Multiobjective gas turbine engine controller design using genetic algorithms

This paper describes the use of multiobjective genetic algorithms (MOGAs) in the design of a multivariable control system for a gas turbine engine. The mechanisms employed to facilitate multiobjective search with the genetic algorithm are described with the aid of an example. It is shown that the MOGA confers a number of advantages over conventional multiobjective optimization methods by evolving a family of Pareto-optimal solutions rather than a single solution estimate. This allows the engineer to examine the trade-offs between the different design objectives and configurations during the course of an optimization. In addition, the paper demonstrates how the genetic algorithm can be used to search in both controller structure and parameter space thereby offering a potentially more general approach to optimization in controller design than traditional numerical methods. While the example in the paper deals with control system design, the approach described can be expected to be applicable to more general problems in the fields of computer aided design (CAD) and computer aided engineering (CAE

Efficient evolutionary algorithms for optimal control

If optimal control problems are solved by means of gradient based local search methods, convergence to local solutions is likely. Recently, there has been an increasing interest in the use of global optimisation algorithms to solve optimal control problems, which are expected to have local solutions. Evolutionary Algorithms (EAs) are global optimisation algorithms that have mainly been applied to solve static optimisation problems. Only rarely Evolutionary Algorithms have been used to solve optimal control problems. This may be due to the belief that their computational efficiency is insufficient to solve this type of problems. In addition, the application of Evolutionary Algorithms is a relatively young area of research. As demonstrated in this thesis, Evolutionary Algorithms exist which have significant advantages over other global optimisation methods for optimal control, while their efficiency is comparable.The purpose of this study was to investigate and search for efficient evolutionary algorithms to solve optimal control problems that are expected to have local solutions. These optimal control problems are called multi-modal. An important additional requirement for the practical application of these algorithms is that they preferably should not require any algorithm parameter tuning. Therefore algorithms with less algorithm parameters should be preferred. In addition guidelines for the choice of algorithm parameter values, and the possible development of automatic algorithm parameter adjustment strategies, are important issues.This study revealed that Differential Evolution (DE) algorithms are a class of evolutionary algorithms that do not share several theoretical and practical limitations that other Genetic Algorithms have. As a result they are significantly more efficient than other Genetic Algorithms, such as Breeder Genetic Algorithms (BGA), when applied to multi-modal optimal control problems. Their efficiency is comparable to the efficiency of Iterative Dynamic Programming (IDP), a global optimisation approach specifically designed for optimal control. Moreover the DE algorithms turned out to be significantly less sensitive to problems concerning the selection or tuning of algorithm parameters and the initialisation of the algorithm.Although it is not a DE algorithm, the GENOCOP algorithm is considered to be one of the most efficient genetic algorithms with real-valued individuals and specialized evolutionary operators. This algorithm was the starting point of our research. In Chapter 2 it was applied to some optimal control problems from chemical engineering. These problems were high dimensional, non-linear, multivariable, multi-modal and non-differentiable. Basically with GENOCOP the same solutions were obtained as with Iterative Dynamic Programming. Moreover GENOCOP is more successful in locating the global solution in comparison with other local optimisation algorithms. GENOCOP'S efficiency however is rather poor and the algorithm parameter tuning rather complicated. This motivated us to seek for more efficient evolutionary algorithms.Mathematical arguments found in the literature state that DE algorithms outperform other Evolutionary Algorithms in terms of computational efficiency. Therefore in Chapter 3, DE algorithms, generally used to solve continuous parameter optimisation problems, were used to solve two multi-modal (benchmark) optimal control problems. Also some Breeder Genetic Algorithms (BGA) were applied to solve these problems. The results obtained with these algorithms were compared to one another, and to the results obtained with IDP. The comparison confirmed that DE algorithms stand out in terms of efficiency as compared to the Breeder Genetic algorithms. Moreover, in contrast to the majority of Evolutionary Algorithms, which have many algorithm parameters that need to be selected or tuned, DE has only three algorithm parameters that have to be selected or tuned. These are the population size (µ), the crossover constant (CR) and the differential variation amplification (F). The population size plays a crucial role in solving multi-modal optimal control problems. Selecting a smaller population