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A Framework for Implementing Metaheuristic algorithms Using Intercellular Communication

FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2021年 9卷 660148页

作者： Ortiz, Yerko Carrion, Javier Lahoz-Beltra, Rafael Gutierrez, Martin Diego Portales Univ Fac Engn & Sci Sch Informat & Telecommun Santiago Chile Univ Complutense Madrid Fac Biol Sci Dept Biodivers Ecol & Evolut Biomath Madrid Spain

Metaheuristics (MH) are Artificial Intelligence procedures that frequently rely on evolution. MH approximate difficult problem solutions, but are computationally costly as they explore large solution spaces. This work pursues to lay the foundations of general mappings for implementing MH using Synthetic Biology constructs in cell colonies. Two advantages of this approach are: harnessing large scale parallelism capability of cell colonies and, using existing cell processes to implement basic dynamics defined in computational versions. We propose a framework that maps MH elements to synthetic circuits in growing cell colonies to replicate MH behavior in cell colonies. Cell-cell communication mechanisms such as quorum sensing (QS), bacterial conjugation, and environmental signals map to evolution operators in MH techniques to adapt to growing colonies. As a proof-of-concept, we implemented the workflow associated to the framework: automated MH simulation generators for the gro simulator and two classes of algorithms (Simple Genetic algorithms and Simulated Annealing) encoded as synthetic circuits. Implementation tests show that synthetic counterparts mimicking MH are automatically produced, but also that cell colony parallelism speeds up the execution in terms of generations. Furthermore, we show an example of how our framework is extended by implementing a different computational model: The Cellular Automaton.

关键词： agent based model synthetic biology bioinspired algorithms cell-cell communication metaheuristics framework gro

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Parallel Hybrid Genetic Algorithm for Solving Design and Optimization Problems

Parallel Hybrid Genetic Algorithm for Solving Design and Opt...

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International Symposium on Computer Science, Digital Economy and Intelligent Systems (CSDEIS)

作者： Gladkov, L. A. Gladkova, N., V Semushin, E. Y. Southern Fed Univ Taganrog Russia

ISBN: (纸本)9783030392161;9783030392154

The paper considers a problem of building the hybrid algorithm for solving the optimization design tasks on the basis of integration of different methods of computation intelligence. The authors describe the definition and the main approaches to building the hybrid systems and demonstrate the possibilities of integration of the evolutionary design and multi-agent systems methods The different approaches to evolutionary design of the agents are considered. Different methods of parallelizing the computational process and the main models of parallel genetic algorithms, their benefits and shortcomings are described and analyzed in the paper. A hybrid parallel genetic algorithm for searching and optimization of the design decisions is developed in the paper. The algorithm is implemented as software subsystem and investigated in terms of its effectiveness.

关键词： Design tasks bioinspired algorithms Hybrid methods Parallel genetic algorithm Multiagent systems

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Using Swarm Intelligence in Unmanned Aerial Vehicles for Unknown Location Fixed Target Search 10

Using Swarm Intelligence in Unmanned Aerial Vehicles for Unk...

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10th Euro-American Conference on Telematics and Information Systems (EATIS)

作者： Paula, Patricia de Sousa Ferreira Sarmento, Wellington W. Louis Paillard, Gabriel A. de Castro, Miguel Franklin Univ Fed Ceara Fortaleza Ceara Brazil

ISBN: (纸本)9781450377119

The context of this research is the use of bioinspired algorithms applied to unmanned aerial vehicles (UAV) to search for a fixed target of unknown location. A target can be a lost human being or a broken vehicle, for example. Swarm algorithms used with UAVs can be adapted to perform better than a simple scanning algorithm such as Parallel Path Finder. The Particle Swarm Optimization and Bat Algorithm algorithms are compared using constraints such as UAV battery life and the size of the search area. Thus, the best solution to this problem is shown, among the adapted ones, considering the applied restrictions.

关键词： Swarm intelligence UAV bioinspired algorithms Mobile

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Integrated Approach to the Solution of Computer-Aided Design Problems 4th

Integrated Approach to the Solution of Computer-Aided Design...

