scholarly journals The Comparison of Lately Proposed Harris Hawks Optimization and Jaya Optimization in Solving Directional Overcurrent Relays Coordination Problem

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-22 ◽  
Author(s):  
Jiangtao Yu ◽  
Chang-Hwan Kim ◽  
Sang-Bong Rhee
Keyword(s):  
Nonlinear Programming ◽  
Convergence Rate ◽  
Objective Function ◽  
Objective Function Value ◽  
Cooperative Behavior ◽  
Mixed Integer ◽  
Coordination Problem ◽  
Computation Efficiency ◽  
Design Variables ◽  
Overcurrent Relays

In this paper, a lately proposed Harris Hawks Optimizer (HHO) is used to solve the directional overcurrent relays (DOCRs) coordination problem. To the best of the authors’ knowledge, this is the first time HHO is being used in the DOCRs coordination problem. The main inspiration of HHO is the cooperative behavior and chasing style of Harris’ hawks from different directions, based on the dynamic nature of scenarios and escaping patterns of the prey. To test its performances in solving the DOCRs coordination problem, it is adopted in 3-bus, 4-bus, 8-bus, and 9-bus systems, which are formulated by three kinds of optimization models as linear programming (LP), nonlinear programming (NLP), and mixed integer nonlinear programming (MINLP), according to the nature of the design variables. Meanwhile, another lately proposed optimization algorithm named Jaya is also adopted to solve the same problem, and the results are compared with HHO in aspects of objective function value, convergence rate, robustness, and computation efficiency. The comparisons show that the robustness and consistency of HHO is relatively better than Jaya, while Jaya provides faster convergence rate with less CPU time and occasionally more competitive objective function value than HHO.

scholarly journals Joint admission control and power allocation in hospital networks based on cognitive radios.

2021 ◽  
Author(s):  
Yicong Liu
Keyword(s):  
Objective Function ◽  
Admission Control ◽  
Objective Function Value ◽  
Cognitive Radios ◽  
Hospital Environment ◽  
Mixed Integer ◽  
Total System ◽  
Secondary Users ◽  
Source Power ◽  
Minlp Problem

In this thesis, we present an approach to solve the joint call admission control and power allo- cation problem in a hospital environment based on cognitive radio. Specifically, a multi-objective non-convex mixed integer non-linear programming (MINLP) problem with weighted-sum method for wireless access in an indoor hospital environment has been formulated in order to maximize the number of admitted secondary users and minimize transmit power while guaranteeing the through- put of all secondary users and satisfying the interference constraints for the protected and primary users. To solve this MINLP problem with different weights given to different objectives, we pro- pose to use the standard branch and bound algorithm as appropriately modified to find the optimal solution. We also coded a specific program using OPTI Toolbox to find the minimum objective function value, number of admitted secondary users and all related values such as total system power and throughput. To analyze the numerical results, we considered three cases with equal and non-equal weights. We also changed the values of interference and maximum source power to obtain and analyze different results comparing with the normal one. Our results indicate that more power is allocated and better throughput is guaranteed while the number of admitted users is increasing. However, as they increase, the objective function value increases steadily as well, which means that it is more difficult to reach our minimizing objective.

Nonlinear Mixed Integer-Discrete-Continuous Programming and its Application to Engineering Design Problems

1989 ◽  
Author(s):  
J.-F. Fu ◽  
R. G. Fenton ◽  
W. L. Cleghorn
Keyword(s):  
Nonlinear Programming ◽  
Objective Function ◽  
Engineering Design ◽  
Optimization Algorithm ◽  
Mixed Integer ◽  
Continuous Variables ◽  
Design Problems ◽  
Continuous Programming ◽  
Engineering Design Problems ◽  
Standard Values

Abstract An algorithm for solving nonlinear programming problems containing integer, discrete and continuous variables is presented. Based on a commonly employed optimization algorithm, penalties on integer and/or discrete violations are imposed on the objective function to force the search to converge onto standard values. Examples are included to illustrate the practical use of this algorithm.

Modified Harmony Search Algorithm for 0-1 Knapsack Problems

2013 ◽  
Vol 365-366 ◽  
pp. 182-185
Author(s):  
Hong Gang Xia ◽  
Qing Liang Wang
Keyword(s):  
Convergence Rate ◽  
Objective Function ◽  
Objective Function Value ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Knapsack Problems ◽  
Convergence And Stability ◽  
Update Strategy ◽  
Harmony Memory

In this paper, a modified harmony search (MHS) algorithm was presented for solving 0-1 knapsack problems. MHS employs position update strategy for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. Besides, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory, and the key parameters PAR and BW dynamically adjusted with the number of generation. Based on the experiment of solving ten classic 0-1 knapsack problems, the MHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and NGHS).

