319 research outputs found

    Multiobjective synchronization of coupled systems

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    Copyright @ 2011 American Institute of PhysicsSynchronization of coupled chaotic systems has been a subject of great interest and importance, in theory but also various fields of application, such as secure communication and neuroscience. Recently, based on stability theory, synchronization of coupled chaotic systems by designing appropriate coupling has been widely investigated. However, almost all the available results have been focusing on ensuring the synchronization of coupled chaotic systems with as small coupling strengths as possible. In this contribution, we study multiobjective synchronization of coupled chaotic systems by considering two objectives in parallel, i. e., minimizing optimization of coupling strength and convergence speed. The coupling form and coupling strength are optimized by an improved multiobjective evolutionary approach. The constraints on the coupling form are also investigated by formulating the problem into a multiobjective constraint problem. We find that the proposed evolutionary method can outperform conventional adaptive strategy in several respects. The results presented in this paper can be extended into nonlinear time-series analysis, synchronization of complex networks and have various applications

    Self-Adaptive Genetic Algorithms with Simulated Binary Crossover

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    Self-adaptation is an essential feature of natural evolution. However, in the context of function optimization, self-adaptation features of evolutionary search algorithms have been explored only with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the self-adaptive feature of real-parameter genetic algorithms (GAs) using simulated binary crossover (SBX) operator and without any mutation operator. The connection between the working of self-adaptive ESs and real-parameter GAs with SBX operator is also discussed. Thereafter, the self-adaptive behavior of real-parameter GAs is demonstrated on a number of test problems commonly-used in the ES literature. The remarkable similarity in the working principle of real-parameter GAs and self-adaptive ESs shown in this study suggests the need of emphasizing further studies on self-adaptive GAs

    Optimal Microgrid Topology Design and Siting of Distributed Generation Sources Using a Multi-Objective Substrate Layer Coral Reefs Optimization Algorithm

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    n this work, a problem of optimal placement of renewable generation and topology design for a Microgrid (MG) is tackled. The problem consists of determining the MG nodes where renewable energy generators must be optimally located and also the optimization of the MG topology design, i.e., deciding which nodes should be connected and deciding the lines’ optimal cross-sectional areas (CSA). For this purpose, a multi-objective optimization with two conflicting objectives has been used, utilizing the cost of the lines, C, higher as the lines’ CSA increases, and the MG energy losses, E, lower as the lines’ CSA increases. To characterize generators and loads connected to the nodes, on-site monitored annual energy generation and consumption profiles have been considered. Optimization has been carried out by using a novel multi-objective algorithm, the Multi-objective Substrate Layers Coral Reefs Optimization algorithm (Mo-SL-CRO). The performance of the proposed approach has been tested in a realistic simulation of a MG with 12 nodes, considering photovoltaic generators and micro-wind turbines as renewable energy generators, as well as the consumption loads from different commercial and industrial sites. We show that the proposed Mo-SL-CRO is able to solve the problem providing good solutions, better than other well-known multi-objective optimization techniques, such as NSGA-II or multi-objective Harmony Search algorithm.This research was partially funded by Ministerio de Economía, Industria y Competitividad, project number TIN2017-85887-C2-1-P and TIN2017-85887-C2-2-P, and by the Comunidad Autónoma de Madrid, project number S2013ICE-2933_02

    Self-adaptive simulated binary crossover for real-parameter optimization

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    Simulated binary crossover (SBX) is a real-parameter recombinationoperator which is commonly used in the evolutionary algorithm (EA) literature. The operatorinvolves a parameter which dictates the spread of offspring solutionsvis-a-vis that of the parent solutions. In all applications of SBX sofar, researchers have kept a fixed value throughout a simulation run. In this paper, we suggest a self-adaptive procedure of updating theparameter so as to allow a smooth navigation over the functionlandscape with iteration. Some basic principles of classicaloptimization literature are utilized for this purpose. The resultingEAs are found to produce remarkable and much better results comparedto the original operator having a fixed value of the parameter. Studieson both single and multiple objective optimization problems are madewith success

    Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)

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    Recently, increasing works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i.e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality. To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into real and fake samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables. Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm

    Evolutionary multi-objective decision support systems for conceptual design

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    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

    Comparison of multiobjective optimization methods applied to urban drainage adaptation problems

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     This is the author accepted manuscript. The final version is available from American Society of Civil Engineers via the DOI in this recordThis article compares three multiobjective evolutionary algorithms (MOEAs) with application to the urban drainage system (UDS) adaptation of a capital city in North China. Particularly, we consider the well-known NSGA-II, the built-in solver in the MATLAB Global Optimization Toolbox (MLOT), and a newly-developed hybrid MOEA called GALAXY. Avariety of parameter combinations of each MOEA is systemically applied to examine their impacts on optimization efficiency. Results suggest that the traditional MOEAs suffer from severe parameterization issues. For NSGA-II, the distribution indexes of crossover and mutation operators were found to have dominant impacts, while the probabilities of the two operators played a secondary role. For MLOT, the two-point and the scattered crossover operators accompanied by the adaptive-feasible mutation operator gained the best Pareto fronts, provided the crossover fraction is set to lower values. In contrast, GALAXY was the most robust and easy-to-use tool among the three MOEAs, owing to its elimination of various associated parameters of searching operators for substantially alleviating the parameterization issues. This study contributes to the literature by showing how to improve the robustness of identifying optimal solutions through better selection of operators and associated parameter settings for real-world UDS applications.National Natural Science Foundation of ChinaPublic Welfare Research and Ability Construction Project of Guangdong Province, ChinaScience and Technology Program of Guangzhou, ChinaWater Conservancy Science and Technology Innovation Project of Guangdong Province, Chin
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