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4th International Scientific Conference on Intelligent Information Technologies for Industry (IITI)

作者： Gladkov, L. A. Gladkova, N., V Gusev, D. Y. Semushina, N. S. Southern Fed Univ Taganrog Russia

ISBN: (纸本)9783030500979

This article describes an integrated approach to the placement and routing of elements of digital computing devices of large dimensions. The approach is based on the joint decision of the problem of designing such devices using fuzzy control methods, multiagent systems and parallel computing. The authors describe the problem under consideration and show a brief analysis of existing approaches to its solution. The article describes the following main points: the structure of the proposed algorithm and its main stages;developed genetic operators crossover;proposed model of the formation of the population of solutions;developed heuristics, operators and search strategies for optimal solutions. The structure of the parallel search algorithm is developed. A scheme for parallelizing the computation process and on the basis of the island model is proposed. The article shows the results of computational experiments. These experiments confirm the effectiveness of the proposed method. The authors comment on the brief analysis of the results.

关键词： Computer-aided design Optimization tasks bioinspired algorithms Hybrid methods Multiagent systems Parallel computing

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Using swarm intelligence in unmanned aerial vehicles for unknown location fixed target search 20

Using swarm intelligence in unmanned aerial vehicles for unk...

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Proceedings of the 10th Euro-American Conference on Telematics and Information Systems

作者： Patricia de Sousa Paula Wellington W. Ferreira Sarmento Gabriel A. Louis Paillard Miguel Franklin de Castro Universidade Federal do Ceará Fortaleza Ceará Brasil

ISBN: (纸本)9781450377119

关键词： swarm intelligence UAV mobile bioinspired algorithms

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Development and Research of the Hybrid Approach to the Solution of Optimization Design Problems 3rd

Development and Research of the Hybrid Approach to the Solut...

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3rd International Scientific Conference on Intelligent Information Technologies for Industry (IITI)

作者： Gladkov, Leonid A. Gladkova, Nadezhda V. Leiba, Sergey N. Strakhov, Nikolay E. Southern Fed Univ Taganrog Russia

ISBN: (纸本)9783030018214

The article suggests a hybrid approach to solving optimization problems of computer-aided design. As an example, to illustrate the proposed approach, the problems of location and tracing of fragments of circuits of digital electronic computing equipment are chosen. The statement of the problem is given, limitations of the domain of admissible solutions are chosen and a criterion for estimating the quality of the solutions is formulated. A hybrid approach is described on the basis of a combination of evolutionary search methods, the mathematical apparatus of fuzzy logic and the possibilities of parallel organization of the computational process. A modified migration operator is proposed to exchange information between solution populations in the process of performing parallel computations. The structure of the parallel search algorithm is developed. Features of software implementation of the proposed hybrid algorithm are considered. A brief description of the computational experiments that confirm the effectiveness of the proposed method is presented.

关键词： Design tasks bioinspired algorithms Neural networks Hybrid methods Parallel computing

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Chaotic metaheuristic algorithms for learning and reproduction of robot motion trajectories

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NEURAL COMPUTING & APPLICATIONS 2018年第4期30卷 1065-1083页

作者： Mitic, Marko Vukovic, Najdan Petrovic, Milica Miljkovic, Zoran Univ Belgrade Fac Mech Engn Prod Engn Dept Kraljice Marije 16 Belgrade 11120 35 Serbia Univ Belgrade Fac Mech Engn Innovat Ctr Kraljice Marije 16 Belgrade 11120 35 Serbia

Most of today's mobile robots operate in controlled environments prone to various unpredictable conditions. Programming or reprogramming of such systems is time-consuming and requires significant efforts by number of experts. One of the solutions to this problem is to enable the robot to learn from human teacher through demonstrations or observations. This paper presents novel approach that integrates Learning from Demonstrations methodology and chaotic bioinspired optimization algorithms for reproduction of desired motion trajectories. Demonstrations of the different trajectories to reproduce are gathered by human teacher while teleoperating the mobile robot in working environment. The learning (optimization) goal is to produce such sequence of mobile robot actuator commands that generate minimal error in the final robot pose. Four different chaotic methods are implemented, namely chaotic Bat Algorithm, chaotic Firefly Algorithm, chaotic Accelerated Particle Swarm Optimization and newly developed chaotic Grey Wolf Optimizer (CGWO). In order to determine the best map for CGWO, this algorithm is tested on ten benchmark problems using ten well-known chaotic maps. Simulations compare aforementioned algorithms in reproduction of two complex motion trajectories with different length and shape. Moreover, these tests include variation of population in swarm and demonstration examples. Real-world experiment on a nonholonomic mobile robot in indoor environment proves the applicability of the proposed approach.

