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2nd International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2021

作者： Kang, Senbo Li, Jie Li, Juan Xiong, Jing Liu, Chang Beijing Institute of Technology Beijing China

ISBN: (纸本)9783800756162

In the process of the Unmanned Aerial Vehicle (UAV) swarms conducting search-attack missions, target recognition is sometimes inaccurate, due to observation errors. This may lead to misjudgment of the targets' types, resulting in a decrease in the swarm’s efficiency. In response to this problem, the target recognition error is first modelled as a recognition matrix P. Secondly, a tuning matrix Q is introduced to reduce detrimental effects induced by recognition errors. In target recognition uncertain environments, we develop a task allocation model with the tuning matrix Q as the decision variable with an aim at maximizing the reward of task assignment. Afterwards, a problem-specific evolutionary algorithm termed the Stochastic-Tuning-based evolutionary Algorithm (ST-EA) is designed to optimize the above formulated constrained optimization model. Numerical experiments are performed on six test instances with different P values and different numbers of UAVs. Experimental results demonstrate the efficiency of the proposed ST-EA and the superiority when it is compared with ST-EA-NoQ, a variant of ST-EA without using the tuning matrix Q. Furthermore, the stability and convergence of the proposed algorithm is further demonstrated. Finally, by comparing with the Stochastic-Tuning-based Differential Evolution Algorithm (ST-DE), we show the advantages of ST-EA. © 2021 AIIPCC 2021 - 2nd International Conference on Artificial Intelligence, Information Processing and Cloud Computing. All rights reserved.

关键词： evolutionary algorithms

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Runtime analysis of population-based evolutionary algorithms 21

Runtime analysis of population-based evolutionary algorithms

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2021 Genetic and evolutionary Computation Conference, GECCO 2021

作者： Lehre, Per Kristian Oliveto, Pietro S. University of Birmingham B15 2TT United Kingdom University of Sheffield S1 4DP United Kingdom

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that c... 详细信息

ISBN: (纸本)9781450383516

关键词： evolutionary algorithms

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Fuzzy and evolutionary algorithms for Transport Logistics Under Uncertainty

Fuzzy and Evolutionary Algorithms for Transport Logistics Un...

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International Conference on Intelligent and Fuzzy Systems, INFUS 2020

作者： Kondratenko, Yuriy Kondratenko, Galyna Sidenko, Ievgen Taranov, Mykyta Intelligent Information Systems Department Petro Mohyla Black Sea National University 68th Desantnykiv Str. 10 Mykolaiv54003 Ukraine

ISBN: (纸本)9783030511555

This paper deals with the analysis of fuzzy and evolutionary approaches for efficiently solving vehicle routing problems (VRP) with constraints on vehicle’s capacity (CVRP) and time-windows (VRPTW). Authors focused their research on CVRP for marine bunkering tankers, in particular, on the planning of tanker’s routes under uncertainty of fuel demands at nodes. Triangular fuzzy numbers (TFNs) are proposed for modeling uncertain demands. In this case, the maximum possible number of customers is calculated, which can be served based on the subtraction operation with TFNs. In the paper, the authors also analyzed the planning of transport routes with time-windows. Currently, there are several methods and algorithms for planning of transport routes with time-windows, in particular: saving and sweeping algorithms, ant colony optimization (ACO) algorithm, artificial bee colony (ABC) algorithm, etc. In this paper, the authors discussed the features of using the ACO algorithm and the ABC algorithm to solve the vehicle routing problems with time-windows and the influence of their application on the results. The modeling results confirm the efficiency of the proposed fuzzy and evolutionary algorithms. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： evolutionary algorithms

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Optimum design of shallow foundation using evolutionary algorithms

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SOFT COMPUTING 2020年第9期24卷 6809-6833页

作者： Kashani, Ali R. Gandomi, Mostafa Camp, Charles V. Gandomi, Amir H. Univ Memphis Dept Civil Engn Memphis TN 38152 USA Univ Tehran Dept Civil Engn Tehran Iran Stevens Inst Technol Sch Business Hoboken NJ 07030 USA

