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Maximizing main-lobe to the side-lobe level for the non-uniform chaos game planar antenna array using an IWO algorithm

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INTERNATIONAL JOURNAL OF MICROWAVE AND WIRELESS TECHNOLOGIES 2012年第6期4卷 635-646页

作者： Jabbar, Ahmed Najah Hasooni, Ali Shaban Ibrahim, Muthana Khallil Univ Babylon Dept Elect Engn Hilla Iraq Univ Babylon Collage Eng Dept Elect Hilla Iraq

In this study, we present the implementation of invasive weed optimization (IWO) in the maximization of main-lobe to side-lobe level for the non-uniform planar antenna array. The antenna arrays investigated in this study are generated using the chaos game algorithm (CGA) and shaped into selected fractal geometries chosen on the basis of their interesting performance. This CGA is picked out in order to overcome the limitations found in the fractal arrays. All the attained results are compared with the results produced by a well-known optimization algorithm that is the particle swarm optimization (PSO). In all the optimized arrays, IWO shows superior optimization results compared with PSO.

关键词： IWO Non-uniform planar antenna array Antenna array optimization evolutionary optimization algorithm PSO Chaos game

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evolutionary fuzzy clustering for the classification of transients in nuclear components

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PROGRESS IN NUCLEAR ENERGY 2005年第3-4期46卷 282-296页

作者： Zio, E Baraldi, P Politecn Milan Dept Nucl Engn Milan Italy

The classification of objects or patterns is an important area of research with practical applications in a variety of fields. In this paper we are interested in the classification of signal transients for the reliable monitoring and timely diagnosing of nuclear components and systems. These represent fundamental tasks for the operation, control and accident management of nuclear power plants. The problem is tackled within a fuzzy clustering approach. The choice of the metrics upon which the clustering is based is critical for obtaining geometric clusters in the features space as close as possible to the real physical classes. In this respect, here the a priori known information regarding the true classes to which the objects belong will be exploited to select, by means of an evolutionary algorithm of literature, an optimal metrics for the clustering. In case the classification thereby obtained were still unsatisfactory, an iterative procedure is used to split the less compact physical classes in further subclasses.

关键词： Transient classification fuzzy clustering evolutionary optimization algorithm steam generator

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Using Echolocation Search algorithm (ESA) for truss size optimization

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STEEL AND COMPOSITE STRUCTURES 2022年第6期42卷 855-864页

作者： Nobahari, Mehdi Ghabdiyan, Nafise Islamic Azad Univ Neyshabur Branch Dept Civil Engn Neyshabur Iran Islamic Azad Univ Neyshabur Branch Dept Math Neyshabur Iran

Due to limited resources, and increasing speed of development, the optimal use of available resources has become the most important challenge of human societies. In the last few decades, many researchers have focused their research on solving various optimization problems, providing new optimization methods, and improving the performance of existing optimization methods. Echolocation Search algorithm (ESA) is an evolutionary optimization algorithm that is based on mimicking the mechanism of the animals such as bats, dolphins, oilbirds, etc in food finding to solve optimization problems. In this paper, the ability of ESA for solving truss size optimization problems with continuous variables is investigated. To examine the efficiency of ESA, three benchmark examples are considered. The numerical results exhibit the effectiveness of ESA for solving truss optimization problems.

关键词： ESA evolutionary optimization algorithm structural optimization truss

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Phase-Only Synthesis for Large Planar Arrays via Zernike Polynomials and Invasive Weed optimization

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2022年第3期70卷 1954-1964页

作者： Angeletti, Piero Berretti, Lisa Maddio, Stefano Pelosi, Giuseppe Selleri, Stefano Toso, Giovanni European Space Agcy European Space Res & Technol Ctr ESTEC Radio Frequency Payloads & Technol Div NL-2200 AG Noordwijk Netherlands Inst Natl Sci Appl Rennes INSA Rennes Inst Elect & Technol NumeR IETR UMR CNRS 6164 F-35708 Rennes France Univ Florence Dept Informat Engn I-50139 Florence Italy

A synthesis method for the design of large planar array antennas with phase-only control is here presented. The synthesis is based on the use of Zernike polynomials, as global basis function for the phase, to reduce the number of optimization variables with respect to the number of elements of the array. Invasive weed optimization (IWO) is applied to polynomial coefficients' optimization to circumvent nonlinearity and local trapping issues typical of phase-only problems. The periodicity of the array factor is exploited to reduce the optimization to a Voronoi cell of the grating-lobes' lattice and nonuniform meshing is introduced to best adapt the control stations to the beam shape requirements. The technique is applied to the design of shaped beams for continental coverage from geostationary satellites.

