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2013 | Buch

Community Optimization Kapitel lesen Erstes Kapitel lesen

Transactions on Computational Science XXI

Special Issue on Innovations in Nature-Inspired Computing and Applications

herausgegeben von: Marina L. Gavrilova, C. J. Kenneth Tan, Ajith Abraham

Verlag: Springer Berlin Heidelberg

Buchreihe : Lecture Notes in Computer Science

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This, the 21st issue of the Transactions on Computational Science journal, edited by Ajith Abraham, is devoted to the topic of nature-inspired computing and applications. The 15 full papers included in the volume focus on the topics of neurocomputing, evolutionary algorithms, swarm intelligence, artificial immune systems, membrane computing, computing with words, artificial life and hybrid approaches.

Inhaltsverzeichnis

Frontmatter
Community Optimization
Abstract
In recent years a number of web-technology supported communities of humans have been developed. Such a web community is able to let emerge a collective intelligence with a higher performance in solving problems than the single members of the community. Thus, collective intelligence systems are explicitly designed to take advantage of these increased capabilities. A well-known collective intelligence system is Wikipedia, the web encyclopedia. It uses a collaborative web community of authors, which improves and completes the content of articles. The quality of a certain number of these articles comes close to some degree to that of a famous printed encyclopedia. Based on such successes of collective intelligence systems, the question arises, whether such a collaborative web community could also be capable of function optimization.
This paper introduces an optimization algorithm called Community Optimization (CO), which optimizes a function by simulating a collaborative web community, which edits or improves an article-base, or, more general, a knowledge-base. The knowledge-base represents the problem to be solved and is realized as a real valued vector. The different vector components (decision variables) represent different topics contained in this knowledge-base. Thus, the dimension of the problem is the number of topics to be improved by the simulated community, whereby the dimension remains static. In order to realize this, CO implements a behavioral model of collaborative human communities derived from the human behavior that can be observed within certain web communities (e.g., Wikipedia or open source communities). The introduced CO method is applied to eight well-known benchmark problems for lower as well as higher dimensions. CO turns out to be the best choice in 9 cases and the Fully Informed Particle Swarm Optimization (FIPS) as well as Differential Evolution (DE) approaches in 4 cases. Concerning the high dimensional problems, CO significantly outperformed FIPS as well as DE in 6 of 8 cases and seems to be a suitable approach for high dimensional problems.
Christian B. Veenhuis
Diffusion of Innovation Simulation Using an Evolutionary Algorithm
Abstract
The diffusion of innovation theory aims to explain how new ideas and practices are disseminated among social system members. A significant number of the existing models is based on the use of parameters which determine the process of innovation adoption, and rely on simple mathematical functions centered in the observation and description of diffusion patterns. These models enable a more explicit diffusion process study, but their use involves the estimation of diffusion coefficients, usually obtained from historical data or chronological series. This raises some application problems in contexts where there is no data or the data is insufficient. This paper proposes the use of evolutionary computation as an alternative approach for the simulation of innovation diffusion within organizations. To overcome some of the problems inherent to existing models an evolutionary algorithm is proposed based on a probabilistic approach. The results of the simulations that were done to validate the algorithm revealed to be very promissing in this context.
Simulation experiment results are presented that reveals a very promising approach of the proposed model.
Luciano Sampaio, João Varajão, E. J. Solteiro Pires, P. B. de Moura Oliveira
Driving Robots Using Emotions
Abstract
