Multi-agent system for dynamic manufacturing system optimization

This paper deals with the application of multi-agent system concept for optimization of dynamic uncertain process. These problems are known to have a computationally demanding objective function, which could turn to be infeasible when large problems are considered. Therefore, fast approximations to the objective function are required. This paper employs bundle of intelligent systems algorithms tied together in a multi-agent system. In order to demonstrate the system, a metal reheat furnace scheduling problem is adopted for highly demanded optimization problem. The proposed multi-agent approach has been evaluated for different settings of the reheat furnace scheduling problem. Particle Swarm Optimization, Genetic Algorithm with different classic and advanced versions: GA with chromosome differentiation, Age GA, and Sexual GA, and finally a Mimetic GA, which is based on combining the GA as a global optimizer and the PSO as a local optimizer. Experimentation has been performed to validate the multi-agent system on the reheat furnace scheduling problem

Using intelligent optimization methods to improve the group method of data handling in time series prediction

In this paper we show how the performance of the basic algorithm of the Group Method of Data Handling (GMDH) can be improved using Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The new improved GMDH is then used to predict currency exchange rates: the US Dollar to the Euros. The performance of the hybrid GMDHs are compared with that of the conventional GMDH. Two performance measures, the root mean squared error and the mean absolute percentage errors show that the hybrid GMDH algorithm gives more accurate predictions than the conventional GMDH algorithm

Modeling the Input Variables and Setting on the Static System Model at Using the Genetic Algorithm for Fault Location in the Power Transmission Grid

Part 14: Energy DistributionInternational audienceIn the paper is presented a method for fault location in the power grid through waveform matching of the recorded wave from failure with simulation from the static system model wave failure. The basis of the approach is comparing of the phase of the waves. The search process to find the best waveform match is actually an optimization problem. The genetic algorithm is used to find the optimal solution. The proposed method is suitable in cases where data from digital fault recorders are scarce. In these circumstances, the proposed approach provides more accurate results compared to the other known techniques. But for the correct operation of this method for fault locating in the system exercise influence both the form of the acquired form from digital fault recorders input data thus the correlation between the power transmission system and the static system model. Namely these issues are the subject of this paper

A directed mutation operator for real coded genetic algorithms

Copyright @ Springer-Verlag Berlin Heidelberg 2010.Developing directed mutation methods has been an interesting research topic to improve the performance of genetic algorithms (GAs) for function optimization. This paper introduces a directed mutation (DM) operator for GAs to explore promising areas in the search space. In this DM method, the statistics information regarding the fitness and distribution of individuals over intervals of each dimension is calculated according to the current population and is used to guide the mutation of an individual toward the neighboring interval that has the best statistics result in each dimension. Experiments are carried out to compare the proposed DM technique with an existing directed variation on a set of benchmark test problems. The experimental results show that the proposed DM operator achieves a better performance than the directed variation on most test problems

Adaptive intelligence applied to numerical optimisation

The article presents modification strategies theoretical comparison and experimental results achieved by adaptive heuristics applied to numerical optimisation of several non-constraint test functions. The aims of the study are to identify and compare how adaptive search heuristics behave within heterogeneous search space without retuning of the search parameters. The achieved results are summarised and analysed, which could be used for comparison to other methods and further investigation

Discovering predictive variables when evolving cognitive models

A non-dominated sorting genetic algorithm is used to evolve models of learning from different theories for multiple tasks. Correlation analysis is performed to identify parameters which affect performance on specific tasks; these are the predictive variables. Mutation is biased so that changes to parameter values tend to preserve values within the population's current range. Experimental results show that optimal models are evolved, and also that uncovering predictive variables is beneficial in improving the rate of convergence

Extending the class of order-k delineable problems for the gene expression messy genetic algorithm

This paper revisits the recently introduced gene expression messy genetic algorithm (GEMGA) and offers some modifications to the extend the class of order-k delineable problems (class of problems that can be solved using a bounded order of relations) in GEMGA. The fundamental components that control the delineability of relations are reviewed in the light of the recently proposed SEARCH framework. Modified class and relation comparison statistics of GEMGA are proposed. The sample complexity of this improved version of GEMGA is shown to be subquadratic. Theoretical conclusions are also substantiated by experimental results for large, multimodal order-k delineable problems with respect to class average comparison statistic. We also present results for the recently constructed Goldberg-Lewei test functions

Evolution of Cache Replacement Policies to Track Heavy-hitter Flows

Several important network applications cannot easily scale to higher data rates without requiring focusing just on the large traffic flows. Recent works have discussed algorithmic solutions that trade-off accuracy to gain efficiency for filtering and tracking the so-called "heavy-hitters". However, a major limit is that flows must initially go through a filtering process, making it impossible to track state associated with the first few packets of the flow. In this paper, we propose a different paradigm in tracking the large flows which overcomes this limit. We view the problem as that of managing a small flow cache with a finely tuned replacement policy that strives to avoid evicting the heavy-hitters. Our scheme starts from recorded traffic traces and uses Genetic Algorithms to evolve a replacement policy tailored for supporting seamless, stateful traffic-processing. We evaluate our scheme in terms of missed heavy-hitters: it performs close to the optimal, oracle-based policy, and when compared to other standard policies, it consistently outperforms them, even by a factor of two in most cases. © 2011 Springer-Verlag

Integrating computer log files for process mining: a genetic algorithm inspired technique

Process mining techniques are applied to single computer log files. But many processes are supported by different software tools and are by consequence recorded into multiple log files. Therefore it would be interesting to find a way to automatically combine such a set of log files for one process. In this paper we describe a technique for merging log files based on a genetic algorithm. We show with a generated test case that this technique works and we give an extended overview of which research is needed to optimise and validate this technique