A Novel Optimization Method for Bipolar Chaotic Toeplitz Measurement Matrix in Compressed Sensing

In this paper, a bipolar chaotic Toeplitz measurement matrix optimization algorithm for alternating optimization is presented. The construction of measurement matrices is one of the key techniques for compressive sensing from theory to engineering applications. Recent studies have shown that bipolar chaotic Toeplitz matrices, constructed by combining the intrinsic determinism of bipolar chaotic sequences with the advantages of Toeplitz matrices, have significant advantages over other measurement matrices in terms of memory overhead, computational complexity, and hard implementation. However, problems such as strong correlation and large interdependence coefficients between measurement matrices and sparse dictionaries may still exist in practical applications. To address this problem, we propose a new bipolar chaotic Toeplitz measurement matrix alternating optimization algorithm. Firstly, by introducing the structure matrix, the optimization problem of the measurement matrix is transformed into the optimization problem of the generating sequence, thus ensuring that the optimization process does not destroy the structural properties of the matrix; then, constraints are added to the values of the generating sequence during the optimization process, so that the optimized measurement matrix still maintains the bipolar properties. Finally, the effectiveness of the optimization algorithm in this paper is verified by simulation experiments. The experimental results show that the optimized bipolar chaotic Toeplitz measurement matrix can effectively reduce the reconstruction error and improve the reconstruction probability.

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Application of Parallel Chaos Optimization Algorithm for Plug-in Hybrid Electric Vehicle Design

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127414500011 ◽

2014 ◽

Vol 24 (01) ◽

pp. 1450001 ◽

Cited By ~ 3

Author(s):

Xiaolan Wu ◽

Guifang Guo ◽

Jun Xu ◽

Binggang Cao

Keyword(s):

Optimization Algorithm ◽

Control Strategy ◽

Hybrid Electric Vehicle ◽

Optimization Problem ◽

Optimization Method ◽

Vehicle Performance ◽

Chaos Optimization ◽

Performance Requirements ◽

Chaos Optimization Algorithm ◽

Hybrid Electric

Plug-in hybrid electric vehicles (PHEVs) have been offered as alternatives that could greatly reduce fuel consumption relative to conventional vehicles. A successful PHEV design requires not only optimal component sizes but also proper control strategy. In this paper, a global optimization method, called parallel chaos optimization algorithm (PCOA), is used to optimize simultaneously the PHEV component sizes and control strategy. In order to minimize the cost, energy consumption (EC), and emissions, a multiobjective nonlinear optimization problem is formulated and recast as a single objective optimization problem by weighted aggregation. The driving performance requirements of the PHEV are considered as the constraints. In addition, to evaluate the objective function, the optimization process is performed over three typical driving cycles including Urban Dynamometer Driving Schedule (UDDS), Highway Fuel Economy Test (HWFET), and New European Driving Cycle (NEDC). The simulation results show the effectiveness of the proposed approach for reducing the fuel cost, EC and emissions while ensuring that the vehicle performance has not been sacrificed.

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Shuffled shepherd optimization method: a new Meta-heuristic algorithm

Engineering Computations ◽

10.1108/ec-10-2019-0481 ◽

2020 ◽

Vol 37 (7) ◽

pp. 2357-2389 ◽

Cited By ~ 4

Author(s):

Ali Kaveh ◽

Ataollah Zaerreza

Keyword(s):

Heuristic Algorithm ◽

Optimization Algorithm ◽

Design Methodology ◽

Optimization Method ◽

Optimization Process ◽

Heuristic Optimization ◽

Benchmark Problems ◽

Content Type

Purpose This paper aims to present a new multi-community meta-heuristic optimization algorithm, which is called shuffled shepherd optimization algorithm (SSOA). In this algorithm. Design/methodology/approach The agents are first separated into multi-communities and the optimization process is then performed mimicking the behavior of a shepherd in nature operating on each community. Findings A new multi-community meta-heuristic optimization algorithm called a shuffled shepherd optimization algorithm is developed in this paper and applied to some attractive examples. Originality/value A new metaheuristic is presented and tested with some classic benchmark problems and some attractive structures are optimized.

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Optimization of Radial Cams with Translating Roller Follower through Genetic Algorithms

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.783.83 ◽

2015 ◽

Vol 783 ◽

pp. 83-94

Author(s):

Alberto Borboni

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Contact Pressure ◽

Optimization Problem ◽

Optimization Method ◽

Optimization Process ◽

Radius Of Curvature ◽

Design Problems ◽

Computational Optimization ◽

Different Types

In this work, the optimization problem is studied for a planar cam which rotates around its axis and moves a centered translating roller follower. The proposed optimization method is a genetic algorithm. The paper deals with different design problems: the minimization of the pressure angle, the maximization of the radius of curvature and the minimization of the contact pressure. Different types of motion laws are tested to found the most suitable for the computational optimization process.