In the current study, the performance of three evolutionary algorithms, differential algorithm (DE), evolution strategy (ES), and biogeography-based optimization algorithm (BBO), is examined for foundation design optimization. Moreover, four recent variations of evolutionary-based algorithms [i.e., improved differential evolution algorithm based on an adaptive mutation scheme, weighted differential evolution algorithm (WDE), linear population size reduction success-history-based adaptive differential evolution algorithm, and biogeography-based optimization with covariance matrix-based migration] have been tackled for handling the current problem. The objective function is based on the cost of shallow foundation designs that satisfy ACI 318-05 requirements is formulated as the objective function. This study addresses shallow footing optimization with two attitudes, routine optimization, and sensitivity analysis. As a further study, the effect of the location of the column at the top of the foundation is examined by adding two additional design variables. Three numerical case studies are used for both routine and sensitivity analysis. Moreover, the most common evolutionary-based technique, genetic algorithm (GA), is considered as a benchmark to evaluate the proposed methods' efficiency. Based on the results, there is no algorithm which works as the most efficient solver over all the cases;while, BBO and WDE showed an acceptable performance because of satisfying records in most cases. There were several cases in which GA, DE, and ES were incapable of finding a valid solution which meets all the constraints simultaneously.

关键词： Metaheuristic algorithms Global optimization Construction industry Shallow footing evolutionary algorithms

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Multi-adaptive neuro-fuzzy inference system modelling for prediction of band-notched behaviour of slotted-UWB antennas optimised using evolutionary algorithms

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IET MICROWAVES ANTENNAS & PROPAGATION 2020年第12期14卷 1396-1403页

作者： Sarkar, Debanjali Khan, Taimoor Talukdar, Fazal Ahmed Natl Inst Technol Silchar Dept Elect & Commun Engn Silchar Assam India

This study introduces modelling of multi-adaptive neuro-fuzzy inference system (MANFIS) for predicting the bandwidth and notch frequencies of slotted ultra-wideband (UWB) antennas. Rectangular-shaped printed monopole antennas embedded with U-shaped slots are designed to realise triple band-notch characteristics. A MANFIS model is then developed to predict five output parameters (two cut-off frequency points and three notched frequency points) considering 15 geometrical variables of the designed antennas as the inputs of MANFIS model. Extensive simulation has been performed using HFSS software to generate training and testing data patterns. Two optimisation algorithms, genetic algorithm (GA) and particle swarm optimisation (PSO), are implemented to optimally determine the appropriate values of the fuzzy inference system parameters. For GA-optimised MANFIS model, the percentage error is observed between 1 and 2%, whereas, in PSO-trained MANFIS model, it is observed <1%. The comparative analysis establishes that the PSO-trained MANFIS model precisely predicts the antenna performances. For validating the proposed modelling technique, an optimised configuration of the slotted UWB antenna prototype has been fabricated and characterised.

关键词： slot antennas fuzzy reasoning monopole antennas fuzzy neural nets ultra wideband antennas genetic algorithms particle swarm optimisation microstrip antennas telecommunication computing multifrequency antennas genetic algorithm data pattern testing HFSS software rectangular-shaped printed monopole antennas PSO-trained MANFIS model GA-optimised MANFIS model fuzzy inference system parameters particle swarm optimisation optimisation algorithms geometrical variables notched frequency points cut-off frequency points triple band-notch characteristics U-shaped slots ultrawideband antennas notch frequencies multiadaptive neuro-fuzzy inference system evolutionary algorithms slotted-UWB antennas band-notched behaviour neuro-fuzzy inference system modelling slotted UWB antenna prototype optimised configuration antenna performances

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A Revisit of Infinite Population Models for evolutionary algorithms on Continuous Optimization Problems

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evolutionary COMPUTATION 2020年第1期28卷 55-85页

作者： Song, Bo Li, Victor O. K. Univ Hong Kong Dept Elect & Elect Engn Pokfulam Hong Kong Peoples R China