size enhances the computational efficiency but reduces the probability of finding the global solution. During our investigations we tried to find the best trade-off. One of the most efficient DE algorithms is denoted by DE/best/2/bin . All the investigated DE algorithms solved the two benchmark multi-modal optimal control problems properly and efficiently. The computational efficiency achieved by the DE algorithms in solving the first low multi-modal problem, was comparable to that of IDP. When applied to the second highly multi-modal problem, the computational efficiency of DE was slightly inferior to the one of IDP, after tuning of the algorithm parameters. However, the selection or tuning of the algorithm parameters for IDP is more difficult and more involved.From our investigation the following guidelines were obtained for the selection of the DE algorithm parameters. Take the population size less than or equal to two times the number of variables to be optimised that result from the control parameterisation of the original optimal control problem ( µ ≤ 2n u ). Highly multi-modal optimal control problems require a large value of the differential variation amplification ( F ≥0.9) and a very small or zero value for the crossover constant (0≤ CR ≤0.2). Low multi-modal optimal control problems need a medium value for the differential variation amplification (0.4≤ CR ≤0.6) and a large or medium value for the crossover constant (0.2≤ CR ≤0.5). In contrast to IDP, finding near-optimal values for the algorithm parameters is very simple for DE algorithms.Generally, the DE algorithm parameters are kept constant during the optimization process. A more effective and efficient algorithm may be obtained if they are adjusted on-line. In Chapter 4, a strategy that on-line adjusts the differential variation amplification ( F ) and the crossover constant ( CR ) using a measure of the diversity of the individuals in the population, was proposed. Roughly, the proposed strategy takes large values for F and small values for CR at the beginning of the optimization in order to promote a global search. When the population approaches the solution, F is decreased in order to promote a local search, and the crossover parameter CR is enlarged to increase the speed of convergence. When implemented on the DE algorithm DE/rand/1/bin and applied to the two benchmark multi-modal optimal control problems, the computational efficiency significantly improved and also the probability of locating the global solution.To judge the opportunities and advantages of using Evolutionary Algorithms to solve problems related to optimal control, in Chapter 5 several engineering applications concerning optimal greenhouse cultivation control are considered. In Chapter 5.1 genetic algorithms with binary individuals (Simple Genetic Algorithm) and floating-point representation (GENOCOP) for the individuals are used to estimate some of the parameters of a two-state dynamic model of a lettuce crop, the so-called NICOLET model. This model is intended to predict dry weight and nitrate content of lettuce at harvest time. Parameter estimation problems usually suffer from local minima. This study showed that Evolutionary Algorithms are suitable to calibrate the parameters of a dynamic model. However the required computation time is significant. Partly this is due to the high computational load of a single objective function evaluation, which for parameter optimisation problems involves a system simulation. Even though parameter optimisation is very often performed off-line, thus making computation time perhaps less important, more efficient evolutionary algorithms like DE are to be preferred.In Chapter 5.2 an optimal control problem of nitrate concentration in a lettuce crop was solved by means of two different algorithms. The ACW (Adjustable Control-variation Weight) gradient algorithm, which searches for local solutions, and the DE algorithm DE/best/2/bin that searches for a global solution. The dynamic system is a modified two-state dynamic model of a lettuce crop (NICOLET B3) and the control problem has a fixed final time and control and terminal state constraints. The DE algorithm was extended in order to deal with this.The results showed that this problem probably does not have local solutions and that the control parameterisation required by the DE algorithm causes some difficulties in accurately approximating the continuous solution obtained by the ACW algorithm. On the other hand the computational efficiency of the evolutionary algorithm turned out to be impressive. An almost natural conclusion therefore is to combine a DE algorithm with a gradient algorithm.In Chapter 5.3 the combination of a DE algorithm and a first order gradient algorithm is used to solve an optimal control problem. The DE algorithm is used to approximate the global solution sufficiently close after which the gradient algorithm can converge to it efficiently. This approach was successfully tried on the optimal control of nitrate in lettuce, which unfortunately in this case, seems to have no local solutions. Still the feasibility of this approach, which is important for all types of optimal control problems of which it is unknown a-priori whether they have local solutions, was clearly demonstrated.Finally, in Chapter six this thesis ends with an overall discussion, conclusions and suggestions for future research

