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Memetic niching-based evolutionary algorithms for solving nonlinear equation system

EXPERT SYSTEMS WITH APPLICATIONS 2020年 149卷 113261-000页

作者： Liao, Zuowen Gong, Wenyin Wang, Ling China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

In numerical computation, finding multiple roots of nonlinear equation systems (NESs) in a single run is a fundamental and difficult problem. Recently, evolutionary algorithms (EAs) have been applied to solve NESs. However, due to the diversity preservation mechanism that EAs use, the accuracy of the roots may be reduced. To remedy this drawback, we propose a generic framework of memetic niching-based EA, referred to as MENI-EA. The main features of the framework are: i) the numerical method for a NES is integrated into an EA to obtain highly accurate roots;ii) the niching technique is employed to improve the diversity of the population;iii) different roots of the NESs are located simultaneously in a singe run;and iv) different numerical methods and different niching techniques can be used in the framework. To evaluate the performance of our approach, thirty NESs were chosen from the literature as the test suite. Experimental results show that the proposed approach is capable of yielding promising performance for different NESs in both the root ratio and success rate. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Nonlinear equation system Niching techniques Numerical methods evolutionary algorithms

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Application of evolutionary algorithms to optimise one- and two-dimensional gradient chromatographic separations

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JOURNAL OF CHROMATOGRAPHY A 2020年 1628卷 461435-461435页

作者： Huygens, Bram Efthymiadis, Kyriakos Nowe, Ann Desmet, Gert Vrije Univ Brussel Dept Chem Engn Pl Laan 2 B-1050 Brussels Belgium Vrije Univ Brussel Artificial Intelligence Lab Pl Laan 2 B-1050 Brussels Belgium

We report on the performance of three classes of evolutionary algorithms (genetic algorithms (GA), evolution strategies (ES) and covariance matrix adaptation evolution strategy (CMA-ES)) as a means to enhance searches in the method development spaces of 1D and 2D-chromatography. After optimisation of the design parameters of the different algorithms, they were benchmarked against the performance of a plain grid search. It was found that all three classes significantly outperform the plain grid search, especially in terms of the number of search runs needed to achieve a given separation quality. As soon as more than 100 search runs are needed, the ES algorithm clearly outperforms the GA and CMA-ES algorithms, with the latter performing very well for short searches (< 50 search runs) but being susceptible to convergence to local optima for longer searches. It was also found that the performance of the ES and GA algorithms, as well as the grid search, follow a hyperbolic law in the large search run number limit, such that the convergence rate parameter of this hyperbolic function can be used to quantify the difference in required number of search runs for these algorithms. In agreement with one's physical expectations, it was also found that the general advantage of the GA and ES algorithms over the grid search, as well as their mutual performance differences, grow with increasing difficulty of the separation problem. (C) 2020 Elsevier B.V. All rights reserved.

关键词： evolutionary algorithms Gradient elution Liquid chromatography Method development Multi-dimensional chromatography

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Demand Response of Residential Houses Equipped with PV-Battery Systems: An Application Study Using evolutionary algorithms

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ENERGIES 2020年第10期13卷 2466-2466页

作者： Lezama, Fernando Faia, Ricardo Faria, Pedro Vale, Zita Polytech Porto ISEP IPP Res Grp Intelligent Engn & Comp Adv Innovat & Dev P-4200072 Porto Portugal Polytech Porto ISEP IPP P-4200072 Porto Portugal

Households equipped with distributed energy resources, such as storage units and renewables, open the possibility of self-consumption of on-site generation, sell energy to the grid, or do both according to the context of operation. In this paper, a model for optimizing the energy resources of households by an energy service provider is developed. We consider houses equipped with technologies that support the actual reduction of energy bills and therefore perform demand response actions. A mathematical formulation is developed to obtain the optimal scheduling of household devices that minimizes energy bill and demand response curtailment actions. In addition to the scheduling model, the innovative approach in this paper includes evolutionary algorithms used to solve the problem under two optimization approaches: (a) the non-parallel approach combine the variables of all households at once;(b) the parallel-based approach takes advantage of the independence of variables between households using a multi-population mechanism and independent optimizations. Results show that the parallel-based approach can improve the performance of the tested evolutionary algorithms for larger instances of the problem. Thus, while increasing the size of the problem, namely increasing the number of households, the proposed methodology will be more advantageous. Overall, vortex search overcomes all other tested algorithms (including the well-known differential evolution and particle swarm optimization) achieving around 30% better fitness value in all the cases, demonstrating its effectiveness in solving the proposed problem.

