scholarly journals Framework proposal for adaptive mobile intelligent agents

2021 ◽  
Vol 10 (5) ◽  
pp. 2759-2770
Author(s):  
Elena Fabiola Ruiz-Ledesma ◽  
Rosaura Palma-Orozco ◽  
Elizabeth Acosta-Gonzaga
Keyword(s):  
Intelligent Agents ◽  
Optimization Problems ◽  
Single Agent ◽  
Search Problem ◽  
Sensors And Actuators ◽  
Autonomous Behavior ◽  
Shortest Route ◽  
Learning Techniques ◽  
Speed Up ◽  
Automated Processes

Intelligent agents are computational entities which have elements that provide them with the ability to perceive and manipulate their environment: sensors and actuators. These are characterized by displaying various properties that adapt and achieve their objectives. Autonomy, learning, collaboration and reasoning are examples of these properties which together make them intelligent artificial entities. This article shows the development of a framework that has made it possible to speed-up the construction of a system of adaptive mobile intelligent agents, called SySAge. The system agents have integrated search and learning techniques for the execution of automated processes focused on solving search, classification and optimization problems. It has been found that through learning, the agents were able to estimate input parameters and apply them in estimating the shortest route in a graph, considering cost and penalty aspects. To determine the choice of search technique, a probabilistic selection was used. The autonomous behavior of each agent was appreciated through the various attempts to solve the search problem and not to focus the information acquired individually on a single agent.

The Map-Based Shortest Route Selection by Using Ant Colony Optimization Algorithm

2018 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Achmad Fanany Onnilita Gaffar ◽  
Agusma Wajiansyah ◽  
Supriadi Supriadi
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Google Earth ◽  
Ant Colony ◽  
Food Sources ◽  
Shortest Route ◽  
Study Results ◽  
Destination Point

The shortest path problem is one of the optimization problems where the optimization value is a distance. In general, solving the problem of the shortest route search can be done using two methods, namely conventional methods and heuristic methods. The Ant Colony Optimization (ACO) is the one of the optimization algorithm based on heuristic method. ACO is adopted from the behavior of ant colonies which naturally able to find the shortest route on the way from the nest to the food sources. In this study, ACO is used to determine the shortest route from Bumi Senyiur Hotel (origin point) to East Kalimantan Governor's Office (destination point). The selection of the origin and destination points is based on a large number of possible major roads connecting the two points. The data source used is the base map of Samarinda City which is cropped on certain coordinates by using Google Earth app which covers the origin and destination points selected. The data pre-processing is performed on the base map image of the acquisition results to obtain its numerical data. ACO is implemented on the data to obtain the shortest path from the origin and destination point that has been determined. From the study results obtained that the number of ants that have been used has an effect on the increase of possible solutions to optimal. The number of tours effect on the number of pheromones that are left on each edge passed ant. With the global pheromone update on each tour then there is a possibility that the path that has passed the ant will run out of pheromone at the end of the tour. This causes the possibility of inconsistent results when using the number of ants smaller than the number of tours.

Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems

2021 ◽  
Author(s):  
Mohammad Shehab ◽  
Laith Abualigah
Keyword(s):  
Real World ◽  
Optimization Problems ◽  
Second Stage ◽  
Opposition Based Learning ◽  
High Fitness ◽  
Fitness Value ◽  
Speed Up ◽  
Disruption Operator ◽  
The Given ◽  
Real World Problems

Abstract Multi-Verse Optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack of diversity which may trapping of local minima, and premature convergence. This paper introduces two steps of improving the basic MVO algorithm. The first step using Opposition-based learning (OBL) in MVO, called OMVO. The OBL aids to speed up the searching and improving the learning technique for selecting a better generation of candidate solutions of basic MVO. The second stage, called OMVOD, combines the disturbance operator (DO) and OMVO to improve the consistency of the chosen solution by providing a chance to solve the given problem with a high fitness value and increase diversity. To test the performance of the proposed models, fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems, and seven CEC 2011 real-world problems were used in both phases of the enhancement. The second step, known as OMVOD, incorporates the disruption operator (DO) and OMVO to improve the accuracy of the chosen solution by giving a chance to solve the given problem with a high fitness value while also increasing variety. Fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the upgrade to assess the accuracy of the proposed models.

