Papers by Rafael Stubs Parpinelli
This paper presents a genetic algorithm for resource scheduling in service discovery to
MANETs op... more This paper presents a genetic algorithm for resource scheduling in service discovery to
MANETs operating in emergency scenarios. The shared resources are for example, ambulances or
support cars. Through an appropriate model for scheduling resources, we aim to attend the greatest
number of victims in the affected area. Performance evaluation results on the Network Simulator
NS-3 confirm the effectiveness of genetic algorithm for resource scheduling.
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Polibits, 2015
Bookmarks Related papers MentionsView impact
Resumo. Através da uniao do Teorema Fundamental da Aritmética e do con-ceito formal de número de... more Resumo. Através da uniao do Teorema Fundamental da Aritmética e do con-ceito formal de número de Gödel, proposto pelo mesmo matemático que lhe dá nome, busca-se o desenvolvimento de um algorıtmo criptográfico e sua poste-rior análise. Para isso é criada uma aplicaç ...
Bookmarks Related papers MentionsView impact
PhD Thesis, Feb 19, 2013
Bookmarks Related papers MentionsView impact
Download at: https://dl.dropboxusercontent.com/u/23505329/SOS-UnconstrainedOptimization.zip Symbi... more Download at: https://dl.dropboxusercontent.com/u/23505329/SOS-UnconstrainedOptimization.zip Symbiotic Organisms Search (ANSI C): If you use this code, pls cite the following work: "Min-Yuan Cheng, Doddy Prayogo, Symbiotic Organisms Search: A new metaheuristic optimization algorithm, Computers & Structures, Volume 139, 15 July 2014, Pages 98-112."
Bookmarks Related papers MentionsView impact
Download at:
https://dl.dropboxusercontent.com/u/23505329/ECO-optimizer.zip
The algorithms tha... more Download at:
https://dl.dropboxusercontent.com/u/23505329/ECO-optimizer.zip
The algorithms that are available in this release are: Artificial Bee Colony Algorithm, Particle Swarm Optimization, Differential Evolution, and Biogeographic-based Optimization.
If you use this code, pls cite one of the following works:
1. PARPINELLI, R.S.; LOPES, H.S. Biological Plausibility in Optimization: An Ecosystemic View. International Journal of Bio-Inspired Computation (Online), v. 4, p. 345-358, 2012.
2. PARPINELLI, R.S.; Lopes, H.S. A Hierarchical Clustering Strategy to Improve the Biological Plausibility of an Ecology-based Evolutionary Algorithm. In: Ibero-American Conference on Artificial Intelligence (IBERAMIA), 2012, Cartajena. Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence. Berlin, Heidelberg: Springer-Verlag, 2012. v. 7637. p. 310-319.
Developed in ANSI C under Linux.
Bookmarks Related papers MentionsView impact
This paper applies an ecology-inspired algorithm (ECO) to solve a complex problem from bioinforma... more This paper applies an ecology-inspired algorithm (ECO) to solve a complex problem from bioinformatics. The ecological-inspired algorithm represents a new perspective to develop cooperative evolutionary algorithms. Different algorithms are applied to compose the computational ecosystem, both homogeneously and heterogeneously. The aim is to search low energy conformations for the Protein Structure Prediction problem, concerning the 2D-AB off-lattice model. From the results, the heterogeneous configuration obtained the best conformations for almost all cases, possibly due to the use of different intensification and diversification strategies provided by different search algorithms.
Bookmarks Related papers MentionsView impact
This paper applies a heterogeneous parallel ecology-inspired algorithm (pECO) to solve a complex ... more This paper applies a heterogeneous parallel ecology-inspired algorithm (pECO) to solve a complex problem from bioinformatics. The ecological-inspired algorithm represents a new perspective to develop cooperative evolutionary algorithms. Different algorithms are applied to compose the computational ecosystem in a heterogeneous model. The aim is to search low energy conformations for the Protein Structure Prediction problem, concerning the 3D-AB off-lattice model. Being a problem that demands a lot of computational effort, a parallel master-slave architecture is employed in order to allow the application of the computational ecosystem in a reasonable computing time. From the results, the pECO approach obtained the best conformation for the 13 amino-acid long sequence and competitive results for the other sequences.