关键词： demand response energy service provider energy storage system evolutionary algorithms optimization photovoltaic generation

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Testing evolutionary algorithms for optimization of water distribution networks

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CANADIAN JOURNAL OF CIVIL ENGINEERING 2019年第5期46卷 391-402页

作者： Moosavian, Naser Lence, Barbara Univ British Columbia Dept Civil Engn 6250 Appl Sci Lane Vancouver BC V2T 1Z4 Canada

Water distribution networks (WDNs) are one of the most important elements of urban infrastructure and require large investment for construction. Design of WDNs is classified as a large combinatorial discrete nonlinear optimization problem. The main concerns associated with the optimization of such networks are the nonlinearity of the discharge-head loss relationships for pipes and the discrete nature of pipe sizes. Due to these issues, this problem is widely considered to be a benchmark problem for testing and evaluating the performance of nonlinear and heuristic optimization algorithms. This paper compares different techniques, all based on evolutionary algorithms (EAs), which yield optimal solutions for least-cost design of WDNs. All of these algorithms search for the global optimum starting from populations of solutions, rather than from a single solution, as in Newton-based search methods. They use different operators to improve the performance of many solutions over repeated iterations. Ten EAs, four of them for the first time, are applied to the design of three networks and their performance in terms of the least cost, under different stopping criteria, are evaluated. Statistical information for 20 executions of the ten algorithms is summarized, and Friedman tests are conducted. Results show that, for the two-loop benchmark network, the particle swarm optimization gravitational search and biology and bioinformatics global optimization algorithms efficiently converge to the global optimum, but perform poorly for large networks. In contrast, given a sufficient number of function evaluations, the covariance matrix adaptation evolution strategy and soccer league competition algorithm consistently converge to the global optimum, for large networks.

关键词： evolutionary algorithms genetic algorithm water distribution networks optimization

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Synthesizing Real-Time Schedulability Tests using evolutionary algorithms: A Proof of Concept 40

Synthesizing Real-Time Schedulability Tests using Evolutiona...

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40th IEEE Real-Time Systems Symposium (RTSS)

作者： Dziurzanski, Piotr Davis, Robert, I Indrusiak, Leandro Soares Univ York Dept Comp Sci York N Yorkshire England

ISBN: (纸本)9781728144030

This paper assesses the potential for mechanised assistance in the formulation of schedulability tests. The novel idea is to use evolutionary algorithms to semi-automate the process of deriving response time analysis equations. The proof of concept presented in this paper focuses on the synthesis of mathematical expressions for the schedulability analysis of messages on Controller Area Network (CAN). This problem is of particular interest, since the original analysis developed in the early 1990s was later found to be flawed. Further, as well as known exact tests that have been formally proven, there are a number of useful sufficient tests of pseudo-polynomial complexity and closed-form polynomial-time upper bounds on response times that provide useful comparisons.

关键词： real-time systems schedulability analysis evolutionary algorithms Controller Area Network

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Analysis of Baseline evolutionary algorithms for the Packing While Travelling Problem 19

Analysis of Baseline Evolutionary Algorithms for the Packing...