关键词： bioinspired algorithms Metaheuristic optimization Chaos Mobile robot Learning from Demonstration Grey Wolf Optimizer Particle Swarm Optimization Bat Algorithm Firefly Algorithm

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A New Methodology for Automatic Cluster-Based Kriging Using K-Nearest Neighbor and Genetic algorithms

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INFORMATION 2019年第11期10卷 357页

作者： Yasojima, Carlos Protazio, Joao Meiguins, Bianchi Neto, Nelson Morais, Jefferson Fed Univ Para Fac Comp Sci BR-66075110 Belem Para Brazil Fed Univ Para Fac Stat BR-66075110 Belem Para Brazil

Kriging is a geostatistical interpolation technique that performs the prediction of observations in unknown locations through previously collected data. The modelling of the variogram is an essential step of the kriging process because it drives the accuracy of the interpolation model. The conventional method of variogram modelling consists of using specialized knowledge and in-depth study to determine which parameters are suitable for the theoretical variogram. However, this situation is not always possible, and, in this case, it becomes interesting to use an automatic process. Thus, this work aims to propose a new methodology to automate the estimation of theoretical variogram parameters of the kriging process. The proposed methodology is based on preprocessing techniques, data clustering, genetic algorithms, and the K-Nearest Neighbor classifier (KNN). The performance of the methodology was evaluated using two databases, and it was compared to other optimization techniques widely used in the literature. The impacts of the clustering step on the stationary hypothesis were also investigated with and without trend removal techniques. The results showed that, in this automated proposal, the clustering process increases the accuracy of the kriging prediction. However, it generates groups that might not be stationary. Genetic algorithms are easily configurable with the proposed heuristic when setting the variable ranges in comparison to other optimization techniques, and the KNN method is satisfactory in solving some problems caused by the clustering task and allocating unknown points into previously determined clusters.

关键词： spatial interpolation variogram fitting clustering bioinspired algorithms

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Bacterial Foraging Optimization for VLSI Fragments Placement 2nd

Bacterial Foraging Optimization for VLSI Fragments Placement

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2nd International Scientific Conference on Intelligent Information Technologies for Industry (IITI)

作者： Zaporozhets, Dmitry Zaruba, Daria Southern Fed Univ Rostov Na Donu Russia

ISBN: (纸本)9783319683218

The paper deals with one of the most significant problem of computer design - a VLSI placement problem. The authors prove a relevance of new heuristic methods for the given problem. The VLSI placement problem is formulated and an optimization criterion is suggested. As an optimization method there is suggested an algorithm based on bacterial colony behavior in nature according with environment conditions. On the basis of these observations it is developed a new bioinspired algorithm for the VLSI placement problem. There are conducted series of computational experiments for comparison of the developed algorithm with known VLSI placement algorithms on the basis of IBM benchmarks. Obtained results are represented in a table. Conducted researches shown the efficiency of the suggested approach better then 8-10% on average.

关键词： VLSI Optimization bioinspired algorithms Bacterial foraging optimization IBM benchmarks

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CIP-aiNet: An Entropy-based Immune Network for Multiple Clustering

CIP-aiNet: An Entropy-based Immune Network for Multiple Clus...

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IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Borges, Ederson Coelho, Guilherme Palermo Univ Campinas SP Sch Technol FT Limeira Brazil

ISBN: (纸本)9781509060177

Datasets interpretation can be a hard task due to their complexity and heterogeneity. Several works have dealt with multiple clustering to bring in new information from databases, which differs from traditional approaches that present a single partition as a solution. This work proposes a novel algorithm based on the Immune Network Theory and also in Information Theoretical Learning. This approach employs the Cross-Information Potential (CIP), based on Renyi's Entropy, to evaluate possible solutions and does not impose any assumption about the data distribution. Besides that, the proposed algorithm, named CIP-aiNet, maintains a diverse set of solutions to allow multiple possible interpretations. CIP-aiNet was compared with the minCEntropy algorithm, which also uses Information Theory concepts, and with an Immune Network-based algorithm that uses the Euclidean distance to evaluate the partitions. The novel algorithm presents better solutions for complex datasets compared to the Immune Network-based algorithm, and returns multiple good solutions in a single execution, which improves its outcomes.

关键词： artificial immune systems cross-information potential cluster evaluation function information theoretical learning bioinspired algorithms machine learning

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