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Robust Estimation of Deformation from Observation Differences Using Some evolutionary optimisation algorithms

SENSORS 2022年第1期22卷 159-159页

作者： Batilovic, Mehmed Durovic, Radovan Susic, Zoran Kanovic, Zeljko Cekic, Zoran Univ Novi Sad Fac Tech Sci Trg Dositeja Obradovica 6 Novi Sad 21101 Serbia Univ Montenegro Fac Civil Engn Bulevar Dzordza Vasingtona bb Podgorica 81000 Montenegro South Ural State Univ Inst Architecture & Construct Lenin Prospect 76 Chelyabinsk 454080 Russia Singidunum Univ Fac Informat & Comp Danijelova 32 Belgrade 11000 Serbia

In this paper, an original modification of the generalised robust estimation of deformation from observation differences (GREDOD) method is presented with the application of two evolutionary optimisation algorithms, the genetic algorithm (GA) and generalised particle swarm optimisation (GPSO), in the procedure of robust estimation of the displacement vector. The iterative reweighted least-squares (IRLS) method is traditionally used to perform robust estimation of the displacement vector, i.e., to determine the optimal datum solution of the displacement vector. In order to overcome the main flaw of the IRLS method, namely, the inability to determine the global optimal datum solution of the displacement vector if displaced points appear in the set of datum network points, the application of the GA and GPSO algorithms, which are powerful global optimisation techniques, is proposed for the robust estimation of the displacement vector. A thorough and comprehensive experimental analysis of the proposed modification of the GREDOD method was conducted based on Monte Carlo simulations with the application of the mean success rate (MSR). A comparative analysis of the traditional approach using IRLS, the proposed modification based on the GA and GPSO algorithms and one recent modification of the iterative weighted similarity transformation (IWST) method based on evolutionary optimisation techniques is also presented. The obtained results confirmed the quality and practical usefulness of the presented modification of the GREDOD method, since it increased the overall efficiency by about 18% and can provide more reliable results for projects dealing with the deformation analysis of engineering facilities and parts of the Earth's crust surface.

关键词： robust M estimation robust deformation analysis evolutionary optimisation algorithms Monte Carlo simulations

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A novel high speed multi-objective evolutionary optimisation algorithm 21st

A novel high speed multi-objective evolutionary optimisation...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： De Buck, Viviane Hashem, Ihab Van Impe, Jan Katholieke Univ Leuven BioTeC Chem & Biochem Proc Technol & Control Dept Chem E Leuven Belgium

Multi-objective optimisation problems (MOOPs) consider multiple objectives simultaneously. Solving these problems does not render one unique solution but instead a set of equally optimal solutions, i.e., the Pareto front. The goal of solving a MOOP is to accurately and efficiently approximate the Pareto front. The use of evolutionary optimisation algorithms is widespread in this discipline. During each iteration, parent solutions are combined and mutated to create new offspring solutions. Both populations are subsequently combined and sorted. Only the N fittest solutions of the combined set are selected as the parent solutions for the subsequent iteration. The fitness of a solution is defined by its convergence to the Pareto front and its contribution to the overall solution diversity. Widely used evolutionary algorithms, like NSGA-II (Deb et al., 2002), use non-dominated sorting to assess the convergence of solutions and the concept of crowding distance to ensure a high solution diversity. Both concepts, however, require that all N solutions of the population are compared with all other (N - 1) solutions for both aspects, and this for all M objectives. This results in a computational complexity of O(M N-2). In this contribution, a novel evolutionary algorithm is presented, boasting a significantly lower computational complexity of O(N log(N)). This is achieved by subdividing the feasible space into angular sections. Solutions are scored based on their distance from the current Utopia point and the overall crowdedness of their respective section. Sorting the population based on the attributed scores allows the selection of the N fittest solutions, without having to mutually compare them. Copyright (C) 2020 The Authors.

关键词： evolutionary optimisation algorithms Algorithm development

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Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

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APPLIED MATHEMATICS AND COMPUTATION 2020年 370卷

作者： Hassan, Bryar A. Rashid, Tarik A. Kurdistan Inst Strateg Studies & Sci Res Sulaimani Iraq Univ Kurdistan Hewler Comp Sci & Engn Dept Erbil Iraq Sulaimani Polytech Univ Tech Coll Informat Dept Comp Networks Sulaimani Iraq

Backtracking search optimisation algorithm (BSA) is a commonly used meta-heuristic optimisation algorithm and was proposed by Civicioglu in 2013. When it was first used, it exhibited its strong potential for solving numerical optimisation problems. Additionally, the experiments conducted in previous studies demonstrated the successful performance of BSA and its non-sensitivity toward the several types of optimisation problems. This success of BSA motivated researchers to work on expanding it, e.g., developing its improved versions or employing it for different applications and problem domains. However, there is a lack of literature review on BSA;therefore, reviewing the aforementioned modifications and applications systematically will aid further development of the algorithm. This paper provides a systematic review and meta-analysis that emphasise on reviewing the related studies and recent developments on BSA. Hence, the objectives of this work are two-fold: (i) First, two frameworks for depicting the main extensions and the uses of BSA are proposed. The first framework is a general framework to depict the main extensions of BSA, whereas the second is an operational framework to present the expansion procedures of BSA to guide the researchers who are working on improving it. (ii) Second, the experiments conducted in this study fairly compare the analytical performance of BSA with four other competitive algorithms: differential evolution (DE), particle swarm optimisation (PSO), artificial bee colony (ABC), and firefly (FF) on 16 different hardness scores of the benchmark functions with different initial control parameters such as problem dimensions and search space. The experimental results indicate that BSA is statistically superior than the aforementioned algorithms in solving different cohorts of numerical optimisation problems such as problems with different levels of hardness score, problem dimensions, and search spaces. This study can act as a systematic and m

