Rafael Stubs Parpinelli

Data mining with an ant colony optimization algorithm

The paper proposes an algorithm for data mining called Ant-Miner (ant-colony-based data miner). The goal of Ant-Miner is to extract classification rules from data. The algorithm is inspired by both research on the behavior of real ant colonies and some data mining concepts as well as principles. We compare the performance of Ant-Miner with CN2, a well-known data mining algorithm for classification, in six public domain data sets. The results provide evidence that: 1) Ant-Miner is competitive with CN2 with respect to predictive accuracy, and 2) the rule lists discovered by Ant-Miner are considerably simpler (smaller) than those discovered by CN2.

New inspirations in swarm intelligence: a survey

The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Evolutionary computation and swarm intelligence meta-heuristics are outstanding examples that nature has been an unending source of inspiration. The behaviour of bees, bacteria, glow-worms, fireflies, slime moulds, cockroaches, mosquitoes and other organisms have inspired swarm intelligence researchers to devise new optimisation algorithms. This tutorial highlights the most recent nature-based inspirations as metaphors for swarm intelligence meta-heuristics. We describe the biological behaviours from which a number of computational algorithms were developed. Also, the most recent and important applications and the main features of such meta-heuristics are reported.

7 – A Survey of Swarm Algorithms Applied to Discrete Optimization Problems

Most swarm intelligence algorithms were devised for continuous optimization problems. However, they have been adapted for discrete optimization as well with applications in different domains. This survey aims at providing an updated review of research of swarm intelligence algorithms for discrete optimization problems, comprising combinatorial or binary. The biological inspiration that motivated the creation of each swarm algorithm is introduced, and later, the discretization and encoding methods are used to adapt each algorithm for discrete problems. Methods are compared for different classes of problems and a critical analysis is provided, pointing to future trends.

An eco-inspired evolutionary algorithm applied to numerical optimization

The search for nature-inspired ideas, models and computational paradigms always was of great interest for computer scientists, particularly for those from the Natural Computing area. The concept of optimization is present in several natural processes as in the evolution of species, in the behavior of social groups, in the dynamics of the immune system, in the food search strategies and ecological relationships of different animal populations. This work uses the ecological concepts of habitats, ecological relationships and ecological successions to build an ecology-inspired optimization algorithm, named ECO. The proposed approach uses several populations of candidate solutions that cooperates and coevolves with each other, according to a given meta-heuristic. In this particular work, we used the Artificial Bee Colony (ABC) algorithm as the main meta-heuristic. Experiments were done for optimizing benchmarck mathematical functions. Results were compared with the ABC algorithm running without the ecology concepts previously mentioned. The ECO algorithm performed significantly better than the ABC, especially as the dimensionality of the functions increase, possibly thanks to the ecological interactions (intra and inter-habitats) that enabled the coevolution of populations. Results suggest that the eco-inspired algorithm can be an interesting alternative for numerical optimization.

Population-based harmony search using GPU applied to protein structure prediction

This work presents a new evolutionary algorithm based on the standard harmony search strategy, called population-based harmony search PBHS. Also, this work provides a parallelisation method for the proposed PBHS by using graphical processing units GPU, allowing multiple function evaluations at the same time. Experiments were done using a benchmark of a hard scientific problem: protein structure prediction with the AB-2D off-lattice model. The performance and the solution quality were evaluated and compared using four implementations: two concerning the standard HS, one running in CPU and another running in GPU, and two implementations concerning the PBHS, also running in CPU and in GPU. Results show that the quality of solutions and speed-ups achieved by the PBHS is significantly better than the HS.

Parallelism, hybridism and coevolution in a multi-level ABC-GA approach for the protein structure prediction problem

This paper reports the hybridization of the artificial bee colony (ABC) and a genetic algorithm (GA), in a hierarchical topology, a step ahead of a previous work. We used this parallel approach for solving the protein 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), and several aspects of the modeling and implementation are presented and discussed. The performance of the hybrid‐hierarchical ABC‐GA approach was compared with a hybrid‐hierarchical ABC‐only approach for four benchmark instances. Results show that the hybridization of the ABC with the GA improves the quality of solutions caused by the coevolution effect between them and their search behavior. Copyright

A computational ecosystem for optimization: review and perspectives for future research

Nature exhibits extremely diverse, dynamic, robust, complex and fascinating phenomena and, since long ago, it has been a great source of inspiration for solving hard and complex problems in computer science. Hence, the search for plausible biologically inspired ideas, models and computational paradigms always drew the interest of computer scientists. It is worth mentioning that most bio-inspired algorithms only focuses on and took inspiration from specific aspects of the natural phenomena. However, in nature, biological systems are interlinked to each other, e.g., biological ecosystems. The ecosystem as a whole can be composed by species that respond to environmental and ecological stimuli. This work reviews the theoretical foundations and applications of a computational ecosystem for optimization, named ECO. Also, as some concepts and processes inherent to biological ecosystems have already been explored in the ECO approach, some related works are described. Finally, several future research directions are pointed.

Bioluminescent Swarm Optimization Algorithm

Evolutionary Computation (EC) is a research area of metaheuristics mainly applied to real-world optimization problems. EC is inspired by biological mechanisms such as reproduction, mutation, recombination, natural selection and collective animal behavior. Two branches of EC can be highlighted: Evolutionary Algorithms (EA) comprising Genetic Algorithms (Goldberg, 1989), Genetic Programming (Koza, 1992), Differential Evolution (Storn & Price, 1997), Harmony Search (Geem et al., 2001), and others; and Swarm Intelligence (SI) comprising Ant Colony Optimization (ACO) (Dorigo & Stutzle, 2004) and Particle Swarm Optimization (PSO) (Kennedy & Eberhart, 2001; Poli et al., 2007) and others. The ACO metaheuristic1 is inspired by the foraging behavior of ants. On the other hand, the PSO metaheuristic2 is motivated by the coordinated movement of fish schools and bird flocks. Both ACO and PSO approaches have been applied successfully in a vast range of problems (Clerc, 2006; Dorigo & Stutzle, 2004). In recent years, new SI algorithms were proposed. They have in common biological inspirations, such as bacterial foraging (Passino, 2002), slime molds life cycle (Monismith & Mayfield, 2008), various bees behaviors (Karaboga & Akay, 2009), cockroaches infestation (Havens et al., 2008), mosquitoes host-seeking (Feng et al., 2009), bats echolocation (Yang, 2010), and fireflies bioluminescense (Krishnanand & Ghose, 2005; 2009; Yang, 2009). This work proposes a new swarm-based evolutionary approach based on the bioluminescent behavior of fireflies, called Bioluminescent Swarm Optimization (BSO) algorithm. The BSO uses two basic characteristics of the Glow-worm Swarm Optimization (GSO) algorithm proposed by (Krishnanand & Ghose, 2005): the luciferin attractant, and the stochastic neighbor selection. However, BSO goes further introducing new features such as: stochastic adaptive step sizing, global optimum attraction, leader movement, and mass extinction. Besides, the proposed algorithm is hybridized with two local search techniques: local unimodal sampling and single-dimension perturbation. All these features makes BSO a powerful algorithm for hard optimization problems. Experiments were done to analyze the sensitivity of the BSO to control parameters. Later, extensive experiments were performed using several benchmark functions with high

A Hierarchical Clustering Strategy to Improve the Biological Plausibility of an Ecology-Based Evolutionary Algorithm

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 co-evolution of populations and to a more bio-plausible probabilistic strategy for habitats definition. Also, a critical parameter was suppressed.

Biological plausibility in optimisation: an ecosystemic view

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.