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Estimation of open channels hydraulic parameters with the stochastic particle collision algorithm

JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2014年第1期36卷 69-77页

作者： Gonzalez, Yoel Martinez Martinez Rodriguez, Jose Bienvenido da Silva Neto, Antonio Jose Gomes Watts Rodrigues, Pedro Paulo Inst Super Politecn Jose Antonio Echeverria Havana Cuba Univ Estado Rio de Janeiro Inst Politecn BR-28630050 Rio De Janeiro Brazil Univ Estado Rio de Janeiro Inst Politecn Dept Computat Modelling BR-28616970 Rio De Janeiro Brazil

In the present work, the 1D flow and transport equations for open channels are numerically solved and coupled to a recently developed global search optimization, the particle collision algorithm (PCA), to estimate two essential parameters present in flow and transport equations, respectively, the bed roughness and the dispersion coefficient. The PCA is inspired in the scattering and absorption phenomena of a given incident nuclear particle by a target nucleus. In this method, if the particle in a given location of the design space reaches a low value of the objective function, it is absorbed, otherwise, it is scattered. This allows the search space to be widely explored, in such a way that the most promising regions are searched through successive scattering and absorption events. Based on real data measured in the Albear channel, Cuba, the bed roughness and longitudinal dispersion coefficient were successfully estimated from two numerical experiments dealing, respectively, with flow and transport equations. The results obtained were supported by the high correlations achieved between simulations and observations, demonstrating the feasibility of the approach here considered.

关键词： particle collision algorithm Inverse problem Parameter estimation Flow and transport equations Bed roughness Dispersion coefficient Albear channel

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Physics-based search and optimization: Inspirations from nature

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EXPERT SYSTEMS 2016年第6期33卷 607-623页

作者： Siddique, Nazmul Adeli, Hojjat Univ Ulster Sch Comp & Intelligent Syst Coleraine Londonderry North Ireland Ohio State Univ Dept Biomed Engn Columbus OH 43210 USA Ohio State Univ Dept Biomed Informat Columbus OH 43210 USA Ohio State Univ Dept Civil Environm & Geodet Engn Columbus OH 43210 USA Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA Ohio State Univ Dept Neurosci Columbus OH 43210 USA Ohio State Univ Dept Neurol Columbus OH 43210 USA

This paper presents a review of recently developed physics-based search and optimization algorithms that have been inspired by natural phenomena. They include Big Bang-Big Crunch, black hole search, galaxy-based search, artificial physics optimization, electromagnetism optimization, charged system search, colliding bodies optimization, and particle collision algorithm.

关键词： Artificial physics optimization Big Bang-Big Crunch search black hole search charged system search colliding bodies optimization electromagnetism optimization galaxy-based search particle collision algorithm physics-based algorithm

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Radiative Properties Estimation with the Luus-Jaakola and the particle collision algorithm

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CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES 2009年第2期54卷 121-145页

作者： Knupp, D. C. Silva Neto, A. J. Sacco, W. F. ANTT Rio De Janeiro Brazil Univ Fed Rio de Janeiro COPPE BR-21945 Rio De Janeiro Brazil Univ Estado Rio de Janeiro IPRJ Inst Politecn Dept Mech Engn & Energy Nova Friburgo RJ Brazil UFOPA Santarem PA Brazil

The inverse analysis of radiative transfer in participating media has several practical applications. In most cases, the inverse problem is formulated implicitly and the solution is given by the minimization of an objective function. Gradient based methods have largely been used for that purpose, but it has been observed in recent years an increasing interest in the use of stochastic methods. In this work, it is proposed the use of the Luus-Jaakola method and the particle collision algorithm. The former is a random search optimization method that has been successfully employed mainly in chemical engineering, and the latter is a novel stochastic method inspired by the physics of the interaction of nuclear particles inside nuclear reactors. The solutions obtained with these methods are analyzed and compared for different test cases.

关键词： Radiative Transfer Inverse Problems Luus-Jaakola particle collision algorithm

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Hybridization Multi-Neighbourhood particle collision algorithm and Great Deluge for Solving Course Timetabling Problems

Hybridization Multi-Neighbourhood Particle Collision Algorit...