关键词： Swarm intelligence evolutionary optimisation algorithms Backtracking search optimisation algorithm optimisation problems BSA applications Performance evaluation

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Low complexity scheme for carrier frequency offset estimation in orthogonal frequency division multiple access uplink

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IET COMMUNICATIONS 2013年第13期7卷 1405-1411页

作者： Shah, Hardip K. Dasgupta, Kankar S. Soni, Himanshu Dharmsinh Desai Univ Dept Elect & Commun Engn Nadiad 387001 India Indian Inst Space Sci & Technol Thiruvananthapuram 695547 Kerala India G H Patel Coll Engn & Technol Vallabh Vidyanagar 388120 Gujarat India

Maximum likelihood (ML) carrier-frequency offset estimation for orthogonal frequency-division multiple access uplink is a complex multi-parameter estimation problem. The ML approach is a global optima search problem, which is prohibitive for practical applications because of the requirement of multidimensional exhaustive search for a large number of users. There are a few attempts to reduce the complexity of ML search by applying evolutionary optimisation algorithms. In this study, the authors propose a novel canonical particle swarm optimisation (CPSO)-based scheme, to reduce the computational complexity without compromising the performance and premature convergence. The proposed technique is a two-step process, where, in the first step, low resolution alternating projection frequency estimation (APFE) is used to generate a single better positioned particle for CPSO, followed by an actual CPSO procedure in second step. The mean square error performance of the proposed scheme is compared with existing low complexity algorithms namely APFE and linear particle swarm optimisation with mutation. Simulation results presented in this study show that the new scheme completely avoids premature convergence for a large number of users as high as 32.

关键词： communication complexity convergence frequency division multiple access frequency estimation maximum likelihood estimation OFDM modulation parameter estimation particle swarm optimisation search problems telecommunication links low complexity scheme division multiple access uplink maximum likelihood carrier-frequency offset estimation ML carrier-frequency offset estimation orthogonal frequency-division multiple access uplink complex multiparameter estimation problem ML approach global optima search problem multidimensional exhaustive search evolutionary optimisation algorithms canonical particle swarm optimisation-based scheme CPSO-based scheme computational complexity reduction premature convergence performance convergence low resolution alternating projection frequency estimation APFE single better positioned particle mean square error performance linear particle swarm optimisation completely premature convergence avoidance

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Null steering in irregularly spaced sparse antenna arrays using aperture distributed subarrays and hybrid optimiser

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IET MICROWAVES ANTENNAS & PROPAGATION 2014年第2期8卷 86-92页

作者： Khan, Asim Ali Brown, Anthony Keith COMSATS Inst Informat Technol Dept Elect Engn Lahore Pakistan Univ Manchester Sch Elect & Elect Engn Manchester Lancs England

Sparse antenna arrays with irregularly spaced elements are increasingly being used in large array applications such as radio astronomy and radar. These antenna arrays may consist of hundreds of elements and therefore optimisation of these arrays becomes computationally expensive. This study proposes a novel solution to the problem of null synthesis in such arrays, where nulls are formed in prescribed locations for two-dimensional irregular sparse antenna arrays. The technique is based on a subarray and hybrid synthesis solution, where each subarray spans the entire antenna aperture. Single and multiple null formations with array gain maximisation are considered. The technique's effectiveness is demonstrated using evolutionary optimisation algorithms; however, the basic technique can be used with other optimisation methods. The simulation results presented compare the null synthesis performance with and without subarraying and it has shown that the use of subarray results in improved convergence compared with a direct full array synthesis. Furthermore the technique is appropriate for ready application to parallel computer architectures.

关键词： antenna arrays beam steering evolutionary computation null steering synthesis irregularly spaced sparse antenna arrays hybrid optimiser subarrays radio astronomy radar two-dimensional irregular sparse antenna arrays antenna aperture array gain maximisation multiple null formations parallel computer architectures direct full array synthesis evolutionary optimisation algorithms

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A novel high speed multi-objective evolutionary optimisation algorithm ⁎

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IFAC-PapersOnLine 2020年第2期53卷 6756-6761页

作者： Viviane De Buck Ihab Hashem Jan Van Impe BioTeC+ Chemical and Biochemical Process Technology and Control Department of Chemical Engineering KU Leuven Belgium

Multi-objective optimisation problems (MOOPs) consider multiple objectives simultaneously. Solving these problems does not render one unique solution but instead a set of equally optimal solutions, i.e., the Pareto front. The goal of solving a MOOP is to accurately and efficiently approximate the Pareto front. The use of evolutionary optimisation algorithms is widespread in this discipline. During each iteration, parent solutions are combined and mutated to create new offspring solutions. Both populations are subsequently combined and sorted. Only the N fittest solutions of the combined set are selected as the parent solutions for the subsequent iteration. The fitness of a solution is defined by its convergence to the Pareto front and its contribution to the overall solution diversity. Widely used evolutionary algorithms, like NSGA-II (Deb et al., 2002), use non-dominated sorting to assess the convergence of solutions and the concept of crowding distance to ensure a high solution diversity. Both concepts, however, require that all N solutions of the population are compared with all other ( N — 1) solutions for both aspects, and this for all M objectives. This results in a computational complexity of O(MN 2 ). In this contribution, a novel evolutionary algorithm is presented, boasting a significantly lower computational complexity of O(N log (N)). This is achieved by subdividing the feasible space into angular sections. Solutions are scored based on their distance from the current Utopia point and the overall crowdedness of their respective section. Sorting the population based on the attributed scores allows the selection of the N fittest solutions, without having to mutually compare them.

关键词： evolutionary optimisation algorithms Algorithm development

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