Design of power system stabilizers using evolutionary algorithms

Includes synopsis.Includes bibliographical references (leaves 151-159).Includes bibliographical references (leaves 125-134).Over the past decades, the issue of low frequency oscillations has been of major concern to power system engineers. These oscillations range from 0.1 to 3Hz and tend to be poorly damped especially in systems equipped with high gain fast acting AVRs and highly interconnected networks. If these oscillations are not adequately damped, they may sustain and grow, which may lead to system separation and loss of power transfer

Incremental evolution strategy for function optimization

This paper presents a novel evolutionary approach for function optimization Incremental Evolution Strategy (IES). Two strategies are proposed. One is to evolve the input variables incrementally. The whole evolution consists of several phases and one more variable is focused in each phase. The number of phases is equal to the number of variables in maximum. Each phase is composed of two stages: in the single-variable evolution (SVE) stage, evolution is taken on one independent variable in a series of cutting planes; in the multi-variable evolving (MVE) stage, the initial population is formed by integrating the populations obtained by the SVE and the MVE in the last phase. And the evolution is taken on the incremented variable set. The other strategy is a hybrid of particle swarm optimization (PSO) and evolution strategy (ES). PSO is applied to adjust the cutting planes/hyper-planes (in SVEs/MVEs) while (1+1)-ES is applied to searching optima in the cutting planes/hyper-planes. The results of experiments show that the performance of IES is generally better than that of three other evolutionary algorithms, improved normal GA, PSO and SADE_CERAF, in the sense that IES finds solutions closer to the true optima and with more optimal objective values

Pre-Harvest Factors Optimization Using Genetic Algorithm for Lettuce

The agricultural sector is facing problems on crop development due to climate change and global warming. Crops such as rice, tomato, corn, lettuce, potato, wheat, soybeans and others are affected. Through analyzing the graphical representation of data, no optimum values are observed. In this study, the suitability of the genetic algorithm in finding the best condition for producing high quality lettuce crop was determined. The parameters that were optimized are the light intensity, temperature and CO2. These parameters were essential preharvest factors for lettuce. The system selected the 50 fittest individuals based on the fitness score and then proceeds to the recombination process. A mutation has been applied to test if the solution is the global one. When the iterations had reached the required number of generation, the system stopped and gave the best condition for lettuce. Critical design on GA was done and the best fitness plot was obtained. The GA results showed that the optimum conditions for a highquality lettuce crop needs a light intensity of 175.22296 μmol/m2/s, a temperature of 19.36228 ºC and a CO2 level of 803.01855 ppm

Reducing the Computational Effort Associated with Evolutionary Optimisation in Single Component Design