关键词： Phased arrays optimization Lattices Gratings Earth Planar arrays Cost function Beam shaping evolutionary optimization algorithm large phased arrays phase-only synthesis

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evolutionary Generation of Test Suites for Multi-Path Coverage of MPI Programs With Non-Determinism

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2023年第6期49卷 3504-3523页

作者： Sun, Baicai Gong, Dunwei Pan, Feng Yao, Xiangjuan Tian, Tian Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Math Xuzhou 221116 Jiangsu Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Shandong Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Shandong Jianzhu Univ Sch Comp Sci & Technol Jinan 250101 Shandong Peoples R China

When a large number of target paths in a sequential program need to be covered, we can divide similar target paths into the same group, and generate a test suite covering the same group of target paths at the same time, so as to reduce the testing cost. However, different communication edges may be run under a same test input when executing a Message-Passing Interface (MPI) program with non-determinism, which cause different code fragments may be traversed, indicating the difficulty of generating a test suite to cover each group of target paths. This paper proposes an approach to evolutionary generation of test suites for multi-path coverage of MPI programs with non-determinism, which can significantly reduce the testing cost and difficulty. We first design an indicator for evaluating each traversal set of communication edges, which is used to form a relation matrix between each target path and each traversal set of communication edges, so as to divide all the target paths into a certain amount of groups. Then, we construct an optimization model for test suite generation associated with each group. Finally, an evolutionary optimization algorithm is extended to solve each model, and used to generate a test suite covering each group of target paths. The proposed approach is utilized and compared with several state-of-the-art approaches to seven benchmark MPI programs, as well as the experimental results illustrate that the proposed approach can efficiently generate a test suite, thus supporting the superiority of the proposed approach.

关键词： A large number of target paths evolutionary optimization algorithm multi-path coverage MPI program with non-determinism test suite generation

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Optimizing PGRs for in vitro shoot proliferation of pomegranate with bayesian-tuned ensemble stacking regression and NSGA-II: a comparative evaluation of machine learning models

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PLANT METHODS 2024年第1期20卷 82页

作者： Zarbakhsh, Saeedeh Shahsavar, Ali Reza Soltani, Mohammad Shiraz Univ Coll Agr Fac Agr Dept Hort Sci Shiraz Iran

Background The process of optimizing in vitro shoot proliferation is a complicated task, as it is influenced by interactions of many factors as well as genotype. This study investigated the role of various concentrations of plant growth regulators (zeatin and gibberellic acid) in the successful in vitro shoot proliferation of three Punica granatum cultivars ('Faroogh', 'Atabaki' and 'Shirineshahvar'). Also, the utility of five Machine Learning (ML) algorithms-Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGB), Ensemble Stacking Regression (ESR) and Elastic Net Multivariate Linear Regression (ENMLR)-as modeling tools were evaluated on in vitro multiplication of pomegranate. A new automatic hyperparameter optimization method named Adaptive Tree Pazen Estimator (ATPE) was developed to tune the hyperparameters. The performance of the models was evaluated and compared using statistical indicators (MAE, RMSE, RRMSE, MAPE, R and R2), while a specific Global Performance Indicator (GPI) was introduced to rank the models based on a single parameter. Moreover, Non-dominated Sorting Genetic algorithm-II (NSGA-II) was employed to optimize the selected prediction *** The results demonstrated that the ESR algorithm exhibited higher predictive accuracy in comparison to other ML algorithms. The ESR model was subsequently introduced for optimization by NSGA-II. ESR-NSGA-II revealed that the highest proliferation rate (3.47, 3.84, and 3.22), shoot length (2.74, 3.32, and 1.86 cm), leave number (18.18, 19.76, and 18.77), and explant survival (84.21%, 85.49%, and 56.39%) could be achieved with a medium containing 0.750, 0.654, and 0.705 mg/L zeatin, and 0.50, 0.329, and 0.347 mg/L gibberellic acid in the 'Atabaki', 'Faroogh', and 'Shirineshahvar' cultivars, *** This study demonstrates that the 'Shirineshahvar' cultivar exhibited lower shoot proliferation success compared to the other cultivars. The results indicated the go

关键词： Hyperparameter tuning Machine learning evolutionary optimization algorithm Plant growth regulators Pomegranate in vitro propagation

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Effective Constrained Handling by Hybridized Cuckoo Search algorithm with Box Complex Method

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IFAC-PapersOnLine 2017年第2期50卷 209-214页

作者： Zade, Anita Patel, Narendra Padhiyar, Nitin Indian Institute of Technology Gandhinagar GandhinagarGujarat382355 India Vishwakarma Government Engineering College AhmedabadGujarat382424 India