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2nd Conference on Data Mining and Optimization

作者： Abuhamdah, Anmar Ayob, Masri Univ Kebangsaan Malaysia Data Min & Optimizat Grp DMO CAIT Dept Comp Sci Ukm Bangi 43600 Selangor Malaysia

ISBN: (纸本)9781424449446

This work presents a particle collision algorithm (PCA) to solve university course timetabling problems. The aim is to produce an effective algorithm for assigning a set of courses, lecturers and students to a specific number of rooms and timeslots, subject to a set of constraints. The structure of PCA resembles a simulated annealing structure. The basic difference is that PCA does not have cooling schedule and it does not rely on user-defined parameters. PCA differs from Simulated Annealing and other meta-heuristic approaches where, before accepting the trial solution (although we obtain good-quality solution). Therefore, PCA is capable of escaping from local optima. The Hybrid Multi-Neighbourhood particle collision algorithm with Great Deluge using Composite Neighbourhood Structure (HPCA), which it is hybridize the Great Deluge acceptance criterion with PCA and enhances a PCA approach that was originally introduced by Sacco for policy optimization. HPCA differs from basic PCA in terms of applying multi-neighbourhood composite structures, which is divided into two stages, one in the solution construction phase and the other in the improvement phase. HPCA also differs from basic PCA in terms of accepting the worst solution in the scattering phase, which is hybrid the Great Deluge acceptance criterion with PCA. HPCA attempts to further enhance the trial solution by exploring different neighbourhood structures. Results tested on Socha benchmark datasets show that HPCA is able to produce significantly good quality solutions within a reasonable time and outperformed some other approaches in some instances.

关键词： Course Timetabling Problem particle collision algorithm Great Deluge

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Multi-Neighbourhood particle collision algorithm for Solving Course Timetabling Problems

Multi-Neighbourhood Particle Collision Algorithm for Solving...

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2nd Conference on Data Mining and Optimization

作者： Abuhamdah, Anmar Ayob, Masri Univ Kebangsaan Malaysia Data Min & Optimizat Grp DMO CAIT Dept Comp Sci Ukm Bangi 43600 Selangor Malaysia

ISBN: (纸本)9781424449446

This work presents a particle collision algorithm (PCA) to solve university course timetabling problems. The aim is to produce an effective algorithm for assigning a set or courses, lecturers and students to a specific number of rooms and timeslots, subject to a set of constraints. PCA always accepts improved solution but adaptively accepts worse solutions based on the quality of the solution. PCA differs from Simulated Annealing and other meta-heuristic approaches in that is does not have cooling schedule and does not rely on user-defined parameters. The Multi-Neighbourhood collision algorithm (MPCA) enhances a PCA approach that was originally introduced by Sacco for policy optimization. The structure of PCA resembles the simulated annealing structure. It differs from basic PCA in terms of improvement phase (Exploration phase). PCA perform local search in both construction solution phase and scattering phase, while MPCA perform local search (Hill Climbing based search) in the scattering phase only, which makes MPCA more capable than PCA of escaping from local optima. Also MPCA differs in terms of applying multi-neighbourhood structures. MPCA employ different neighbourhood in two stages (construction solution stage and improvement stage). We evaluate the effectiveness of MPCA on standard benchmark course timetabling datasets which were introduced by Socha. Results show that MPCA significantly outperformed other approaches in some instances and that MPCA is able to produce good quality solutions within a reasonable time.

关键词： Course Timetabling Problem Meta-Heuristics particle collision algorithm

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Generalised multi-scale image reconstruction algorithm for electrical capacitance tomography

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IMAGING SCIENCE JOURNAL 2011年第3期59卷 134-153页

作者： Lei, J. Liu, S. Wang, X. Y. N China Elect Power Univ Key Lab Power Syst Protect & Dynam Secur Monitori Minist Educ Beijing 102206 Peoples R China Chinese Acad Sci Inst Engn Thermophys Beijing Peoples R China

Successful applications of electrical capacitance tomography (ECT) depend mainly on the precision and speed of the image reconstruction algorithms. In this paper, based on the wavelet multi-scale analysis method, a generalised multi-scale model of considering the inaccuracy of the capacitance data and reconstruction model is proposed, in which the original inverse problem is decomposed into a sequence of inverse problems based on the scale variables and then solved successively from the largest scale to the smallest scale until the solution of the original inverse problem is found. A generalised multi-scale objective functional, which has been developed using the least trimmed squares (LTS) estimation and theM-estimation, is proposed. This objective functional unifies the regularised LTS estimation, the regularised M-estimations, the regularised least squares (LS) estimation, the regularised combinational estimation of the LTS estimation and the M-estimations, the regularised combinational estimation of the LS estimation and the M-estimations into a concise formulation. An efficient solver, which integrates the beneficial advantages of the homotopy algorithm, the harmony search (HS) algorithm that has been developed using the multi-harmony techniques based on the cooperation of solutions, and the particle collision (PC) algorithm, is designed for searching a possible global optimal solution. The proposed algorithm is tested by six typical reconstruction objects using a 12-electrode square sensor. Numerical results show the efficiency and superiority of the proposed algorithm in solving ECT image reconstruction problem. In the cases considered in this paper, good results that show great improvement in the spatial resolution and accuracy are observed. The spatial resolution of the reconstructed images by the proposed algorithm is enhanced and the artefacts in the reconstructed images can be eliminated effectively. Meanwhile, the reconstructed results derived from the