Researchers have tried to embed synthetic emotions into robot much like their biological counterparts. While many have shown the effect of emotion on decision-making for the robot, the scenarios that portray when to use emotions for robots are rare. In this paper, we evaluate the performance of a robot by empowering it with a decision-making capability which uses synthetic emotions. Since from the robot’s perspective the environment is stochastic, it needs to make the right decision for survival. A Comfort level is defined as a metric which determines the quality of life of the robot. The robot possesses various needs and urges all of which influence its decisions. The main objective was to make the robot perform high profile tasks rather than menial ones so as to increase its utility. Results obtained from experiments conducted using a real situated robot with and without emotion indicate that emotion aids more significantly when the environment has abundant resources.
Shashi Shekhar Jha, Shrinivasa Naika C.L., Shivashankar B. Nair
Land Cover Feature Extraction of Multi-spectral Satellite Images Based on Extended Species Abundance Model of Biogeography
Abstract
This paper presents a land cover feature extraction technique based on the extended species abundance model of biogeography [15, 18] where we consider the HSI as a function of different combinations of SIVs depending upon the characteristics of the habitat under consideration as an extension to the classical BBO [33, 39]. Making use of the proposed hypotheses, we calculate the HSI of each of the habitats representing the image pixels using two different functions namely entropy and standard deviation and hence maximize the classification efficiency achieved by adapting to dynamic changes in the HSI function definition. The proposed algorithm has been successfully tested on two different multi-spectral satellite image datasets. We also incorporate the above extended model in our previously designed hybrid bio-inspired intelligent classifier [16] and compare its performance with the original hybrid classifier and twelve other classifiers on the 7-Band Alwar Image.
Lavika Goel, Daya Gupta, V. K. Panchal
Developing Issues for Ant Colony System Based Approach for Scheduling Problems
Abstract
This paper describes some developing issues for ACS based software tools to support decision making process and solve the problem of generating a sequence of jobs that minimizes the total weighted tardiness for a set of jobs to be processed in a single machine. An Ant Colony System (ACS) based algorithm performance is validated with benchmark problems available in the OR library. The obtained results were compared with the optimal (best available results in some cases) and permit to conclude about ACS efficiency and effectiveness. The ACS performance and respective statistical significance was evaluated.
Ana Madureira, Ivo Pereira, Ajith Abraham
Multiobjective Optimization of Green Sand Mould System Using Chaotic Differential Evolution
Abstract
Many industrial optimization cases present themselves in a multi-objective (MO) setting (where each of the objectives portrays different aspects of the problem). Therefore, it is important for the decision-maker to have a solution set of options prior to selecting the best solution. In this work, the weighted sum scalarization approach is used in conjunction with three meta-heuristic algorithms; differential evolution (DE), chaotic differential evolution (CDE) and gravitational search algorithm (GSA). These methods are then used to generate the approximate Pareto frontier to the green sand mould system problem. The Hypervolume Indicator (HVI) is applied to gauge the capabilities of each algorithm in approximating the Pareto frontier. Some comparative studies were then carried out with the algorithms developed in this work and that from the previous work. Analysis on the performance as well as the quality of the solutions obtained by these algorithms is shown here.
T. Ganesan, I. Elamvazuthi, Ku Zilati Ku Shaari, P. Vasant
Categorical Feature Reduction Using Multi Objective Genetic Algorithm in Cluster Analysis
Abstract
In the paper, real coded multi objective genetic algorithm based K-clustering method has been studied, K represents the number of clusters. In K-clustering algorithm value of K is known. The searching power of Genetic Algorithm (GA) is exploited to search for suitable clusters and centers of clusters so that intra-cluster distance (Homogeneity, H) and inter-cluster distances (Separation, S) are simultaneously optimized. It is achieved by measuring H and S using Mod distance per feature metric, suitable for categorical features (attributes). We have selected 3 benchmark data sets from UCI Machine Learning Repository containing categorical features only.
The paper proposes two versions of MOGA based K-clustering algorithm. In proposed MOGA (H, S), all features are taking part in building chromosomes and calculation of H and S values. In MOGA_Feature_Selection (H, S), selected features take part to build chromosomes, relevant for clusters. Here, K-modes is hybridized with GA. We have used hybridized GA to combine global searching capabilities of GA with local searching capabilities of K-modes. Considering context sensitivity, we have used a special crossover operator called “pairwise crossover” and “substitution”. The main contribution of this paper is simultaneous dimensionality reduction and optimization of objectives using MOGA.