scholarly journals An integrated platform for intuitive mathematical programming modeling using LaTeX

2018 ◽  
Vol 4 ◽  
pp. e161 ◽  
Author(s):  
Charalampos P. Triantafyllidis ◽  
Lazaros G. Papageorgiou
Keyword(s):  
Mathematical Programming ◽  
Error Detection ◽  
Development Process ◽  
Optimization Problems ◽  
Scientific Work ◽  
Input Language ◽  
Speed Up ◽  
Cross Platform ◽  
Algebraic Modeling Language ◽  
Friendly Graphical User Interface

This paper presents a novel prototype platform that uses the same LaTeX mark-up language, commonly used to typeset mathematical content, as an input language for modeling optimization problems of various classes. The platform converts the LaTeX model into a formal Algebraic Modeling Language (AML) representation based on Pyomo through a parsing engine written in Python and solves by either via NEOS server or locally installed solvers, using a friendly Graphical User Interface (GUI). The distinct advantages of our approach can be summarized in (i) simplification and speed-up of the model design and development process (ii) non-commercial character (iii) cross-platform support (iv) easier typo and logic error detection in the description of the models and (v) minimization of working knowledge of programming and AMLs to perform mathematical programming modeling. Overall, this is a presentation of a complete workable scheme on using LaTeX for mathematical programming modeling which assists in furthering our ability to reproduce and replicate scientific work.

scholarly journals Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

2017 ◽  
Vol 1 (2) ◽  
pp. 82 ◽  
Author(s):  
Tirana Noor Fatyanosa ◽  
Andreas Nugroho Sihananto ◽  
Gusti Ahmad Fanshuri Alfarisy ◽  
M Shochibul Burhan ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Non Linear ◽  
Speed Up ◽  
Linear Problems

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

scholarly journals Differential Evolution with Linear Bias Reduction in Parameter Adaptation

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 283
Author(s):  
Vladimir Stanovov ◽  
Shakhnaz Akhmedova ◽  
Eugene Semenkin
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Bias Reduction ◽  
Benchmark Problems ◽  
Basic Algorithm ◽  
Optimization Stage ◽  
Speed Up ◽  
Comparison Of The Results ◽  
Lehmer Mean

In this study, a new parameter control scheme is proposed for the differential evolution algorithm. The developed linear bias reduction scheme controls the Lehmer mean parameter value depending on the optimization stage, allowing the algorithm to improve the exploration properties at the beginning of the search and speed up the exploitation at the end of the search. As a basic algorithm, the L-SHADE approach is considered, as well as its modifications, namely the jSO and DISH algorithms. The experiments are performed on the CEC 2017 and 2020 bound-constrained benchmark problems, and the performed statistical comparison of the results demonstrates that the linear bias reduction allows significant improvement of the differential evolution performance for various types of optimization problems.

scholarly journals An Empirical Investigation on System and Statement Level Parallelism Strategies for Accelerating Scatter Search Using Handel-C and Impulse-C

VLSI Design ◽  
2012 ◽  
Vol 2012 ◽  
pp. 1-11
Author(s):  
M. Walton ◽  
O. Ahmed ◽  
G. Grewal ◽  
S. Areibi
Keyword(s):  
Optimization Problems ◽  
Scatter Search ◽  
Population Based ◽  
Field Programmable ◽  
Speed Up ◽  
Time Required ◽  
High Level ◽  
Level Parallelism ◽  
Code Optimizations ◽  
Established Population

Scatter Search is an effective and established population-based metaheuristic that has been used to solve a variety of hard optimization problems. However, the time required to find high-quality solutions can become prohibitive as problem sizes grow. In this paper, we present a hardware implementation of Scatter Search on a field-programmable gate array (FPGA). Our objective is to improve the run time of Scatter Search by exploiting the potentially massive performance benefits that are available through the native parallelism in hardware. When implementing Scatter Search we employ two different high-level languages (HLLs): Handel-C and Impulse-C. Our empirical results show that by effectively exploiting source-code optimizations, data parallelism, and pipelining, a 28x speed up over software can be achieved.