Infinite population models are important tools for studying population dynamics of evolutionary algorithms. They describe how the distributions of populations change between consecutive generations. In general, infinite population models are derived from Markov chains by exploiting symmetries between individuals in the population and analyzing the limit as the population size goes to infinity. In this article, we study the theoretical foundations of infinite population models of evolutionary algorithms on continuous optimization problems. First, we show that the convergence proofs in a widely cited study were in fact problematic and incomplete. We further show that the modeling assumption of exchangeability of individuals cannot yield the transition equation. Then, in order to analyze infinite population models, we build an analytical framework based on convergence in distribution of random elements which take values in the metric space of infinite sequences. The framework is concise and mathematically rigorous. It also provides an infrastructure for studying the convergence of the stacking of operators and of iterating the algorithm which previous studies failed to address. Finally, we use the framework to prove the convergence of infinite population models for the mutation operator and the k-ary recombination operator. We show that these operators can provide accurate predictions for real population dynamics as the population size goes to infinity, provided that the initial population is identically and independently distributed.

关键词： evolutionary algorithms infinite population models population dynamics convergence in distribution theoretical analysis

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Combining evolutionary algorithms and Machine Learning Models in Landslide Susceptibility Assessments

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REMOTE SENSING 2020年第23期12卷 1-26页

作者： Chen, Wei Chen, Yunzhi Tsangaratos, Paraskevas Ilia, Ioanna Wang, Xiaojing Xian Univ Sci & Technol Coll Geol & Environm Xian 710054 Peoples R China Minist Nat Resources Key Lab Coal Resources Explorat & Comprehens Util Xian 710021 Peoples R China Natl Tech Univ Athens Sch Min & Met Engn Dept Geol Sci Lab Engn Geol & Hydrogeol Zografos 15780 Greece

The main objective of the present study is to introduce a novel predictive model that combines evolutionary algorithms and machine learning (ML) models, so as to construct a landslide susceptibility map. Genetic algorithms (GA) are used as a feature selection method, whereas the particle swarm optimization (PSO) method is used to optimize the structural parameters of two ML models, support vector machines (SVM) and artificial neural network (ANN). A well-defined spatial database, which included 335 landslides and twelve landslide-related variables (elevation, slope angle, slope aspect, curvature, plan curvature, profile curvature, topographic wetness index, stream power index, distance to faults, distance to river, lithology, and hydrological cover) are considered for the analysis, in the Achaia Regional Unit located in Northern Peloponnese, Greece. The outcome of the study illustrates that both ML models have an excellent performance, with the SVM model achieving the highest learning accuracy (0.977 area under the receiver operating characteristic curve value (AUC)), followed by the ANN model (0.969). However, the ANN model shows the highest prediction accuracy (0.800 AUC), followed by the SVM (0.750 AUC) model. Overall, the proposed ML models highlights the necessity of feature selection and tuning procedures via evolutionary optimization algorithms and that such approaches could be successfully used for landslide susceptibility mapping as an alternative investigation tool.

关键词： landslide susceptibility feature selection optimizing structural parameters evolutionary algorithms genetic algorithms particle swarm optimization support vector machines artificial neural network

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Memetic algorithms outperform evolutionary algorithms in multimodal optimisation

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ARTIFICIAL INTELLIGENCE 2020年 287卷

作者： Phan Trung Hai Nguyen Sudholt, Dirk Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England Univ Sheffield Dept Comp Sci Sheffield S1 4DP S Yorkshire England