Dipankar Dutta, Paramartha Dutta, Jaya Sil
Tuning Meta-Heuristics Using Multi-agent Learning in a Scheduling System
Abstract
In complexity theory, scheduling problem is considered as a NP-complete combinatorial optimization problem. Since Multi-Agent Systems manage complex, dynamic and unpredictable environments, in this work they are used to model a scheduling system subject to perturbations. Meta-heuristics proved to be very useful in the resolution of NP-complete problems. However, these techniques require extensive parameter tuning, which is a very hard and time-consuming task to perform. Based on Multi-Agent Learning concepts, this article propose a Case-based Reasoning module in order to solve the parameter-tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance.
Ivo Pereira, Ana Madureira, P. B. de Moura Oliveira, Ajith Abraham
Vertical Transfer Algorithm for the School Bus Routing Problem
Abstract
In this paper is a solution to the School Bus Routing Problem by the application of a bio-inspired algorithm in the vertical transfer of genetic material to offspring or the inheritance of genes by subsequent generations. The vertical transfer algorithm or Genetic algorithm uses the clusterization population pre-selection operator, tournament selection, crossover-k operator and an intelligent mutation operator called mutation-S. The use of the bio-inspired algorithm to solve SBRP instances show good results about Total Bus Travel Distance and the Number of Buses with the Routes.
Ocotlán Díaz-Parra, Jorge A. Ruiz-Vanoye, Ma. de los Ángeles Buenabad-Arias, Ana Canepa Saenz
An Efficient Craziness Based Particle Swarm Optimization Technique for Optimal IIR Filter Design
Abstract
In this paper an improved version of Particle Swarm Optimization (PSO) called Craziness based PSO (CRPSO) is considered as an efficient optimization tool for designing digital Infinite Impulse Response (IIR) filters. Apart from gaining better control on cognitive and social components of conventional PSO, the CRPSO dictates better performance due to incorporation of craziness parameter in the velocity equation of PSO. This modification in the velocity equation not only ensures the faster searching in the multidimensional search space but also the solution produced is very close to the global optimal solution. The effectiveness of this algorithm is justified with a comparative study of some well established algorithms, namely, Real coded Genetic Algorithm (RGA) and conventional Particle Swarm Optimization (PSO) with a superior CRPSO based outcome for the designed 8th order IIR low pass (LP), high pass (HP), band pass (BP) and band stop (BS) filters. Simulation results affirm that the proposed CRPSO algorithm outperforms its counterparts not only in terms of quality output, i.e., sharpness at cut-off, pass band ripple and stop band attenuation but also in convergence speed with assured stability.
S. K. Saha, R. Kar, D. Mandal, S. P. Ghoshal
Effect of Spatial Structure on the Evolution of Cooperation in the N-Choice Iterated Prisoner’s Dilemma
Abstract
The evolution of cooperation is an enduring conundrum in biology and the social sciences. The prisoner’s dilemma game has emerged as the most promising mathematical metaphors to study cooperation. Mechanisms promoting the evolution of cooperation in two-player, two-strategy spatial iterated prisoner’s dilemma (IPD) games have been discussed in great detail over the past decades. Understanding the effects of repeated interactions in n-choice spatial IPD game is a formidable challenge. In this paper, the simulations are conducted with four different types of neighbourhood structures, and agents update their strategies by probabilistically imitating the strategies of better performing neighbours. During the evolution each agent can modify his own strategy and/or personal feature via a particle swarm optimization approach in order to improve his utility. The particle swarm optimization (PSO) approach is a bionic method which can simulate the interactions among agents in a realistic way. The results show that the evolutionary stability of cooperation does emerge in n-choice spatial IPD game, and the consideration of social cohesion in PSO approach promotes the evolution of cooperation. In addition, the neighbourhood structures and cost-to-benefit ratio increase the capability of cooperation and prevent the invading of defectors.
Xiaoyang Wang, Yang Yi, Huiyou Chang, Yibin Lin
