A two-level genetic algorithm for the multi-mode resource-constrained project scheduling problem

This paper presents a genetic algorithm for the multimode resource-constrained project scheduling problem (MRCPSP), in which multiple execution modes are available for each of the activities of the project. The objective function is the minimization of the construction project completion time. To solve the problem, is applied a two-level genetic algorithm, which makes use of two separate levels and extend the parameterized schedule generation scheme by introducing an improvement procedure. It is evaluated the quality of the schedule and present detailed comparative computational results for the MRCPSP, which reveal that this approach is a competitive algorithm.

Crossing genetic and swarm intelligence algorithms to generate logic circuits

Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetic. The basic concept of GAs is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the fittest. On the other hand, Particle swarm optimization (PSO) is a population based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques such as GAs. The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. PSO is attractive because there are few parameters to adjust. This paper presents hybridization between a GA algorithm and a PSO algorithm (crossing the two algorithms). The resulting algorithm is applied to the synthesis of combinational logic circuits. With this combination is possible to take advantage of the best features of each particular algorithm.

Implementation of fractional-order electromagnetic potential through a genetic algorithm

Several phenomena present in electrical systems motivated the development of comprehensive models based on the theory of fractional calculus (FC). Bearing these ideas in mind, in this work are applied the FC concepts to define, and to evaluate, the electrical potential of fractional order, based in a genetic algorithm optimization scheme. The feasibility and the convergence of the proposed method are evaluated.

Calculation of fractional derivatives of noisy data with genetic algorithms

This paper addresses the calculation of derivatives of fractional order for non-smooth data. The noise is avoided by adopting an optimization formulation using genetic algorithms (GA). Given the flexibility of the evolutionary schemes, a hierarchical GA composed by a series of two GAs, each one with a distinct fitness function, is established.

A random key based genetic algorithm for the resource constrained project scheduling problem

This paper presents a genetic algorithm for the Resource Constrained Project Scheduling Problem (RCPSP). The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities of the activities are defined by the genetic algorithm. The heuristic generates parameterized active schedules. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

Simulação de um Pool Simétrico com validação técnica do despacho provisório e apresentação de medidas corretivas baseadas num despacho ótimo

Os mercados de energia elétrica são atualmente uma realidade um pouco por todo o mundo. Contudo, não é consensual o modelo regulatório a utilizar, o que origina a utilização de diferentes modelos nos diversos países que deram início ao processo de liberalização e de reestruturação do sector elétrico. A esses países, dado que a energia elétrica não é um bem armazenável, pelo menos em grandes quantidades, colocam-se questões importantes relacionadas com a gestão propriamente dita do seu sistema elétrico. Essas questões implicam a adoção de regras impostas pelo regulador que permitam ultrapassar essas questões. Este trabalho apresenta um estudo feito aos mercados de energia elétrica existentes um pouco por todo o mundo e que o autor considerou serem os mais importantes. Foi também feito um estudo de ferramentas de otimização essencialmente baseado em meta-heurísticas aplicadas a problemas relacionados com a operação dos mercados e com os sistemas elétricos de energia, como é o exemplo da resolução do problema do Despacho Económico. Foi desenvolvida uma aplicação que simula o funcionamento de um mercado que atua com o modelo Pool Simétrico, em que são transmitidas as ofertas de venda e compra de energia elétrica por parte dos produtores, por um lado, e dos comercializadores, consumidores elegíveis ou intermediários financeiros, por outro, analisando a viabilidade técnica do Despacho Provisório. A análise da viabilidade técnica do Despacho Provisório é verificada através do modelo DC de trânsito de potências. No caso da inviabilidade do Despacho Provisório, por violação de restrições afetas ao problema, são determinadas medidas corretivas a esse despacho, com base nas ofertas realizadas e recorrendo a um Despacho Ótimo. Para a determinação do Despacho Ótimo recorreu-se à meta-heurística Algoritmos Genéticos. A aplicação foi desenvolvida no software MATLAB utilizando a ferramenta Graphical User Interfaces. A rede de teste utilizada foi a rede de 14 barramentos do Institute of Electrical and Electronics Engineers (IEEE). A aplicação mostra-se competente no que concerne à simulação de um mercado com tipo de funcionamento Pool Simétrico onde são efetuadas ofertas simples e onde as transações ocorrem no mercado diário, porém, não reflete o problema real relacionado a este tipo de mercados. Trata-se, portanto, de um simulador básico de um mercado de energia cujo modelo de funcionamento se baseia no tipo Pool Simétrico.

Trajectory planning of redundant manipulators using genetic algorithms

The trajectory planning of redundant robots is an important area of research and efficient optimization algorithms are needed. This paper presents a new technique that combines the closed-loop pseudoinverse method with genetic algorithms. The results are compared with a genetic algorithm that adopts the direct kinematics. In both cases the trajectory planning is formulated as an optimization problem with constraints.

Dynamical modelling of a genetic algorithm

This work addresses the signal propagation and the fractional-order dynamics during the evolution of a genetic algorithm (GA). In order to investigate the phenomena involved in the GA population evolution, the mutation is exposed to excitation perturbations during some generations and the corresponding fitness variations are evaluated. Three distinct fitness functions are used to study their influence in the GA dynamics. The input and output signals are studied revealing a fractional-order dynamic evolution, characteristic of a long-term system memory.