Bookmarks Related papers MentionsView impact
The search for biologically plausible ideas, models and computational paradigms always drew the i... more The search for biologically plausible ideas, models and computational paradigms always drew the interest of computer scientists, particularly those from the natural computing area. Also, the concept of optimisation can be abstracted from several natural processes, for instance, in the evolution of species, in the behaviour of social groups, in the dynamics of the immune system, in the food search strategies and in the ecological relationships of different animal populations. Hence, this work highlights the main properties of ecosystems that can be important for building computational tools to solve complex problems. Also, we introduce computational descriptions for such biologically plausible functionalities (e.g., habitats, ecological relationships, ecological succession, and another). The main differential of the discussion presented in this paper is the cooperative use of different populations (candidate solutions) that co-evolve in an ecological context. In addition to the use of different search strategies cooperatively, this work opens the possibility of inserting ecological concepts in the optimisation process allowing the development of new bio-plausible hybrid systems. The potentiality of some ecological concepts is also presented in a simplified ecology-inspired algorithm for optimisation. Finally, concluding remarks and ideas for future research are presented.
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Jonas Krause, Jelson Cordeiro, Rafael Stubs Parpinelli, Heitor Silvério Lopes, A Survey of Swarm ... more Jonas Krause, Jelson Cordeiro, Rafael Stubs Parpinelli, Heitor Silvério Lopes, A Survey of Swarm Algorithms Applied to Discrete Optimization Problems, In: Xin-She Yang, Zhihua Cui, Renbin Xiao, Amir Hossein Gandomi and Mehmet Karamanoglu, Editor(s), Swarm Intelligence and Bio-inspired Computation, Elsevier, Oxford, Pages 169-191, 2013.
Bookmarks Related papers MentionsView impact
The growing complexity of real-world problems has motivated computer scientists to search for eff... more The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Evolutionary Computation and Swarm Intelligence metaheuristics are outstanding examples that nature has been an unending source of inspiration. The behavior of bees, bacteria, glowworms, fireflies, slime molds, cockroaches, mosquitoes and other organisms have inspired swarm intelligence researchers to devise new optimization algorithms. This tutorial highlights the most recent nature-based inspirations as metaphors for swarm intelligence metaheuristics. We describe the biological behaviors from which a number of computational algorithms were developed. Also, the most recent and important applications and the main features of such metaheuristics are reported.
Bookmarks Related papers MentionsView impact
"It is well known that, in nature, populations are dynamic in
space and time. This means that th... more "It is well known that, in nature, populations are dynamic in
space and time. This means that the formation of habitats changes over time and its formation is not deterministic. This work uses the concepts of ecological relationships, ecological successions and probabilistic formation of habitats to build a cooperative search algorithm, named ECO. This work aims at exploring the use of a hierarchical clustering technique to probabilistically set the habitats of the computational ecosystem. The Artificial Bee Colony (ABC) was used in the experiments in
which benchmark mathematical functions were optimized. Results were compared with ABC running alone, and the ECO with and without the use of hierarchical clustering. The ECO algorithm with hierarchical clustering performed better than the other approaches, possibly thanks to the ecological interactions (intra and inter-habitats) that enabled the coevolution of populations and to a more bio-plausible probabilistic strategy for habitats definition. Also, a critical parameter was suppressed."
Bookmarks Related papers MentionsView impact
Page 202. An Ant Colony Algorithm for Classification Rule Discovery 191 Chapter X An Ant Colony A... more Page 202. An Ant Colony Algorithm for Classification Rule Discovery 191 Chapter X An Ant Colony Algorithm for Classification Rule Discovery Rafael S. Parpinelli and Heitor S. Lopes Centro Federal de Educacao Tecnologica ...