The dissertation presents innovative Evolutionary Search (ES) methods for the reduction in computational expense associated with the optimisation of highly dimensional design spaces. The objective is to develop a semi-automated system which successfully negotiates complex search spaces. Such a system would be highly desirable to a human designer by providing optimised design solutions in realistic time. The design domain represents a real-world industrial problem concerning the optimal material distribution on the underside of a flat roof tile with varying load and support conditions. The designs utilise a large number of design variables (circa 400). Due to the high computational expense associated with analysis such as finite element for detailed evaluation, in order to produce "good" design solutions within an acceptable period of time, the number of calls to the evaluation model must be kept to a minimum. The objective therefore is to minimise the number of calls required to the analysis tool whilst also achieving an optimal design solution. To minimise the number of model evaluations for detailed shape optimisation several evolutionary algorithms are investigated. The better performing algorithms are combined with multi-level search techniques which have been developed to further reduce the number of evaluations and improve quality of design solutions. Multi-level techniques utilise a number of levels of design representation. The solutions of the coarse representations are injected into the more detailed designs for fine grained refinement. The techniques developed include Dynamic Shape Refinement (DSR), Modified Injection Island Genetic Algorithm (MiiGA) and Dynamic Injection Island Genetic Algorithm (DiiGA). The multi-level techniques are able to handle large numbers of design variables (i.e. > 100). Based on the performance characteristics of the individual algorithms and multi-level search techniques, distributed search techniques are proposed. These techniques utilise different evolutionary strategies in a multi-level environment and were developed as a way of further reducing computational expense and improve design solutions. The results indicate a considerable potential for a significant reduction in the number of evaluation calls during evolutionary search. In general this allows a more efficient integration with computationally intensive analytical techniques during detailed design and contribute significantly to those preliminary stages of the design process where a greater degree of analysis is required to validate results from more simplistic preliminary design models

CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features

In this paper we propose a crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions. The operator is based on the theoretical distribution of the values of the genes of the best individuals in the population. The proposed operator takes into account the localization and dispersion features of the best individuals of the population with the objective that these features would be inherited by the offspring. Our aim is the optimization of the balance between exploration and exploitation in the search process. In order to test the efficiency and robustness of this crossover, we have used a set of functions to be optimized with regard to different criteria, such as, multimodality, separability, regularity and epistasis. With this set of functions we can extract conclusions in function of the problem at hand. We analyze the results using ANOVA and multiple comparison statistical tests. As an example of how our crossover can be used to solve artificial intelligence problems, we have applied the proposed model to the problem of obtaining the weight of each network in a ensemble of neural networks. The results obtained are above the performance of standard methods

Evolutionary multi-objective decision support systems for conceptual design

Merged with duplicate record 10026.1/2328 on 07.20.2017 by CS (TIS)In this thesis the problem of conceptual engineering design and the possible use of adaptive search techniques and other machine based methods therein are explored. For the multi-objective optimisation (MOO) within conceptual design problem, genetic algorithms (GA) adapted to MOO are used and various techniques explored: weighted sums, lexicographic order, Pareto method with and without ranking, VEGA-like approaches etc. Large number of runs are performed for findingZ Dth e optimal configuration and setting of the GA parameters. A novel method, weighted Pareto method is introduced and applied to a real-world optimisation problem. Decision support methods within conceptual engineering design framework are discussed and a new preference method developed. The preference method for translating vague qualitative categories (such as "more important 91 , 4m.9u ch less important' 'etc. ) into quantitative values (numbers) is based on fuzzy preferences and graph theory methods. Several applications of preferences are presented and discussed: * in weighted sum based optimisation methods; s in weighted Pareto method; * for ordering and manipulating constraints and scenarios; e for a co-evolutionary, distributive GA-based MOO method; The issue of complexity and sensitivity is addressed as well as potential generalisations of presented preference methods. Interactive dynamical constraints in the form of design scenarios are introduced. These are based on a propositional logic and a fairly rich mathematical language. They can be added, deleted and modified on-line during the design session without need for recompiling the code. The use of machine-based agents in conceptual design process is investigated. They are classified into several different categories (e. g. interface agents, search agents, information agents). Several different categories of agents performing various specialised task are developed (mostly dealing with preferences, but also some filtering ones). They are integrated with the conceptual engineering design system to form a closed loop system that includes both computer and designer. All thesed ifferent aspectso f conceptuale ngineeringd esigna re applied within Plymouth Engineering Design Centre / British Aerospace conceptual airframe design project.British Aerospace Systems, Warto

Transmission function models of infinite population genetic algorithms

The so-called transmission function framework is described, and implementations of transmission function models are given for a broad range of genetic algorithms. These models describe GA's with a population of infinite size. An actual implementation of these models for a non-trivial problem involving deception is given, these models are traced, and the results are visualized by means of population flow diagrams. These diagrams show that cross-competition between different parts of the optimal solution takes place