Intelligent Agents in a Trust Environment

2002 ◽  
pp. 98-108
Author(s):  
Rahul Singh ◽  
Mark A. Gill
Keyword(s):  
Distributed System ◽  
Intelligent Agents ◽  
Single Agent ◽  
Intelligent Agent ◽  
The Internet ◽  
Commercial Activity ◽  
Agent Based ◽  
Trust Relationships ◽  
Multi Agent ◽  
Oriented Activity

Intelligent agents and multi-agent technologies are an emerging technology in computing and communications that hold much promise for a wide variety of applications in Information Technology. Agent-based systems range from the simple, single agent system performing tasks such as email filtering, to a very complex, distributed system of multiple agents each involved in individual and system wide goal-oriented activity. With the tremendous growth in the Internet and Internet-based computing and the explosion of commercial activity on the Internet in recent years, intelligent agent-based systems are being applied in a wide variety of electronic commerce applications. In order to be able to act autonomously in a market environment, agents must be able to establish and maintain trust relationships. Without trust, commerce will not take place. This research extends previous work in intelligent agents to include a mechanism for handling the trust relationship and shows how agents can be fully used as intermediaries in commerce.

scholarly journals OCCAM: prediction of small ORFs in bacterial genomes by means of a target-decoy database approach and machine learning techniques

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Fabio R. Cerqueira ◽  
Ana Tereza Ribeiro Vasconcelos
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Open Reading Frames ◽  
Machine Learning Techniques ◽  
Bacterial Genomes ◽  
Small Proteins ◽  
Learning Techniques ◽  
Speed Up ◽  
Computational Procedures ◽  
Small Orfs

Abstract Small open reading frames (ORFs) have been systematically disregarded by automatic genome annotation. The difficulty in finding patterns in tiny sequences is the main reason that makes small ORFs to be overlooked by computational procedures. However, advances in experimental methods show that small proteins can play vital roles in cellular activities. Hence, it is urgent to make progress in the development of computational approaches to speed up the identification of potential small ORFs. In this work, our focus is on bacterial genomes. We improve a previous approach to identify small ORFs in bacteria. Our method uses machine learning techniques and decoy subject sequences to filter out spurious ORF alignments. We show that an advanced multivariate analysis can be more effective in terms of sensitivity than applying the simplistic and widely used e-value cutoff. This is particularly important in the case of small ORFs for which alignments present higher e-values than usual. Experiments with control datasets show that the machine learning algorithms used in our method to curate significant alignments can achieve average sensitivity and specificity of 97.06% and 99.61%, respectively. Therefore, an important step is provided here toward the construction of more accurate computational tools for the identification of small ORFs in bacteria.

scholarly journals Hyperspectral Image Classification Using Parallel Autoencoding Diabolo Networks on Multi-Core and Many-Core Architectures

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 411 ◽  
Author(s):  
Emanuele Torti ◽  
Alessandro Fontanella ◽  
Antonio Plaza ◽  
Javier Plaza ◽  
Francesco Leporati
Keyword(s):  
Deep Learning ◽  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Unsupervised Classification ◽  
Hyperspectral Data ◽  
Machine Learning Techniques ◽  
High Data ◽  
Learning Techniques ◽  
Speed Up ◽  
Many Core

One of the most important tasks in hyperspectral imaging is the classification of the pixels in the scene in order to produce thematic maps. This problem can be typically solved through machine learning techniques. In particular, deep learning algorithms have emerged in recent years as a suitable methodology to classify hyperspectral data. Moreover, the high dimensionality of hyperspectral data, together with the increasing availability of unlabeled samples, makes deep learning an appealing approach to process and interpret those data. However, the limited number of labeled samples often complicates the exploitation of supervised techniques. Indeed, in order to guarantee a suitable precision, a large number of labeled samples is normally required. This hurdle can be overcome by resorting to unsupervised classification algorithms. In particular, autoencoders can be used to analyze a hyperspectral image using only unlabeled data. However, the high data dimensionality leads to prohibitive training times. In this regard, it is important to realize that the operations involved in autoencoders training are intrinsically parallel. Therefore, in this paper we present an approach that exploits multi-core and many-core devices in order to achieve efficient autoencoders training in hyperspectral imaging applications. Specifically, in this paper, we present new OpenMP and CUDA frameworks for autoencoder training. The obtained results show that the CUDA framework provides a speed-up of about two orders of magnitudes as compared to an optimized serial processing chain.

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