Bookmarks Related papers MentionsView impact
"The concept of optimization is present in several natural processes such as the evolution of spe... more "The concept of optimization is present in several natural processes such as the evolution of species, the behavior of social groups and the ecological relationships of different animal populations. This work uses the concepts of habitats, ecological relationships and ecological successions to build a hybrid cooperative search algorithm, named ECO. The Artificial Bee Colony (ABC) and the Particle Swarm Optimization (PSO)
algorithms were used in the experiments where benchmark mathematical functions were optimized. Results were compared with ABC and PSO running alone, and with both algorithms in a well known island model with ring topology, all running without the ecology concepts previously mentioned. The ECO algorithm performed better than the other approaches, especially as the dimensionality of the functions increase, possibly thanks to the ecological interactions (intra and inter-habitats) that enabled the co-evolution of populations. Results suggest that the ECO algorithm can be an interesting alternative for numerical optimization. "
Bookmarks Related papers MentionsView impact
It is well known that, in nature, populations are dynamic in space and time. This means that the ... more It is well known that, in nature, populations are dynamic in space and time. This means that the size of populations oscillate across their habitats over time. This work uses the concepts of habitats, ecological relationships, ecological successions and population dynamics to build a cooperative search algorithm, named ECO. This work aims to explore the population sizing not as a parameter but as a dynamic process. The Artificial Bee Colony (ABC) was used in the experiments where benchmark mathematical functions were optimized. Results were compared with ABC running alone, with and without the use of population dynamics. The ECO algorithm with population dynamics performed better than the other approaches, possibly thanks to the ecological interactions (intra and inter-habitats) that enabled the co-evolution of populations and to a more natural survival selection mechanism by the use of population dynamics.
Bookmarks Related papers MentionsView impact
The field of research that studies the emergent collective intelligence of self-organized and dec... more The field of research that studies the emergent collective intelligence of self-organized and decentralized simple agents is referred to as Swarm Intelligence. It is based on social behavior that can be observed in nature, such as flocks of birds, fish schools and bee hives, where a number of individuals with limited capabilities are able to come to intelligent solutions for complex problems. The computer science community have already learned about the importance of emergent behaviors for complex problem solving. Hence, this book presents some recent advances on Swarm Intelligence, specially on new swarm-based optimization methods and hybrid algorithms for several applications. The content of this book allows the reader to know more both theoretical and technical aspects and applications of Swarm Intelligence.
Bookmarks Related papers MentionsView impact
… : Practice and Experience, Jan 1, 2011
This paper reports the hybridization of the artificial bee colony (ABC) and a genetic algorithm (G... more This paper reports the hybridization of the artificial bee colony (ABC) and a genetic algorithm (GA), ina hierarchical topology, a step ahead of a previous work. We used this parallel approach for solving theprotein structure prediction problem using the three-dimensional hydrophobic-polar model with side-chains(3DHP-SC). The proposed method was run in a parallel processing environment (Beowulf cluster), andseveral aspects of the modeling and implementation are presented and discussed. The performance of thehybrid-hierarchical ABC-GA approach was compared with a hybrid-hierarchical ABC-only approach forfour benchmark instances. Results show that the hybridization of the ABC with the GA improves the qualityof solutions caused by the coevolution effect between them and their search behavior. Copyright © 2011John Wiley & Sons, Ltd.
Bookmarks Related papers MentionsView impact
Uploads
Papers by Rafael Stubs Parpinelli
MANETs operating in emergency scenarios. The shared resources are for example, ambulances or
support cars. Through an appropriate model for scheduling resources, we aim to attend the greatest
number of victims in the affected area. Performance evaluation results on the Network Simulator
NS-3 confirm the effectiveness of genetic algorithm for resource scheduling.
https://dl.dropboxusercontent.com/u/23505329/ECO-optimizer.zip
The algorithms that are available in this release are: Artificial Bee Colony Algorithm, Particle Swarm Optimization, Differential Evolution, and Biogeographic-based Optimization.
If you use this code, pls cite one of the following works:
1. PARPINELLI, R.S.; LOPES, H.S. Biological Plausibility in Optimization: An Ecosystemic View. International Journal of Bio-Inspired Computation (Online), v. 4, p. 345-358, 2012.
