stochastic games. In this paper, the concepts of stigmergy and entropy are imported into learning automata based multi-agent systems with the purpose of providing a simple framework for interaction and coordination in multi-agent systems and speeding up the learning process. The multi-agent system considered in this paper is designed to find optimal policies in Markov games. We consider several dummy agents that walk around in the states of the environment, make local learning automaton active, and bring information so that the involved learning automaton can update their local state. The entropy of the probability vector for the learning automata of the next state is used to determine reward or penalty for the actions of learning automata. The experimental results have shown that in terms of the speed of reaching the optimal policy, the proposed algorithm has better learning performance than other learning algorithms.

Speeding up learning automata based multi agent systems using the concepts of stigmergy and entropy

Text summarization is the objective extraction of some parts of the text, such as sentence and paragraph, as the document abstract. If there are documents with a large amount of information, extractive text summarization would be arisen as an NP-complete problem. To solve these problems, metaheuristic algorithms are used. In this paper, a method based on multi-agent particle swarm optimization approach is proposed to improve the extractive text summarization. In this method, each particle will be upgraded with the status of multi-agent systems. The proposed method is tested on DUC 2002 standard documents and analyzed by ROUGE evaluation software. The experimental results show that this method has better performance than other compared methods.

Automatic text summarization based on multi-agent particle swarm optimization

Human activity recognition (HAR) has been of interest in recent years due to the growing demands in many areas. Applications of HAR include healthcare systems to monitor activities of daily living (ADL) (primarily due to the rapidly growing population of the elderly), security environments for automatic recognition of abnormal activities to notify the relevant authorities, and improve human interaction with the computer. HAR research can be classified according to the data acquisition tools (sensors or cameras), methods (handcrafted methods or deep learning methods), and the complexity of the activity. In the healthcare system, HAR based on wearable sensors is a new technology that consists of three essential parts worth examining: the location of the wearable sensor, data preprocessing (feature calculation, extraction, and selection), and the recognition methods. This survey aims to examine all aspects of HAR based on wearable sensors, thus analyzing the applications, challenges, datasets, approaches, and components. It also provides coherent categorizations, purposeful comparisons, and systematic architecture. Then, this paper performs qualitative evaluations by criteria considered in this system on the approaches and makes available comprehensive reviews of the HAR system. Therefore, this survey is more extensive and coherent than recent surveys in this field.

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Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System

Today, we are facing the growing development of distributed systems which their vast scale has challenged their centralized management. Therefore, many researchers design these systems as self-orga...

Emergence phenomena in self-organizing systems: a systematic literature review of concepts, researches, and future prospects

The goal of aggregating the base classifiers is to achieve an aggregated classifier that has a higher resolution than individual classifiers Random forest is one of the types of ensemble learning methods that have been considered more than other ensemble learning methods due to its simple structure, ease of understanding, as well as higher efficiency than similar methods The ability and efficiency of classical methods are always influenced by the data The capabilities of independence from the data domain, and the ability to adapt to problem space conditions, are the most challenging issues about the different types of classifiers In this paper, a method based on learning automata is presented, through which the adaptive capabilities of the problem space, as well as the independence of the data domain, are added to the random forest to increase its efficiency Using the idea of reinforcement learning in the random forest has made it possible to address issues with data that have a dynamic behaviour Dynamic behaviour refers to the variability in the behaviour of a data sample in different domains Therefore, to evaluate the proposed method, and to create an environment with dynamic behaviour, different domains of data have been considered In the proposed method, the idea is added to the random forest using learning automata The reason for this choice is the simple structure of the learning automata and the compatibility of the learning automata with the problem space The evaluation results confirm the improvement of random forest efficiency

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Behrooz Masoumi

Papers

Speeding up learning automata based multi agent systems using the concepts of stigmergy and entropy

Automatic text summarization based on multi-agent particle swarm optimization

Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System

Emergence phenomena in self-organizing systems: a systematic literature review of concepts, researches, and future prospects

A New Random Forest Algorithm Based on Learning Automata