Biology of Applied Digital Ecosystems

A primary motivation for our research in Digital Ecosystems is the desire to exploit the self-organising properties of biological ecosystems. Ecosystems are thought to be robust, scalable architectures that can automatically solve complex, dynamic problems. However, the biological processes that contribute to these properties have not been made explicit in Digital Ecosystems research. Here, we discuss how biological properties contribute to the self-organising features of biological ecosystems, including population dynamics, evolution, a complex dynamic environment, and spatial distributions for generating local interactions. The potential for exploiting these properties in artificial systems is then considered. We suggest that several key features of biological ecosystems have not been fully explored in existing digital ecosystems, and discuss how mimicking these features may assist in developing robust, scalable self-organising architectures. An example architecture, the Digital Ecosystem, is considered in detail. The Digital Ecosystem is then measured experimentally through simulations, with measures originating from theoretical ecology, to confirm its likeness to a biological ecosystem. Including the responsiveness to requests for applications from the user base, as a measure of the 'ecological succession' (development).Comment: 9 pages, 4 figure, conferenc

An Effective Multi-Population Based Hybrid Genetic Algorithm for Job Shop Scheduling Problem

The job shop scheduling problem is a well known practical planning problem in the manufacturing sector. We have considered the JSSP with an objective of minimizing makespan. In this paper, a multi-population based hybrid genetic algorithm is developed for solving the JSSP. The population is divided in several groups at first and the hybrid algorithm is applied to the disjoint groups. Then the migration operator is used. The proposed approach, MP-HGA, have been compared with other algorithms for job-shop scheduling and evaluated with satisfactory results on a set of JSSPs derived from classical job-shop scheduling benchmarks. We have solved 15 benchmark problems and compared results obtained with a number of algorithms established in the literature. The experimental results show that MP-HGA could gain the best known makespan in 13 out of 15 problems

An Effective Multi-Population Based Hybrid Genetic Algorithm for Job Shop Scheduling Problem

The job shop scheduling problem is a well known practical planning problem in the manufacturing sector. We have considered the JSSP with an objective of minimizing makespan. In this paper, a multi-population based hybrid genetic algorithm is developed for solving the JSSP. The population is divided in several groups at first and the hybrid algorithm is applied to the disjoint groups. Then the migration operator is used. The proposed approach, MP-HGA, have been compared with other algorithms for job-shop scheduling and evaluated with satisfactory results on a set of JSSPs derived from classical job-shop scheduling benchmarks. We have solved 15 benchmark problems and compared results obtained with a number of algorithms established in the literature. The experimental results show that MP-HGA could gain the best known makespan in 13 out of 15 problems

Practical application of penalty-free evolutionary multi-objective optimisation of water distribution systems

Evolutionary algorithms are a commonly applied optimisation approach in water distribution systems. However, the algorithms are time consuming when applied to large optimisation problems. The aim of this paper is to evaluate the application of a penalty-free multi-objective evolutionary optimisation algorithm to solve a real-world network design problem. The optimization model uses pressure-dependent analysis that accounts for the pressure dependency of the nodal flows and thus avoids the need for penalties to address violations of the nodal pressure constraints. The algorithm has been tested previously using benchmark optimisation problems in the literature. In all cases, the algorithm found improved solutions and/or the best solution reported previously in the literature with considerably fewer function evaluations. In this paper, a real-world network with over 250 pipes was considered. The network comprises multiple sources, multiple demand categories, many fire flows and involves extended period simulation. Due to the size and complexity of the optimization problem, a high performance computer that comprises multiple cores was used for the computational solution. Multiple optimisation runs were performed concurrently. Overall, the algorithm performs well; it consistently provides least cost solutions that satisfy the system requirements quickly. The least-cost design obtained was over 40% cheaper than the existing network in terms of the pipe costs

Использование модели акторов для реализации распределенных генетических алгоритмов