2. PARPINELLI, R.S.; Lopes, H.S. A Hierarchical Clustering Strategy to Improve the Biological Plausibility of an Ecology-based Evolutionary Algorithm. In: Ibero-American Conference on Artificial Intelligence (IBERAMIA), 2012, Cartajena. Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence. Berlin, Heidelberg: Springer-Verlag, 2012. v. 7637. p. 310-319.
Developed in ANSI C under Linux.
space and time. This means that the formation of habitats changes over time and its formation is not deterministic. This work uses the concepts of ecological relationships, ecological successions and probabilistic formation of habitats to build a cooperative search algorithm, named ECO. This work aims at exploring the use of a hierarchical clustering technique to probabilistically set the habitats of the computational ecosystem. The Artificial Bee Colony (ABC) was used in the experiments in
which benchmark mathematical functions were optimized. Results were compared with ABC running alone, and the ECO with and without the use of hierarchical clustering. The ECO algorithm with hierarchical clustering performed better than the other approaches, possibly thanks to the ecological interactions (intra and inter-habitats) that enabled the coevolution of populations and to a more bio-plausible probabilistic strategy for habitats definition. Also, a critical parameter was suppressed."
algorithms were used in the experiments where benchmark mathematical functions were optimized. Results were compared with ABC and PSO running alone, and with both algorithms in a well known island model with ring topology, all running without the ecology concepts previously mentioned. The ECO algorithm performed better than the other approaches, especially as the dimensionality of the functions increase, possibly thanks to the ecological interactions (intra and inter-habitats) that enabled the co-evolution of populations. Results suggest that the ECO algorithm can be an interesting alternative for numerical optimization. "
MANETs operating in emergency scenarios. The shared resources are for example, ambulances or
support cars. Through an appropriate model for scheduling resources, we aim to attend the greatest
number of victims in the affected area. Performance evaluation results on the Network Simulator
NS-3 confirm the effectiveness of genetic algorithm for resource scheduling.
https://dl.dropboxusercontent.com/u/23505329/ECO-optimizer.zip
The algorithms that are available in this release are: Artificial Bee Colony Algorithm, Particle Swarm Optimization, Differential Evolution, and Biogeographic-based Optimization.
If you use this code, pls cite one of the following works:
1. PARPINELLI, R.S.; LOPES, H.S. Biological Plausibility in Optimization: An Ecosystemic View. International Journal of Bio-Inspired Computation (Online), v. 4, p. 345-358, 2012.
2. PARPINELLI, R.S.; Lopes, H.S. A Hierarchical Clustering Strategy to Improve the Biological Plausibility of an Ecology-based Evolutionary Algorithm. In: Ibero-American Conference on Artificial Intelligence (IBERAMIA), 2012, Cartajena. Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence. Berlin, Heidelberg: Springer-Verlag, 2012. v. 7637. p. 310-319.
Developed in ANSI C under Linux.
space and time. This means that the formation of habitats changes over time and its formation is not deterministic. This work uses the concepts of ecological relationships, ecological successions and probabilistic formation of habitats to build a cooperative search algorithm, named ECO. This work aims at exploring the use of a hierarchical clustering technique to probabilistically set the habitats of the computational ecosystem. The Artificial Bee Colony (ABC) was used in the experiments in
which benchmark mathematical functions were optimized. Results were compared with ABC running alone, and the ECO with and without the use of hierarchical clustering. The ECO algorithm with hierarchical clustering performed better than the other approaches, possibly thanks to the ecological interactions (intra and inter-habitats) that enabled the coevolution of populations and to a more bio-plausible probabilistic strategy for habitats definition. Also, a critical parameter was suppressed."
algorithms were used in the experiments where benchmark mathematical functions were optimized. Results were compared with ABC and PSO running alone, and with both algorithms in a well known island model with ring topology, all running without the ecology concepts previously mentioned. The ECO algorithm performed better than the other approaches, especially as the dimensionality of the functions increase, possibly thanks to the ecological interactions (intra and inter-habitats) that enabled the co-evolution of populations. Results suggest that the ECO algorithm can be an interesting alternative for numerical optimization. "