Extracting, Identifying and Visualisation of the Content, Users and Authors in Software Projects
Abstract
The paper proposes a method for extracting, identifying and visualisation of topics, code tiers, users and authors in software projects. In addition to standard information retrieval techniques, we use AST for source code and WordNet ontology to enrich document vectors extracted from parsed code, LSI to reduce its dimensionality and the swarm intelligence in the bee behaviour inspired algorithms to cluster documents contained in it. We extract topics from the identified clusters and visualise them in 3D graphs. Developers within and outside the teams can receive and utilize visualized information from the code and apply them to their projects. This new level of aggregated 3D visualization improves refactoring, source code reusing, implementing new features and exchanging knowledge.
Ivan Polášek, Marek Uhlár
Beehive Based Machine to Give Snapshot of the Ongoing Stories on the Web
Abstract
In this paper we present an approach, inspired by honey bees, that allows us to take a glance at current events by exploring a portion of the Web and extracting keywords, relevant to current news stories. Not unlike the bees, that cooperate together to retrieve little bits of food, our approach uses agents to select random keywords and carry them from one article to another, landing only on the articles relevant to the keyword. Keywords that best represent multiple articles are selected, while keywords not relevant to articles are subsequently discarded and not explored further. Our results show, that with this approach, it is possible to extract keywords relevant to news stories, without utilizing learning methods, or analysis of a data corpus.
Pavol Návrat, Štefan Sabo
Incorporating Highly Explorative Methods to Improve the Performance of Variable Neighborhood Search
Abstract
Variable Neighborhood Search (VNS) is one of the most recently introduced metaheuristics. Although VNS is successfully applied on various problem domains, there is still some room for it to get improved. While VNS has an efficient exploitation strategy, it suffers from its inefficient solution space exploration. To overcome this limitation, VNS can be joined with explorative methods such as Evolutionary Algorithms (EAs) which are global population-based search methods. Due to its effective search space exploration, Differential Evolution (DE) is a popular EA which is a great candidate to be joined with VNS. In this article, two different DEs are proposed to be combined with VNS. The first DE uses explorative evolutionary operators and the second one is a Multi-Population Differential Evolution (MP-DE). Incorporating a number of sub-populations improves the population diversity and increases the chance of reaching to unexplored regions. Both proposed hybrid methods are evaluated on the classical Job Shop Scheduling Problems. The experimental results reveal that the combination of VNS with more explorative method is more reliable to find acceptable solutions. Furthermore, the proposed methods offer competitive solutions compared to the state-of-the-art hybrid EAs proposed to solve JSSPs.
Mohammad R. Raeesi N., Ziad Kobti
Self Organising Maps on Compute Unified Device Architecture for the Performance Monitoring of Emergency Call-Taking Centre
Abstract
The collaborative emergency call-taking information system in the Czech Republic forms a network of cooperating emergency call centers processing emergency calls to the European 112 emergency number. Large amounts of various incident records are stored in the databases. The data can be used for mining spatial and temporal anomalies, as well as for the monitoring and analysis of the performance of the emergency call- taking system. In this paper, we describe a method for knowledge discovery and visualization targeted at the performance analysis of the system with respect to the organization of the emergency call-taking information system and its data characteristics. The method is based on the Kohonen Self-Organising Map (SOM) algorithm and its extension, the Growing Grid algorithm. To handle the massive data, the growing grid algorithm is implemented in a parallel environment using compute unified device architecture. Experimental results illustrate that the proposed method is very efficient.
Václav Snášel, Petr Klement, Petr Gajdoš, Ajith Abraham
Backmatter
Metadaten
Titel
Transactions on Computational Science XXI
herausgegeben von
Marina L. Gavrilova
C. J. Kenneth Tan
Ajith Abraham
Copyright-Jahr
2013
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-642-45318-2
Print ISBN
978-3-642-45317-5
DOI
https://doi.org/10.1007/978-3-642-45318-2

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