Досліджено можливості застосування моделі акторів як засобу проектування та аналізу розподілених програмних систем з високою завантаженістю. Основну увагу приділено використанню моделі акторів для реалізації паралельного розподіленого генетичного алгоритму. Здійснено огляд різноманітних моделей паралельних розподілених генетичних алгоритмів, окреслено їхні переваги та недоліки. Для концепції «господар-робітники» запропоновано адаптацію її синхронного та асинхронного варіантів до моделі акторів. Засобами фреймворку Akka створено розподілену систему – кластер акторів. У середовищі кластера описано розгортання застосунка, який демонструє використання пропонованої адаптації концепції «господар-робітники» для розв’язання задачі пошуку найкращої стратегії поведінки робота у штучному середовищі.The article presents an application of the actor model for the high load systems development and analysis. The main attention is dedicated to the usage of actors for an implementation of the distributed genetic algorithms. Different models of parallel distributed genetic algorithms, such as Master-Slave, coarse-grained, and fine-grained genetic algorithms, were investigated in regards to their strong and weak points. Synchronous and asynchronous variants of the Master-Slave approach were adapted to the actor model. With the power of Akka framework, a distributed system – cluster of actors – has been successfully created. Finally, the deployment into the cluster environment of a real program is described which demonstrates the usage of the proposed adaptation of Master-Slave approach for the task of finding robot’s best behavior strategy inside an artificial environment.Исследована возможность применения модели акторов в качестве средства проектирования и анализа высоконагруженных распределенных программных систем. Основное внимание уделено использованию модели актеров для реализации параллельного распределенного генетического алгоритма. Сделан обзор различных моделей параллельных распределеных генетических алгоритмов, очерчены их преимущества и недостатки. Для концепции «хозяин-рабочие» предложено применение ее синхронного и асинхронного вариантов к модели актеров. Средствами фреймворка Akka создана распределенная система – кластер актеров. В среде кластера описано развертывание приминения, которое демонстрирует использование предложенной адаптации концепции «хозяин-работкик» для решения задачи поиска наилучшей стратегии поведения робота в искусственной среде

An evolutive approach for the delineation of local labour markets

This paper presents a new approach to the delineation of local labour markets based on evolutionary computation. The main objective is the regionalisation of a given territory into functional regions based on commuting flows. According to the relevant literature, such regions are defined so that (a) their boundaries are rarely crossed in daily journeys to work, and (b) a high degree of intra-area movement exists. This proposal merges municipalities into functional regions by maximizing a fitness function that measures aggregate intra-region interaction under constraints of inter-region separation and minimum size. Real results are presented based on the latest database from the Census of Population in the Region of Valencia. Comparison between the results obtained through the official method which currently is most widely used (that of British Travel-to-Work Areas) and those from our approach is also presented, showing important improvements in terms of both the number of different market areas identified that meet the statistical criteria and the degree of aggregate intra-market interaction.José M. Casado-Díaz has received financial support from the Spanish Department of Education and Science (ref. BEC2003-02391) through a program partly funded by the European Regional Development Fund (ERDF). Lucas Martínez-Bernabeu acknowledges financial support from the Spanish Dept. of Education and Science, the European Social Fund (ESF) and the University of Alicante

A parallel evolutionary algorithm applied to the minimum interference frequency assignment problem

This article presents the application of a Parallel Evolutionary Algorithm to solve the Minimum Interference Frequency Assignment Problem (MI-FAP). This is a capital problem in the mobile telecommunication field, which proposes to find an assignation of a set of frequencies to minimize the communication interference. MI-FAP is a NP-Complete optimization problem; so traditional exact algorithms are useless for solving real-life problem instances in reasonable execution times. This work proposes to use a metaheuristic approach to find good quality solutions for real-life MIFAP instances never faced before using Evolutionary Algorithms. Evaluation experiments performed on those real-life instances report promising numerical results for both serial and parallel models of the algorithm proposed. In addition, the parallel version shows high levels of computational efficiency, demonstrating a superlinear speedup behavior for the instances studiedVII Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI