Controller tuning by means of evolutionary multiobjective optimization: current trends and applications

Control engineering problems are generally multi-objective problems; meaning that there are several specifications and requirements that must be fulfilled. A traditional approach for calculating a solution with the desired trade-off is to define an optimisation statement. Multi-objective optimisation techniques deal with this problem from a particular perspective and search for a set of potentially preferable solutions; the designer may then analyse the trade-offs among them, and select the best solution according to his/her preferences. In this paper, this design procedure based on evolutionary multiobjective optimisation (EMO) is presented and significant applications on controller tuning are discussed. Throughout this paper it is noticeable that EMO research has been developing towards different optimisation statements, but these statements are not commonly used in controller tuning. Gaps between EMO research and EMO applications on controller tuning are therefore detected and suggested as potential trends for research.The first author is grateful for the hospitality and availability of the UTC at the University of Sheffield during his academic research stay at 2011; especially to Dr. P.J. Fleming for his valuable comments and insights in the development of this paper. This work was partially supported by Grant FPI-2010/19 and project PAID-2011/2732 from the Universitat Politecnica de Valencia and projects TIN2011-28082 and ENE2011-25900 from the Spanish Ministry of Economy and Competitiveness.Reynoso Meza, G.; Blasco Ferragud, FX.; Sanchís Saez, J.; Martínez Iranzo, MA. (2014). Controller tuning by means of evolutionary multiobjective optimization: current trends and applications. Control Engineering Practice. 28:58-73. https://doi.org/10.1016/j.conengprac.2014.03.003S58732

A Survey on Adaptation Strategies for Mutation and Crossover Rates of Differential Evolution Algorithm

Differential Evolution (DE), the well-known optimization algorithm, is a tool under the roof of Evolutionary Algorithms (EAs) for solving non-linear and non-differential optimization problems. DE has many qualities in its hand, which are attributing to its popularity. DE also is known for its simplicity in solving the given problem with few control parameters: the population size (NP), the mutation rate (F) and the crossover rate (Cr). To avoid the difficulty involved in setting of suitable values for NP, F and Cr many parameter adaptation strategies are proposed in the literature. This paper is to present the working principle of the parameter adaptation strategies of F and Cr. The adaptation strategies are categorized based on the logic used by the authors, and clear insights about all the categories are presented

Gasification for Practical Applications

Although there were many books and papers that deal with gasification, there has been only a few practical book explaining the technology in actual application and the market situation in reality. Gasification is a key technology in converting coal, biomass, and wastes to useful high-value products. Until renewable energy can provide affordable energy hopefully by the year 2030, gasification can bridge the transition period by providing the clean liquid fuels, gas, and chemicals from the low grade feedstock. Gasification still needs many upgrades and technology breakthroughs. It remains in the niche market, not fully competitive in the major market of electricity generation, chemicals, and liquid fuels that are supplied from relatively cheap fossil fuels. The book provides the practical information for researchers and graduate students who want to review the current situation, to upgrade, and to bring in a new idea to the conventional gasification technologies

Controller tuning by means of multi-objective optimization algorithms: a global tuning framework

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A holistic multi-objective optimization design technique for controller tuning is presented. This approach gives control engineers greater flexibility to select a controller that matches their specifications. Furthermore, for a given controller it is simple to analyze the tradeoff achieved between conflicting objectives. By using the multi-objective design technique it is also possible to perform a global comparison between different control strategies in a simple and robust way. This approach thereby enables an analysis to be made of whether a preference for a certain control technique is justified. This proposal is evaluated and validated in a nonlinear multiple-input multiple-output system using two control strategies: a classical proportional- integral-derivative control scheme and a feedback state controller.This work was supported in part by the FPI-2010/19 Grant and the Project PAID-06-11 from the Universitat Politecnica de Valencia and in part by the Projects DPI2008-02133, TIN2011-28082, and ENE2011-25900 from the Spanish Ministry of Science and Innovation.Reynoso Meza, G.; García-Nieto Rodríguez, S.; Sanchís Saez, J.; Blasco, X. (2013). Controller tuning by means of multi-objective optimization algorithms: a global tuning framework. IEEE Transactions on Control Systems Technology. 21(2):445-458. https://doi.org/10.1109/TCST.2012.2185698S44545821

An Enhanced Differential Evolution Algorithm Based on Multiple Mutation Strategies

Differential evolution algorithm is a simple yet efficient metaheuristic for global optimization over continuous spaces. However, there is a shortcoming of premature convergence in standard DE, especially in DE/best/1/bin. In order to take advantage of direction guidance information of the best individual of DE/best/1/bin and avoid getting into local trap, based on multiple mutation strategies, an enhanced differential evolution algorithm, named EDE, is proposed in this paper. In the EDE algorithm, an initialization technique, opposition-based learning initialization for improving the initial solution quality, and a new combined mutation strategy composed of DE/current/1/bin together with DE/pbest/bin/1 for the sake of accelerating standard DE and preventing DE from clustering around the global best individual, as well as a perturbation scheme for further avoiding premature convergence, are integrated. In addition, we also introduce two linear time-varying functions, which are used to decide which solution search equation is chosen at the phases of mutation and perturbation, respectively. Experimental results tested on twenty-five benchmark functions show that EDE is far better than the standard DE. In further comparisons, EDE is compared with other five state-of-the-art approaches and related results show that EDE is still superior to or at least equal to these methods on most of benchmark functions

Multiobjective differential evolution based on fuzzy performance feedback: Soft constraint handling and its application in antenna designs

The recently emerging Differential Evolution is considered one of the most powerful tools for solving optimization problems. It is a stochastic population-based search approach for optimization over the continuous space. The main advantages of differential evolution are simplicity, robustness and high speed of convergence. Differential evolution is attractive to researchers all over the world as evidenced by recent publications. There are many variants of differential evolution proposed by researchers and differential evolution algorithms are continuously improved in its performance. Performance of differential evolution algorithms depend on the control parameters setting which are problem dependent and time-consuming task. This study proposed a Fuzzy-based Multiobjective Differential Evolution (FMDE) that exploits three performance metrics, specifically hypervolume, spacing, and maximum spread, to measure the state of the evolution process. We apply the fuzzy inference rules to these metrics in order to adaptively adjust the associated control parameters of the chosen mutation strategy used in this algorithm. The proposed FMDE is evaluated on the well known ZDT, DTLZ, and WFG benchmark test suites. The experimental results show that FMDE is competitive with respect to the chosen state-of-the-art multiobjective evolutionary algorithms. The advanced version of FMDE with adaptive crossover rate (AFMDE) is proposed. The proof of concept AFMDE is then applied specifically to the designs of microstrip antenna array. Furthermore, the soft constraint handling technique incorporates with AFMDE is proposed. Soft constraint AFMDE is evaluated on the benchmark constrained problems. AFMDE with soft constraint handling technique is applied to the constrained non-uniform circular antenna array design problem as a case study

Multi-level Systems Modeling and Optimization

This research combines the disciplines of system-of-systems (SoS) modeling, platform-based design, optimization and evolving design spaces to achieve a novel capability for designing solutions to key aeronautical mission challenges. A central innovation in this approach is the confluence of multi-level modeling (from sub-systems to the aircraft system to aeronautical system-of-systems) in a way that coordinates the appropriate problem formulations at each level and enables parametric search in design libraries for solutions that satisfy level-specific objectives. The work here addresses the topic of SoS optimization and discusses problem formulation, solution strategy, the need for new algorithms that address special features of this problem type, and also demonstrates these concepts using two example application problems - a surveillance UAV swarm problem, and the design of noise optimal aircraft and approach procedures