Memetic algorithms integrate local search into an evolutionary algorithm to combine the advantages of rapid exploitation and global optimisation. We provide a rigorous runtime analysis of memetic algorithms on the Hurdle problem, a landscape class of tunable difficulty with a "big valley structure", a characteristic feature of many hard combinatorial optimisation problems. A parameter called hurdle width describes the length of fitness valleys that need to be overcome. We show that the expected runtime of plain evolutionary algorithms like the (1+1) EA increases steeply with the hurdle width, yielding superpolynomial times to find the optimum, whereas a simple memetic algorithm, (1+1) MA, only needs polynomial expected time. Surprisingly, while increasing the hurdle width makes the problem harder for evolutionary algorithms, it becomes easier for memetic algorithms. We further give the first rigorous proof that crossover can decrease the expected runtime in memetic algorithms. A (2+1) MA using mutation, crossover and local search outperforms any other combination of these operators. Our results demonstrate the power of memetic algorithms for problems with big valley structures and the benefits of hybridising multiple search operators. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Search heuristics Hybridisation evolutionary algorithms Black-box optimisation Memetic algorithms Time complexity

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evolutionary algorithms for solving unconstrained, constrained and multi-objective noisy combinatorial optimisation problems

arXiv

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arXiv 2021年

作者： Aishwaryaprajna Rowe, Jonathan E. School of Computer Science University of Birmingham Birmingham United Kingdom The Alan Turing Institute London United Kingdom

We present an empirical study of a range of evolutionary algorithms applied to various noisy combinatorial optimisation problems. There are three sets of experiments. The first looks at several toy problems, such as ONEMAX and other linear problems. We find that UMDA and the Paired-Crossover evolutionary Algorithm (PCEA) are the only ones able to cope robustly with noise, within a reasonable fixed time budget. In the second stage, UMDA and PCEA are then tested on more complex noisy problems: SUBSETSUM, KNAPSACK and SETCOVER. Both perform well under increasing levels of noise, with UMDA being the better of the two. In the third stage, we consider two noisy multi-objective problems (COUNTING-ONESCOUNTINGZEROS and a multi-objective formulation of SETCOVER). We compare several adaptations of UMDA for multi-objective problems with the Simple evolutionary Multi-objective Optimiser (SEMO) and NSGAII. We conclude that UMDA, and its variants, can be highly effective on a variety of noisy combinatorial optimisation, outperforming many other evolutionary algorithms. © 2021, CC BY.

关键词： evolutionary algorithms

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Revising seismic behaviour factors for reinforced concrete bridge design in the longitudinal direction using multi-objective evolutionary algorithms

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BULLETIN OF EARTHQUAKE ENGINEERING 2020年第3期18卷 925-951页

作者： Camacho, Vitor T. Lopes, Mario Oliveira, Carlos S. IST Dept Civil Engn CERIS Av Rovisco Pais 1 P-1049001 Lisbon Portugal

Seismic behaviour factors represent the ratio between the strength of a structure, assuming it always maintains an elastic behaviour, and the strength demand with plastic behaviour and consequent loss of stiffness, at the seismic target displacement. This value is closely related to ductility and to energy dissipation due to hysteretic behaviour. The use of behaviour factors allows to design structures with elastic models, without having to explicitly account for material non-linearity while taking advantage of ductility. However, the definition of these values is not easy, and is dependent on several factors. In bridges, these factors can be, among others, regularity of the bridge in terms of pier height, concrete and steel quality, size of elements and amount of steel reinforcement, pier confinement, etc. These factors influence ductility demand and available ductility in different ways and through multi-objective optimization (MOO), the infrastructure solutions that maximize the use of the available ductility under a given earthquake action and for a given bridge superstructure, pier height scheme and ductility class according to Eurocode 8-part 2, can be obtained. Those optimized solutions, which are obtained through the minimization of steel and concrete in the piers as concurrent objectives, are associated with the maximum behaviour factors that can be used in the design of a given bridge and can be compared with the values recommended by EC8-part 2. Without loss of generality, the methodology is applied to a set of case-studies composed of RC bridges with four 30-m spans and circular piers, analysed in the longitudinal direction and without accounting for abutment effects. With the results from the MOO, the behaviour factors associated to solutions with different ductility levels and pier irregularity schemes are calculated and equations are derived, relating the obtained behaviour factors with a pier irregularity measure and ductility level. The results also

关键词： Seismic behaviour factors Multi-objective optimization evolutionary algorithms Bridge seismic design Non-linear static analysis

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