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15th ACM/SIGEVO Conference on Foundations of Genetic algorithms (FOGA)

作者： Roostapour, Vahid Pourhassan, Mojgan Neumann, Frank Univ Adelaide Sch Comp Sci Adelaide SA Australia

ISBN: (纸本)9781450362542

The performance of base-line evolutionary algorithms (EAs) on combinatorial problems has been studied rigorously. From the theoretical viewpoint, the literature extensively investigates the linear problems, while the theoretical analysis of the non-linear problems is still far behind. In this paper, variations of the Packing While Travelling (PWT) - also known as the non-linear knapsack problem - are studied as an attempt to analyse the behaviour of EAs on non-linear problems from theoretical perspective. We investigate PWT for two cities and n items with correlated weights and profits, using single-objective and multi-objective algorithms. Our results show that RLS swap, which differs from the classical RLS by having the ability to swap two bits in one iteration, finds the optimal solution in O(n(3)) expected time. We also study an enhanced version of GSEMO, which a specific selection operator to deal with exponential population size, and prove that it finds the Pareto front in the same asymptotic expected time. In the case of uniform weights, (1+1) EA is able to find the optimal solution in expected time O(n(2) log (max{n,p(max)})), where p(max) is the largest profit of the given items. We also perform an experimental analysis to complement our theoretical investigations and provide additional insights into the runtime behavior.

关键词： evolutionary algorithms Packing while travelling problem Run-time analysis (1+1) EA

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Combining evolutionary algorithms and Deep Learning for Hardware/Software Interface Optimization 1

Combining Evolutionary Algorithms and Deep Learning for Hard...

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ACM/IEEE 1st Workshop on Machine Learning for CAD (MLCAD)

作者： Servadei, Lorenzo Mosca, Edoardo Werner, Michael Esen, Volkan Wille, Robert Ecker, Wolfgang Johannes Kepler Univ Linz Munich Germany Infineon Technol AG Munich Germany Tech Univ Munich Munich Germany

ISBN: (纸本)9781728157580

With the advancement of Internet of Things, the cost of System-on-Chips (in terms of area, performance, etc.) becomes increasingly relevant for realizing affordable as well as performant devices. Although System-on-Chips are very diverse with respect to specifications and requirements, some components are ubiquitous. One of them is the Hardware/Software Interface, which serves for controlling communication and interconnected functionalities between Hardware and Software. Motivated by their common use, the implementation of optimized interfaces towards certain costs (in terms of area, performance, etc.) becomes a central problem in the design of embedded systems. In this work we introduce a novel optimization method for minimizing the cost of Hardware/Software Interfaces using Convolutional Neural Networks coupled with evolutionary algorithms.

关键词： Hardware/Software Interface Optimization Machine Learning Deep Learning evolutionary algorithms

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Multilevel design optimization of hydraulic turbines based on hierarchical metamodel-assisted evolutionary algorithms 29

Multilevel design optimization of hydraulic turbines based o...

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29th IAHR Symposium on Hydraulic Machinery and Systems (IAHR)

作者： Kontoleontos, E. Zormpa, M. Nichtawitz, S. Mack-Sahl, D. Weissenberger, S. ANDRITZ HYDRO GmbH Linz Austria Natl Tech Univ Athens Athens Greece

In this paper, an efficient hydraulic optimization procedure is presented and applied to the design of hydraulic turbines. For computationally expensive industrial design optimization problems, an advanced optimization tool (EASY software) and a fast CFD evaluation tool are required. EASY optimization software is a Hierarchical Metamodel-Assisted evolutionary Algorithm (HMAEA) that can be used in both single- (SOO) and multi-objective optimization (MOO) problems. In order to minimize the CFD solver calls during the optimization design, the MAEA rely on local metamodels, trained on the fly, that are used to identify the most promising members in each population and then only these are to be re-evaluated by the CPU costly CFD solver. For additional economy in the CPU cost, the hierarchical (two-level) optimization scheme is used in this paper, where at each level, a different evaluation tool, a low and a high fidelity specific software, can be linked. The low level utilizes a low-CPU cost and low-accuracy tool to explore the design space with a minimum impact to the wall clock time and the high level, using the high fidelity, high-CPU cost tool is used to exploit the information from the low level. For the applications presented in this paper, the high fidelity model is an incompressible Navier-Stokes equation solver and the low fidelity model is based on the solution of the incompressible Euler equations. In order to optimize the geometry of hydraulic machines, an in-house automatic geometry and mesh generation tool has been integrated in the optimization tool chain. In what follows, 2 three-objective design optimization problems of 3D Francis hydraulic turbines are presented. The optimization objective functions concern the 'quality' of the runner outlet velocity profile, the cavitation behavior and efficiency of the runner. The optimization results of the hydraulic turbine components along with the performance of the presented optimization procedure are shown in th