Cuckoo search algorithm (CSA) is an evolutionary optimization algorithm, which requires an additional constraint handling mechanism. While CSA has been successfully applied for exploring the search space, it lacks good convergence property near the optimum point. On the other hand Box Complex method (BCM) has good local convergence property. This paper presents a hybrid cuckoo search algorithm (CSA) with effective constraint handling mechanism by Box Complex method (BCM). Each infeasible member is processed using BCM by projecting it through the centroid of a fixed number of feasible members. This hybridization approach provides good convergence rate along with an effective constraint handling technique. To demonstrate the efficacy of the proposed algorithm, a comparative study with the other popular algorithms has been presented using eleven test problems and an optimal grade transition problem. © 2017

关键词： evolutionary algorithms Constrained optimization Learning algorithms Complex methods Constraint handling Convergence properties Cuckoo search algorithms Effective constraints evolutionary optimization algorithm Optimal grade transition PMMA

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Symmetric optical multipass matrix systems and the general rapid design methodology

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HELIYON 2024年第15期10卷 e34682页

作者： Xiao, Xiangjun Shi, Miyun Qiu, Jingjing Ou, Xue Liu, Peng Zhou, Xin Beijing Normal Univ Ctr Adv Quantum Studies Dept Phys Appl Opt Beijing Area Major Lab Beijing 100875 Peoples R China

We proposed an original type of multipass cell named symmetric optical multipass matrix system (SMMS), in which the same matrix patterns of various sizes can be formed on both sides. According to its special symmetric configurations, the SMMS design problem is modeled as a variant of the classical traveling salesman problem, which can be rapidly solved by evolutionary optimization algorithms. Two sets of 3-mirror SMMSs are designed, analyzed and built, which show superior characteristics of high stability, desirable beam quality and adjustable optical path lengths. Additionally, they can support simultaneous detection of multiple species with multi-laser channels. The proposed method is further extended to design a 4-mirror SMMS, which verifies the universality and robustness of the design methodology. The experimental observations are in consistent with the theoretical calculations. The newly proposed SMMSs have a broad application prospect in trace gas measurement.

关键词： Multipass matrix systems evolutionary optimization algorithm Optical path length

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Cuckoo Search optimization Based design of linear phase FIR filters: A comparison approach

Cuckoo Search Optimization Based design of linear phase FIR ...

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International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE)

作者： Mallick, Arijit Dutta, Souradeep Roy, Sourya Kumar, Utkarsh Chattopadhyay, Arka Chakraborty, Debayan Nag, Sayak Jadavpur Univ Dept Instrumentat & Elect Engn Kolkata India

ISBN: (纸本)9781479949816

optimization algorithms has been used to solve many design problems in science and engineering;notable in image processing, construction design, path planning and many other application. In this paper the method of designing a linear phase symmetric FIR (Finite Impulse Response) filter with the help of Cuckoo Search optimization (CSO) algorithm has been highlighted. Since evolutionary optimization algorithms are more powerful than its linear counterpart, CSO has been deployed in order to find out the coefficients of the required filter for a given problem requirement. Even order linear phase symmetric FIR filter are best suitable for this kind of design procedure. The best nests selected by the CSO denotes the particular coefficient set for a given particular even order filter. A low pass filter is shown as a design example. Comparisonal analysis with respect to other strong evolutionary algorithm is also presented in this paper.

关键词： FIR CSO linear phase evolutionary optimization algorithm even order symmetric filter

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Multi-Verse optimization of Multilayer Perceptrons (MV-MLPs) for Efficient Modeling and Forecasting of Crude Oil Prices Data

Multi-Verse Optimization of Multilayer Perceptrons (MV-MLPs)...

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International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： Nayak, Sarat Chandra Dash, Ch Sanjeev Kumar Mishra, Bijan Bihari Dehuri, Satchidananda CMR Coll Engn & Technol Dept Comp Sci & Engn Hyderabad 501401 India Silicon Inst Technol Dept Comp Sci Bhubaneswar India Fakir Mohan Univ Dept Informat & Commun Technol Balasore 756019 Odisha India

ISBN: (纸本)9783030390334;9783030390327

Arbitrary changes in crude oil prices makes its forecasting quite difficult. Multilayer neural networks are found to be effective in predicting such cured oil prices. Crafting optimal neural network architecture requires numerous trial and error methods. This article presents a hybrid model based on multi-verse optimization (MVO) of multilayer perceptron (MLP), termed as (MV-MLP), where a universe/individual of MVO represents a potential MLP in the universe of discourse. A set of such universes forms a population and the best universe, i.e. optimal MLP is selected through a search process. The search process starts with a random population, gradually moves toward the global optimum and the optimal MLP is obtained at fly rather fixing it earlier. The proposed MV-MLP is evaluated on forecasting the crude oil prices and the predictability performance is established through comparative study with other trained models trained. Experimental results and comparative study suggests the superiority of MV-MLP based forecasting.

关键词： Crude oil price forecasting Multi-verse optimizer Multilayer perceptron Artificial neural network evolutionary optimization algorithm

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