关键词： electrical capacitance tomography semiparametric model wavelet analysis homotopy method harmony search algorithm particle collision algorithm

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MPCA-ARDA for Solving Course Timetabling Problems

MPCA-ARDA for Solving Course Timetabling Problems

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3rd Conference on Data Mining and Optimization (DMO)/1st Multi Conference on Artificial Intelligence Technology (MCAIT)

作者： Abuhamdah, Anmar Ayob, Masri Univ Kebangsaan Malaysia Dept Comp Sci Ctr Artificial Intelligent CAIT Data Min & Optimizat Grp DMO Bangi 43600 Malaysia

ISBN: (纸本)9781612842127

This work presents a hybridization between Multi-Neighborhood particle collision algorithm (MPCA) and Adaptive Randomized Descent algorithm (ARDA) acceptance criterion to solve university course timetabling problems. The aim of this work is to produce an effective algorithm for assigning a set of courses, lecturers and students to a specific number of rooms and timeslots, subject to a set of constraints. The structure of the MPCA-ARDA resembles a Hybrid particle collision algorithm (HPCA) structure. The basic difference is that MPCA-ARDA hybridize MPCA and ARDA acceptance criterion, whilst HPCA, hybridize MPCA and great deluge acceptance criterion. In other words, MPCA-ARDA employ adaptive acceptance criterion, whilst HPCA, employ deterministic acceptance criterion. Therefore, MPCA-ARDA has better capability of escaping from local optima compared to HPCA and MPCA. MPCA-ARDA attempts to enhance the trial solution by exploring different neighborhood structures to overcome the limitation in HPCA and MPCA. Results tested on Socha benchmark datasets show that, MPCA-ARDA is able to produce significantly good quality solutions within a reasonable time and outperformed some other approaches in some instances.

关键词： Course Timetabling Problem particle collision algorithm Adaptive Randomized Descent algorithm

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Generalised multi-scale image reconstruction algorithm for electrical capacitance tomography

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The Journal of Photographic Science 2011年第3期59卷 134-153页

Successful applications of electrical capacitance tomography (ECT) depend mainly on the precision and speed of the image reconstruction algorithms. In this paper, based on the wavelet multi-scale analysis method, a generalised multi-scale model of considering the inaccuracy of the capacitance data and reconstruction model is proposed, in which the original inverse problem is decomposed into a sequence of inverse problems based on the scale variables and then solved successively from the largest scale to the smallest scale until the solution of the original inverse problem is found. A generalised multi-scale objective functional, which has been developed using the least trimmed squares (LTS) estimation and the M-estimation, is proposed. This objective functional unifies the regularised LTS estimation, the regularised M-estimations, the regularised least squares (LS) estimation, the regularised combinational estimation of the LTS estimation and the M-estimations, the regularised combinational estimation of the LS estimation and the M-estimations into a concise formulation. An efficient solver, which integrates the beneficial advantages of the homotopy algorithm, the harmony search (HS) algorithm that has been developed using the multi-harmony techniques based on the cooperation of solutions, and the particle collision (PC) algorithm, is designed for searching a possible global optimal solution. The proposed algorithm is tested by six typical reconstruction objects using a 12-electrode square sensor. Numerical results show the efficiency and superiority of the proposed algorithm in solving ECT image reconstruction problem. In the cases considered in this paper, good results that show great improvement in the spatial resolution and accuracy are observed. The spatial resolution of the reconstructed images by the proposed algorithm is enhanced and the artefacts in the reconstructed images can be eliminated effectively. Meanwhile, the reconstructed results derived from the

关键词： electrical capacitance tomography semiparametric model wavelet analysis homotopy method harmony search algorithm particle collision algorithm

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