关键词： evolutionary algorithms

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Archiver Effects on the Performance of State-of-the-art Multi- and Many-objective evolutionary algorithms

Archiver Effects on the Performance of State-of-the-art Mult...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Bezerra, Leonardo C. T. Lopez-Ibanez, Manuel Stutzle, Thomas Univ Fed Rio Grande do Norte Inst Metropole Digital Natal RN Brazil Univ Manchester Alliance Manchester Business Sch Manchester Lancs England Univ Libre Bruxelles CoDE IRIDIA Brussels Belgium

ISBN: (纸本)9781450361118

Early works on external solution archiving have pointed out the benefits of unbounded archivers and there have been great advances, theoretical and algorithmic, in bounded archiving methods. Moreover, recent work has shown that the populations of most multi- and many-objective evolutionary algorithms (MOEAs) lack the properties that one would desire when trying to find a bounded Pareto-optimal front. Despite all these results, many recent MOEAs are still being proposed, analyzed and compared without considering any kind of archiver assuming their additional computational cost is not justified. In this paper, we investigate the effect of using various kinds of archivers, improving over previous studies in several aspects: (i) the parameters of MOEAs with and without an external archiver are tuned separately using automatic configuration methods;(ii) we consider a comprehensive range of problem scenarios (number of objectives, function evaluations, computation time limit);(iii) we employ multiple, complementary quality metrics;and (iv) we study the effect of unbounded archivers and two state-of-the-art bounded archiving methods. Our results show that both unbounded and bounded archivers are beneficial even for many-objective problems. We conclude that future proposals and comparisons of MOEAs must include archiving as an algorithmic component.

关键词： Multi-objective optimization evolutionary algorithms algorithm configuration experimental analysis archiving

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Expert Competitive Traffic Light Optimization with evolutionary algorithms 5

Expert Competitive Traffic Light Optimization with Evolution...

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5th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS)

作者： Semet, Yann Berthelot, Benoit Glais, Thierry Isberie, Christian Varest, Aurelien Thales Res & Technol 1 Ave Augustin Fresnel Palaiseau France CDVIA 2 Rue Suchet Maisons Alfort France Thales Commun & Secur Rue Mare Joncs Bretigny Sur Orge France

ISBN: (纸本)9789897583742

We present a complete system to optimize traffic lights green phases and temporal offsets based on a combination of microscopic simulation and black box, evolutionary algorithms. We also report the outcome of an AI versus experts comparison workshop conducted with our algorithm and seasoned experts from a specialized traffic engineering office. Experimental results indicate that the proposed algorithmic scheme significantly outperforms expert efforts. Our system entails a memetic (genetic+gradient) calibration module to adapt the Origin/Destination (O/D) matrix to current traffic conditions, an inoculation procedure to incorporate existing traffic light programs, genetic multi-objective optimization capabilities and sound metrics. Experiments are conducted over several real world datasets of operational sizes from the Paris outskirts and various other French urban areas. Our experimental outcome is threefold. First, we report the success of the memetic calibration module in adjusting the simulator's O/D matrix to a point with variation levels corresponding to recorded sensor data. Second, we confirm the ability of the system to obtain significant gains on that sound basis: gains ranging from 15% to 35% are consistently reached on both traffic jams reduction and pollutant emissions. Most importantly, we report the outcome of the comparison workshop: a formalized methodology followed by experts to manually optimize traffic lights, iterative experimental logs tracing the application of that methodology to two real world cases and comparable results obtained by the algorithm on the same cases. Results indicate that the AI module performs significantly better than experts in both speed and final solution quality.

关键词： Traffic Lights Optimization evolutionary algorithms Genetic algorithms Artificial Intelligence Simulation Calibration Experts

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