scholarly journals Global Minimization for Generalized Polynomial Fractional Program

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xue-Ping Hou ◽  
Pei-Ping Shen ◽  
Chun-Feng Wang
Keyword(s):  
Optimization Problem ◽  
Optimal Solution ◽  
Efficient Global Optimization ◽  
Global Optimization Algorithm ◽  
Global Optimal Solution ◽  
Program Problem ◽  
Fractional Program ◽  
Generalized Polynomial ◽  
Branch And Bound Search ◽  
Global Optimal

This paper is concerned with an efficient global optimization algorithm for solving a kind of fractional program problem(P), whose objective and constraints functions are all defined as the sum of ratios generalized polynomial functions. The proposed algorithm is a combination of the branch-and-bound search and two reduction operations, based on an equivalent monotonic optimization problem of(P). The proposed reduction operations specially offer a possibility to cut away a large part of the currently investigated region in which the global optimal solution of(P)does not exist, which can be seen as an accelerating device for the solution algorithm of(P). Furthermore, numerical results show that the computational efficiency is improved by using these operations in the number of iterations and the overall execution time of the algorithm, compared with other methods. Additionally, the convergence of the algorithm is presented, and the computational issues that arise in implementing the algorithm are discussed. Preliminary indications are that the algorithm can be expected to provide a practical approach for solving problem(P)provided that the number of variables is not too large.

scholarly journals Prototype Filter Design Based on Channel Estimation for FBMC/OQAM Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yongjin Liu ◽  
Xihong Chen ◽  
Yu Zhao
Keyword(s):  
Genetic Algorithm ◽  
Channel Estimation ◽  
Optimization Problem ◽  
Filter Design ◽  
Stop Band ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Global Optimal Solution ◽  
Prototype Filter ◽  
Global Optimal

A prototype filter design for FBMC/OQAM systems is proposed in this study. The influence of both the channel estimation and the stop-band energy is taken into account in this method. An efficient preamble structure is proposed to improve the performance of channel estimation and save the frequency spectral efficiency. The reciprocal of the signal-to-interference plus noise ratio (RSINR) is derived to measure the influence of the prototype filter on channel estimation. After that, the process of prototype filter design is formulated as an optimization problem with constraint on the RSINR. To accelerate the convergence and obtain global optimal solution, an improved genetic algorithm is proposed. Especially, the History Network and pruning operator are adopted in this improved genetic algorithm. Simulation results demonstrate the validity and efficiency of the prototype filter designed in this study.

scholarly journals Secrecy Wireless-Powered Sensor Networks for Internet of Things

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Junxia Li ◽  
Hui Zhao ◽  
Xueyan Chen ◽  
Zheng Chu ◽  
Li Zhen ◽  
...  
Keyword(s):  
Time Allocation ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Access Point ◽  
Case Scenario ◽  
Secrecy Rate ◽  
Global Optimal Solution ◽  
Worst Case ◽  
Sdp Relaxation ◽  
Global Optimal

This paper investigates a secure wireless-powered sensor network (WPSN) with the aid of a cooperative jammer (CJ). A power station (PS) wirelessly charges for a user equipment (UE) and the CJ to securely transmit information to an access point (AP) in the presence of multiple eavesdroppers. Also, the CJ are deployed, which can introduce more interference to degrade the performance of the malicious eavesdroppers. In order to improve the secure performance, we formulate an optimization problem for maximizing the secrecy rate at the AP to jointly design the secure beamformer and the energy time allocation. Since the formulated problem is not convex, we first propose a global optimal solution which employs the semidefinite programming (SDP) relaxation. Also, the tightness of the SDP relaxed solution is evaluated. In addition, we investigate a worst-case scenario, where the energy time allocation is achieved in a closed form. Finally, numerical results are presented to confirm effectiveness of the proposed scheme in comparison to the benchmark scheme.

scholarly journals A general framework of particle swarm optimization

2021 ◽  
Author(s):  
Loc Nguyen
Keyword(s):  
Particle Swarm Optimization ◽  
General Framework ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Target Function ◽  
Optimal Solution ◽  
Exploration And Exploitation ◽  
Global Optimal Solution ◽  
Swarm Optimization ◽  
Global Optimal

Particle swarm optimization (PSO) is an effective algorithm to solve the optimization problem in case that derivative of target function is inexistent or difficult to be determined. Because PSO has many parameters and variants, I propose a general framework of PSO called GPSO which aggregates important parameters and generalizes important variants so that researchers can customize PSO easily. Moreover, two main properties of PSO are exploration and exploitation. The exploration property aims to avoid premature converging so as to reach global optimal solution whereas the exploitation property aims to motivate PSO to converge as fast as possible. These two aspects are equally important. Therefore, GPSO also aims to balance the exploration and the exploitation. It is expected that GPSO supports users to tune parameters for not only solving premature problem but also fast convergence.

