A genetic algorithm for the design of a fuzzy controller for active queue management

Active queue management (AQM) policies are those policies of router queue management that allow for the detection of network congestion, the notification of such occurrences to the hosts on the network borders, and the adoption of a suitable control policy. This paper proposes the adoption of a fuzzy proportional integral (FPI) controller as an active queue manager for Internet routers. The analytical design of the proposed FPI controller is carried out in analogy with a proportional integral (PI) controller, which recently has been proposed for AQM. A genetic algorithm is proposed for tuning of the FPI controller parameters with respect to optimal disturbance rejection. In the paper the FPI controller design metodology is described and the results of the comparison with random early detection (RED), tail drop, and PI controller are presented

On the evolutionary optimisation of many conflicting objectives

This inquiry explores the effectiveness of a class of modern evolutionary algorithms, represented by Non-dominated Sorting Genetic Algorithm (NSGA) components, for solving optimisation tasks with many conflicting objectives. Optimiser behaviour is assessed for a grid of mutation and recombination operator configurations. Performance maps are obtained for the dual aims of proximity to, and distribution across, the optimal trade-off surface. Performance sweet-spots for both variation operators are observed to contract as the number of objectives is increased. Classical settings for recombination are shown to be suitable for small numbers of objectives but correspond to very poor performance for higher numbers of objectives, even when large population sizes are used. Explanations for this behaviour are offered via the concepts of dominance resistance and active diversity promotion

A multi-objective genetic algorithm for the design of pressure swing adsorption

Pressure Swing Adsorption (PSA) is a cyclic separation process, more advantageous over other separation options for middle scale processes. Automated tools for the design of PSA processes would be beneficial for the development of the technology, but their development is a difficult task due to the complexity of the simulation of PSA cycles and the computational effort needed to detect the performance at cyclic steady state. We present a preliminary investigation of the performance of a custom multi-objective genetic algorithm (MOGA) for the optimisation of a fast cycle PSA operation, the separation of air for N2 production. The simulation requires a detailed diffusion model, which involves coupled nonlinear partial differential and algebraic equations (PDAEs). The efficiency of MOGA to handle this complex problem has been assessed by comparison with direct search methods. An analysis of the effect of MOGA parameters on the performance is also presented

Study of genetic algorithm for optimization problems

Dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáThis work consists in to explore the Genetic Algorithms to solve non-linear optimization problems. The aim of this work is to study and develop strategies in order to improve the performance of the Genetic Algorithm that can be applied to solve several optimization problems, as time schedule, costs minimization, among others. For this, the behavior of a traditional Genetic Algorithm was observed and the acquired information was used to propose variations of this algorithm. Thereby, a new approach for the selection operator was proposed, considering the abilities of population individuals to generate offspring. In addition, a Genetic Algorithm that uses dynamic operators rates, controlled by the amplitude and the standard deviation of the population, is also proposed. Together with this algorithm, a new stopping criterion is also proposed. This criterion uses population and the problem information to identify the stopping point. The strategies proposed are validated by twelve benchmark optimization functions, defined in the literature for testing optimization algorithms. The dynamic rate algorithm results were compared with a fixed rate Genetic Algorithm and with the defaultMatlab Genetic Algorithm, and in both cases, the proposed algorithm presented excellent results, for all considered functions, which demonstrates the robustness of the algorithm for solving several optimization problems.Este trabalho consiste em explorar o Algoritmo Genético para resolução de problemas de otimização não-linear. O objetivo deste trabalho é estudar e desenvolver estratégias para melhorar o desempenho do Algoritmo Genético que possa ser aplicado para resolução de problemas de otimização variados, como escalonamento de horários, minimização de custos, entre outros. Para isso, foi observado o comportamento usual do Algoritmo Genético e as informações adquiridas foram usadas para propor variações deste algoritmo. Assim, uma nova abordagem para o operador de seleção é proposta, considerando a habilidade dos indivíduos da população em gerar descendentes. Além disso, também é proposto um Algoritmo Genético que utiliza taxas dinâmicas nos operadores, controladas pela amplitude e desvio padrão da população. Juntamente com este algoritmo, um novo critério de paragem também é proposto. Este critério utiliza informações da população e do problema de otimização para determinar o local de paragem. As estratégias propostas são validadas por doze funções de teste, definidas na literatura para teste de algoritmos de otimização. Os resultados do algoritmo de taxas dinâmicas foram comparados com um Algoritmo Genético de taxas fixas e com o Algoritmo Genético padrão disponível no Matlab, e em ambos os casos o algoritmo proposto apresentou excelentes resultados, para todas as funções consideradas, o que demonstra a robustez do método para resolução de problemas de otimização variados

Fuzzy-logic controlled genetic algorithm for the rail-freight crew-scheduling problem

AbstractThis article presents a fuzzy-logic controlled genetic algorithm designed for the solution of the crew-scheduling problem in the rail-freight industry. This problem refers to the assignment of train drivers to a number of train trips in accordance with complex industrial and governmental regulations. In practice, it is a challenging task due to the massive quantity of train trips, large geographical span and significant number of restrictions. While genetic algorithms are capable of handling large data sets, they are prone to stalled evolution and premature convergence on a local optimum, thereby obstructing further search. In order to tackle these problems, the proposed genetic algorithm contains an embedded fuzzy-logic controller that adjusts the mutation and crossover probabilities in accordance with the genetic algorithm’s performance. The computational results demonstrate a 10% reduction in the cost of the schedule generated by this hybrid technique when compared with a genetic algorithm with fixed crossover and mutation rates