Optimal fuzzy PID control tuned with genetic algorithms

Fuzzy logic controllers (FLC) are intelligent systems, based on heuristic knowledge, that have been largely applied in numerous areas of everyday life. They can be used to describe a linear or nonlinear system and are suitable when a real system is not known or too difficult to find their model. FLC provide a formal methodology for representing, manipulating and implementing a human heuristic knowledge on how to control a system. These controllers can be seen as artificial decision makers that operate in a closed-loop system, in real time. The main aim of this work was to develop a single optimal fuzzy controller, easily adaptable to a wide range of systems – simple to complex, linear to nonlinear – and able to control all these systems. Due to their efficiency in searching and finding optimal solution for high complexity problems, GAs were used to perform the FLC tuning by finding the best parameters to obtain the best responses. The work was performed using the MATLAB/SIMULINK software. This is a very useful tool that provides an easy way to test and analyse the FLC, the PID and the GAs in the same environment. Therefore, it was proposed a Fuzzy PID controller (FL-PID) type namely, the Fuzzy PD+I. For that, the controller was compared with the classical PID controller tuned with, the heuristic Ziegler-Nichols tuning method, the optimal Zhuang-Atherton tuning method and the GA method itself. The IAE, ISE, ITAE and ITSE criteria, used as the GA fitness functions, were applied to compare the controllers performance used in this work. Overall, and for most systems, the FL-PID results tuned with GAs were very satisfactory. Moreover, in some cases the results were substantially better than for the other PID controllers. The best system responses were obtained with the IAE and ITAE criteria used to tune the FL-PID and PID controllers.

Modelling a network of heterogeneous e-learning systems

In recent years emerged several initiatives promoted by educational organizations to adapt Service Oriented Architectures (SOA) to e-learning. These initiatives commonly named eLearning Frameworks share a common goal: to create flexible learning environments by integrating heterogeneous systems already available in many educational institutions. However, these frameworks were designed for integration of systems participating in business like processes rather than on complex pedagogical processes as those related to automatic evaluation. Consequently, their knowledge bases lack some fundamental components that are needed to model pedagogical processes. The objective of the research described in this paper is to study the applicability of eLearning frameworks for modelling a network of heterogeneous eLearning systems, using the automatic evaluation of programming exercises as a case study. The paper surveys the existing eLearning frameworks to justify the selection of the e-Framework. This framework is described in detail and identified the necessary components missing from its knowledge base, more precisely, a service genre, expression and usage model for an evaluation service. The extensibility of the framework is tested with the definition of this service. A concrete model for evaluation of programming exercises is presented as a validation of the proposed approach.

Multi-agent system for distributed manufacturing scheduling with genetic algorithms and tabu search

Computerized scheduling methods and computerized scheduling systems according to exemplary embodiments. A computerized scheduling method may be stored in a memory and executed on one or more processors. The method may include defining a main multi-machine scheduling problem as a plurality of single machine scheduling problems; independently solving the plurality of single machine scheduling problems thereby calculating a plurality of near optimal single machine scheduling problem solutions; integrating the plurality of near optimal single machine scheduling problem solutions into a main multi-machine scheduling problem solution; and outputting the main multi-machine scheduling problem solution.

Exciting dynamical behavior in a network of two coupled rings of Chen oscillators

We study exotic patterns appearing in a network of coupled Chen oscillators. Namely, we consider a network of two rings coupled through a “buffer” cell, with Z3×Z5 symmetry group. Numerical simulations of the network reveal steady states, rotating waves in one ring and quasiperiodic behavior in the other, and chaotic states in the two rings, to name a few. The different patterns seem to arise through a sequence of Hopf bifurcations, period-doubling, and halving-period bifurcations. The network architecture seems to explain certain observed features, such as equilibria and the rotating waves, whereas the properties of the chaotic oscillator may explain others, such as the quasiperiodic and chaotic states. We use XPPAUT and MATLAB to compute numerically the relevant states.

Influence of Soil Chemistry and Plant Physiology in the Phytoremediation of Cu, Mn, and Zn

Different anthropogenic sources of metals can result from agricultural, industrial, military, mining and urban activities that contribute to environmental pollution. Plants can be grown for phytoremediation to remove or stabilize contaminants in water and soil. Copper (Cu), manganese (Mn) and zinc (Zn) are trace essential metals for plants, although their role in homeostasis in plants must be strictly regulated to avoid toxicity. In this review, we summarize the processes involved in the bioavailability, uptake, transport and storage of Cu, Mn and Zn in plants. The efficiency of phytoremediation depends on several factors including metal bioavailability and plant uptake, translocation and tolerance mechanisms. Soil parameters, such as clay fraction, organic matter content, oxidation state, pH, redox potential, aeration, and the presence of specific organisms, play fundamental roles in the uptake of trace essential metals. Key processes in the metal homeostasis network in plants have been identified. Membrane transporters involved in the acquisition, transport and storage of trace essential metals are reviewed. Recent advances in understanding the biochemical and molecular mechanisms of Cu, Mn and Zn hyperaccumulation are described. The use of plant-bacteria associations, plant-fungi associations and genetic engineering has opened a new range of opportunities to improve the efficiency of phytoremediation. The main directions for future research are proposed from the investigation of published results.

Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.

Demand Response Design and Use Based on Network Locational Marginal Prices

Power systems have been experiencing huge changes mainly due to the substantial increase of distributed generation (DG) and the operation in competitive environments. Virtual Power Players (VPP) can aggregate several players, namely a diversity of energy resources, including distributed generation (DG) based on several technologies, electric storage systems (ESS) and demand response (DR). Energy resources management gains an increasing relevance in this competitive context. This makes the DR use more interesting and flexible, giving place to a wide range of new opportunities. This paper proposes a methodology to support VPPs in the DR programs’ management, considering all the existing energy resources (generation and storage units) and the distribution network. The proposed method is based on locational marginal prices (LMP) values. The evaluation of the impact of using DR specific programs in the LMP values supports the manager decision concerning the DR use. The proposed method has been computationally implemented and its application is illustrated in this paper using a 33-bus network with intensive use of DG.