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Assignment and layout integration optimization for simplified satellite re-entry module component layout

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410017704220 ◽

2017 ◽

Vol 233 (12) ◽

pp. 4287-4301 ◽

Cited By ~ 4

Author(s):

Zhi-Zheng Xu ◽

Chong-Quan Zhong ◽

Hong-Fei Teng

Keyword(s):

Evolutionary Algorithm ◽

Optimization Model ◽

Optimization Problem ◽

Optimization Method ◽

Layout Design ◽

Layout Optimization ◽

Optimization Process ◽

Supporting Surface ◽

Computational Performance ◽

Component Layout

Previous studies of satellite module component (equipment) layout optimization usually initialized a component assignment in the initialization stage, which kept constant in following optimization process. The invariable component assignment will restrict the further improvement in layout optimization. To overcome this deficiency, an assignment and layout integration optimization method is presented for multi-module or supporting surface satellite module component layout design. The assignment and layout integration optimization model and the component reassignment model are built. The component reassignment model is solved by algorithms with new heuristic rule, and the integration optimization model itself is solved by evolutionary algorithm. The purpose of this article is to improve the computational performance of algorithms for multi-module or supporting surface satellite module component layout optimization. The proposed method is applied to a simplified satellite re-entry module component layout optimization problem to illustrate its effectiveness.

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Gradient-based optimization method for interference suppression of linear arrays by the amplitude-only and phase-only control

International Journal of Microwave and Wireless Technologies ◽

10.1017/s1759078721001355 ◽

2021 ◽

pp. 1-7

Author(s):

Renjing Gao ◽

Yi Tang ◽

Qi Wang ◽

Shutian Liu

Keyword(s):

Optimization Algorithm ◽

Optimization Problem ◽

Optimization Problems ◽

Search Algorithm ◽

Interference Suppression ◽

Computation Time ◽

Optimization Method ◽

Main Beam ◽

Linear Arrays ◽

Gradient Based

Abstract This paper presents a gradient-based optimization method for interference suppression of linear arrays by controlling the electrical parameters of each array element, including the amplitude-only and phase-only. Gradient-based optimization algorithm (GOA), as an efficient optimization algorithm, is applied to the optimization problem of the anti-interference arrays that is generally solved by the evolutionary algorithms. The goal of this method is to maximize the main beam gain while minimizing the peak sidelobe level (PSLL) together with the null constraint. To control the nulls precisely and synthesize the radiation pattern accurately, the full-wave method of moments is used to consider the mutual coupling among the array elements rigorously. The searching efficiency is improved greatly because the gradient (sensitivity) information is used in the algorithm for solving the optimization problem. The sensitivities of the design objective and the constraint function with respect to the design variables are analytically derived and the optimization problems are solved by using GOA. The results of the GOA can produce the desired null at the specific positions, minimize the PSLL, and greatly shorten the computation time compared with the often-used non-gradient method such as genetic algorithm and cuckoo search algorithm.

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Ultrasonic Transducer Design via APDL Optimization

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.34-35.1076 ◽

2010 ◽

Vol 34-35 ◽

pp. 1076-1081

Author(s):

Xiang Hui Zhang ◽

Gui Hua Li

Keyword(s):

Vibration Amplitude ◽

Optimization Problem ◽

Ultrasonic Transducer ◽

Optimization Method ◽

Continuous Variables ◽

Discrete Variables ◽

Ultrasonic Machine ◽

Practical Applications ◽

Design Variables ◽

Transducer Design

The design of ultrasonic transducer is used to achieve the desired value of terminal vibration amplitude. However, in many practical applications of ultrasonic transducer, the maximum vibration amplitude failed to achieve that value such as ultrasonic machine and bonding. For that matter, the design of ultrasonic transducer used APDL optimization method. In this paper the optimal design of a sandwich ultrasonic transducer is investigated. The design problem is formulated mathematically as a constrained single-objective optimization problem. The maximum vibration amplitude is considered as optimization objective. Design variables involve continuous variables and discrete variables. What is more, the behavior of ultrasonic transducer is modeled using ANSYS based on models of the transfer matrix method. In this paper, the optimized results are analyzed and the vibration amplitude is determined.