scholarly journals A New Global Optimization Algorithm for Solving Generalized Geometric Programming

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
San-Yang Liu ◽  
Chun-Feng Wang ◽  
Li-Xia Liu
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Geometric Programming ◽  
Optimal Solution ◽  
Global Optimization Algorithm ◽  
Global Optimal Solution ◽  
Linearization Technique ◽  
Generalized Geometric Programming ◽  
Global Optimal ◽  
Pruning Technique

A global optimization algorithm for solving generalized geometric programming (GGP) problem is developed based on a new linearization technique. Furthermore, in order to improve the convergence speed of this algorithm, a new pruning technique is proposed, which can be used to cut away a large part of the current investigated region in which the global optimal solution does not exist. Convergence of this algorithm is proved, and some experiments are reported to show the feasibility of the proposed algorithm.

Improved ADOPT Algorithm for the Problem of Multi-Robot Communication Failure

2013 ◽  
Vol 427-429 ◽  
pp. 2834-2837
Author(s):  
Bin Ge ◽  
Ling Liu
Keyword(s):  
Optimization Problem ◽  
Optimal Solution ◽  
Global Optimal Solution ◽  
Communication Failure ◽  
Distributed Constraint Optimization ◽  
Global Optimal ◽  
Set Up ◽  
Distributed Constraint Optimization Problem ◽  
Robot Communication ◽  
Multi Robot

DCOP (Distributed Constraint Optimization Problem) is currently the most widely used in multi-robot communication algorithm problem, Adopt algorithm is a kind of algorithm which is relatively perfect; it can transport messages accurately under the harsh environment of communication. It is completely asynchronous algorithm, through analyzing algorithm for local agents to obtain the global optimal solution, in the comparison of each algorithm can see that ADOPT algorithm not only can well solve the problem of communication failure, and the algorithm set up mechanism of terminate detection, it can make the algorithm stops on the optimal solution, and effectively solve the deadlock problem. The text use ADOPT algorithm to analyze how to solve the problem of multi-robot communication failure, and improve the algorithm to take messages more accurately and effectively.

scholarly journals An Effective Global Optimization Algorithm for Quadratic Programs with Quadratic Constraints

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 424
Author(s):  
Dongwei Shi ◽  
Jingben Yin ◽  
Chunyang Bai
Keyword(s):  
Numerical Experiments ◽  
Optimal Solution ◽  
Linearization Method ◽  
Linear Programming Relaxation ◽  
Global Optimization Algorithm ◽  
Quadratic Programs ◽  
Global Optimal Solution ◽  
Quadratic Constraints ◽  
Relaxation Problem ◽  
Global Optimal

This paper will present an effective algorithm for globally solving quadratic programs with quadratic constraints. In this algorithm, we propose a new linearization method for establishing the linear programming relaxation problem of quadratic programs with quadratic constraints. The proposed algorithm converges with the global optimal solution of the initial problem, and numerical experiments show the computational efficiency of the proposed algorithm.

AO-BBO: A Novel Optimization Algorithm and Its Application in Plant Drug Extraction

2019 ◽  
Vol 19 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Bote Lv ◽  
Juan Chen ◽  
Boyan Liu ◽  
Cuiying Dong
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Global Optimal Solution ◽  
Plant Medicine ◽  
Suitability Index ◽  
Sensing Model ◽  
Local Optimal Solution ◽  
Global Optimal

<P>Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. </P><P> Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. </P><P> Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.</P>

scholarly journals Best Proximity Point Results in Complex Valued Metric Spaces

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Binayak S. Choudhury ◽  
Nikhilesh Metiya ◽  
Pranati Maity
Keyword(s):  
Minimum Distance ◽  
Metric Spaces ◽  
Optimal Solution ◽  
Global Optimal Solution ◽  
Complex Valued Metric Space ◽  
Fixed Point Equation ◽  
Point Equation ◽  
Global Optimal ◽  
Metric Function ◽  
Complex Valued

We introduce the concept of proximity points for nonself-mappings between two subsets of a complex valued metric space which is a recently introduced extension of metric spaces obtained by allowing the metric function to assume values from the field of complex numbers. We apply this concept to obtain the minimum distance between two subsets of the complex valued metric spaces. We treat the problem as that of finding the global optimal solution of a fixed point equation although the exact solution does not in general exist. We also define and use the concept of P-property in such spaces. Our results are illustrated with examples.

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