Using Stigmergy to Solve Numerical Optimization Problems

The current methodology for designing highly efficient technological systems needs to choose the best combination of the parameters that affect the performance. In this paper we propose a promising optimization algorithm, referred to as the Multilevel Ant Stigmergy Algorithm (MASA), which exploits stigmergy in order to optimize multi-parameter functions. We evaluate the performance of the MASA and Differential Evolution -- one of the leading stochastic method for numerical optimization -- in terms of their applicability as numerical optimization techniques. The comparison is performed using several widely used benchmark functions with added noise

Beyond Dataflow

This paper presents some recent advanced dataflow architectures. While the dataflow concept offers the potential of high performance, the performance of an actual dataflow implementation can be restricted by a limited number of functional units, limited memory bandwidth, and the need to associatively match pending operations with available functional units. Since the early 1970s, there have been significant developments in both fundamental research and practical realizations of dataflow models of computation. In particular, there has been active research and development in multithreaded architectures that evolved from the dataflow model. Also some other techniques for combining control-flow and dataflow emerged, such as coarse-grain dataflow, dataflow with complex machine operations, RISC dataflow, and micro dataflow. These developments have also had certain impact on the conception of highperformance superscalar processors in the “post-RISC” era

Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis

<p>Abstract</p> <p>Background</p> <p>We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods.</p> <p>Results</p> <p>We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input.</p> <p>Conclusions</p> <p>Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These results hold for both real and artificial data, for all observability scenarios considered, and for all amounts of noise added to the artificial data. In sum, the meta-heuristic methods considered are suitable for estimating the parameters in the ODE model of the dynamics of endocytosis under a range of conditions: With the model and conditions being representative of parameter estimation tasks in ODE models of biochemical systems, our results clearly highlight the promise of bio-inspired meta-heuristic methods for parameter estimation in dynamic system models within system biology.</p

Program Partitioning for a Control/Data Driven Computer

The paper examines the problem of dataflow graph partitioning aiming to improve the efficiency of macro-dataflow computing on a hybrid control/data driven architecture. The partitioning consists of dataflow graph synchronization and scheduling of the synchronous graph. A new scheduling algorithm, called Global Arc Minimization (GAM), is introduced. The performance of the GAM algorithm is evaluated relative to some other known heuristic methods for static scheduling. When interprocessor communication delays are taken into account, the GAM algorithm achieves better performance on the simulated hybrid architecture

Evolutionary Synthesis Algorithm - Genetic Operators Tuning

Abstract:- This paper presents the evaluation and fine-tuning of different values of genetic operators in the process of optimizing the designs of the integrated circuits. Due to the increasing usage of the evolutionary optimization in the area of the integrated circuit design, there is a need to find a proper combination of genetic operators parameters ’ value. We investigated the interdependence of various values of these parameters and their influence on the quality of the final solution. Generally, the quality of solution is influenced by parameters and the input design. Therefore, it is important to perform this kind of evaluation each time we are searching the optimal values of the genetic operators for some new problem to be solved. Key-Words:- evolutionary, scheduling, allocation, genetic operators, tuning

A comparison of models for forecasting the residential natural gas demand of an urban area

Forecasting the residential natural gas demand for large groups of buildings is extremely important for efficient logistics in the energy sector. In this paper different forecast models for residential natural gas demand of an urban area were implemented and compared. The models forecast gas demand with hourly resolution up to 60 h into the future. The model forecasts are based on past temperatures, forecasted temperatures and time variables, which include markers for holidays and other occasional events. The models were trained and tested on gas-consumption data gathered in the city of Ljubljana, Slovenia. Machine-learning models were considered, such as linear regression, kernel machine and artificial neural network. Additionally, empirical models were developed based on data analysis. Two most accurate models were found to be recurrent neural network and linear regression model. In realistic setting such trained models can be used in conjunction with a weather-forecasting service to generate forecasts for future gas demand