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Size/Layout Optimization of Truss Structures Using Shuffled Shepherd Optimization Method

Periodica Polytechnica Civil Engineering ◽

10.3311/ppci.15726 ◽

2020 ◽

Cited By ~ 1

Author(s):

Ali Kaveh ◽

Ataollah Zaerreza

Keyword(s):

Optimization Algorithm ◽

Heuristic Algorithms ◽

Optimization Method ◽

Layout Optimization ◽

Optimization Process ◽

Truss Structures ◽

Heuristic Optimization

The main purpose of this paper is to investigate the ability of the recently developed multi-community meta-heuristic optimization algorithm, shuffled shepherd optimization algorithm (SSOA), in layout optimization of truss structures. The SSOA is inspired by mimicking the behavior of shepherd in nature. In this algorithm, agents are first divided into communities which are called herd and then optimization process, inspired by the shepherd’s behavior in nature, is operated on each community. The new position of agents is obtained using elitism technique. Then communities are merged for sharing the information. The results of SSOA in layout optimization show that SSOA is competitive with other considered meta-heuristic algorithms.

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Finding Multiple Solutions of Multimodal Optimization Using Spiral Optimization Algorithm with Clustering

MENDEL ◽

10.13164/mendel.2017.1.095 ◽

2017 ◽

Vol 23 (1) ◽

pp. 95-102

Author(s):

Kuntjoro Adji Sidarto ◽

Adhe Kania ◽

Novriana Sumarti

Keyword(s):

Optimization Algorithm ◽

Differential Form ◽

Optimization Problem ◽

Heuristic Algorithms ◽

Optimization Problems ◽

Linear Equations ◽

Multimodal Optimization ◽

Practical Applications ◽

All Solutions ◽

Non Linear Equations

Multimodal optimization is one of the interesting problems in optimization which arises frequently in a widerange of engineering and practical applications. The goal of this problem is to find all of optimum solutions in a single run. Some algorithms fail to find all solutions that have been proven their existence analytically. In our paper [1], a method is proposed to find the roots of a system of non-linear equations using a clustering technique that combine with Spiral Optimization algorithm and Sobol sequence of points. An interesting benefit using this method is that the same inputs will give the same results. Most of the time this does not happen in meta-heuristic algorithms using random factors. Now the method is modified to find solutions of multimodal optimization problems. Generally in an optimization problem, the differential form of the objective function is needed. In this paper, the proposed method is to find optimum points of general multimodal functions that its differential form is not required. Several problems with benchmark functions have been examined using our method and they give good result.

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An improved 3DMax algorithm to reconstruct the three-dimensional structure of the chromosome

10.1101/2020.07.09.195693 ◽

2020 ◽

Author(s):

Liwei Liu ◽

Huili Yao

Keyword(s):

Objective Function ◽

Optimization Algorithm ◽

Optimization Problem ◽

3D Structure ◽

Three Dimensional ◽

Optimization Method ◽

Dimensional Structure ◽

Chromosome Conformation ◽

Three Dimensional Structure ◽

Interaction Map

AbstractIn recent years, with the development of high-throughput chromosome conformation capture (Hi-C) technology and the reduction of high-throughput sequencing cost, the data volume of whole-genome interaction has increased rapidly, and the resolution of interaction map keeps improving. Great progress has been made in the research of 3D structure modeling of chromosomes and genomes. Several methods have been proposed to construct the chromosome structure from chromosome conformation capture data. Based on the Hi-C data, this paper analyses the relevant literature of chromosome 3D structure reconstruction and it summarizes the principle of 3DMAX, which is a classical algorithm to construct the 3D structure of a chromosome. In this paper, we introduce a new gradient ascent optimization algorithm called XNadam that is a variant of Nadam optimization method. When XNadam is applied to 3DMax algorithm, the performance of 3DMax algorithm can be improved, which can be used to predict the three-dimensional structure of a chromosome.Author summaryThe exploration of the three-dimensional structure of chromosomes has gradually become a necessary means to understand the relationship between genome function and gene regulation. An important problem in the construction of three-dimensional model is how to use the interaction map. Usually, the interaction frequency can be transformed into the spatial distance according to the deterministic or non-deterministic function relationship, and the interaction frequency can be weighted as weight in the objective function of the optimization problem. When the frequency of interaction is weighted as weight in the objective function of the optimization problem, what kind of optimization method is used to optimize the objective function is the problem we consider. In order to solve this problem, we provide an improved stochastic gradient ascent optimization algorithm(XNadam). The XNadam optimization algorithm combined with maximum likelihood algorithm is applied to high resolution Hi-C data set to infer 3D chromosome structure.

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