scholarly journals An Effective Coordination Setting for Directional Overcurrent Relays Using Modified Harris Hawk Optimization

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3007
Author(s):  
Muhammad Irfan ◽  
Seung-Ryle Oh ◽  
Sang-Bong Rhee
Keyword(s):  
Distribution Networks ◽  
Optimal Operation ◽  
Mixed Integer ◽  
Coordination Problem ◽  
Roulette Wheel Selection ◽  
Medium Level ◽  
Overcurrent Relays ◽  
Comparison Of The Results ◽  
Matlab Programming ◽  
Bus System

The relay optimization expresses quite a challenge for smooth and optimal operation of power system networks. The relay optimization is formulated as a mixed integer non-linear problem and is highly constrained. Furthermore, a reliable relaying system must be able to detect and isolate the faulted portion in a timely manner. Therefore, it is necessary to find optimal parameters for relay settings to be able to respond in a timely way to the encountered fault and at the same time keep in consideration the operational and coordination constraints. This paper proposes modified Harris hawk optimization (MHHO), which is based on the intelligent preying tactics of Harris hawks and the improvement of intended modifications, crowding distance and roulette wheel selection. The proposed algorithm has been tested on IEEE 8 and 15-bus systems, using MATLAB programming. The test systems are the distribution networks covering the medium level voltage for consideration. The simulation results verified the success of MHHO to find optimal settings for the relays. For IEEE 8-bus system, MHHO was able to give 35.45% improvement in the results in comparison to other algorithms. Furthermore, for the IEEE 15-bus system, MHHO showed 24.09% improvement on average. The comparison of the results obtained by MHHO with the other state-of-the-art algorithms proved that it is the strong candidate for optimization of the relay coordination problem.

scholarly journals Improved Solutions for the Optimal Coordination of DOCRs Using Firefly Algorithm

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Muhammad Sulaiman ◽  
Waseem ◽  
Shakoor Muhammad ◽  
Asfandyar Khan
Keyword(s):  
Objective Function ◽  
Firefly Algorithm ◽  
Operating Time ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Nonlinear Constrained Optimization ◽  
Design Variables ◽  
Highly Nonlinear ◽  
Overcurrent Relays ◽  
Optimal Coordination

Nature-inspired optimization techniques are useful tools in electrical engineering problems to minimize or maximize an objective function. In this paper, we use the firefly algorithm to improve the optimal solution for the problem of directional overcurrent relays (DOCRs). It is a complex and highly nonlinear constrained optimization problem. In this problem, we have two types of design variables, which are variables for plug settings (PSs) and the time dial settings (TDSs) for each relay in the circuit. The objective function is to minimize the total operating time of all the basic relays to avoid unnecessary delays. We have considered four models in this paper which are IEEE (3-bus, 4-bus, 6-bus, and 8-bus) models. From the numerical results, it is obvious that the firefly algorithm with certain parameter settings performs better than the other state-of-the-art algorithms.

Development of a Spreadsheet DSS for Multi-Response Taguchi Parameter Optimization Problems Using the TOPSIS, VIKOR, and GRA Methods

2019 ◽  
Vol 18 (05) ◽  
pp. 1501-1531 ◽  
Author(s):  
Bariş Keçeci ◽  
Yusuf Tansel Iç ◽  
Ergün Eraslan
Keyword(s):  
Objective Function ◽  
Parameter Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Objective Function Value ◽  
Optimal Parameter ◽  
The Other ◽  
Mixed Integer ◽  
Parameter Combination ◽  
Single Response

This paper presents a spreadsheet-based decision support system (DSS) for any parameter optimization problem, in the small- and medium-sized enterprises to help the managers to make better decisions. Microsoft Excel is used as a DSS development platform. The DSS application requires the quality characteristics and the level of parameters affecting the problem. The proposed system considers three multi-criteria decision-making methods: TOPSIS, VIKOR and GRA. These methods are integrated into the Taguchi method to convert the multi-response optimization problem to a single-response problem. The DSS suggests proper Taguchi experimental designs and provides the decision maker with an opportunity to use different metrics and to validate the experimental results. Several issues and an application are provided for illustrative purposes. The proposed DSS is tested on a case study (the performance of the mixed integer programming (MIP) formulation solver) and the results highlight that the system is capable of offering satisfactory outcomes. Using such a quick and flexible DSS might help to reduce the daily workload of the decision makers. The different metrics used for the response variables which results with the different parameter combination. Using the optimal parameter combination of TOPSIS (come to the fore in case MinBest metric used), the MIP formulation solver gives the best integer objective function value of 609 and a GAP value of 1.93%, both of which are less than the values obtained using the other methods. Using the optimal parameter combination of GRA (come to the fore in case OptBest metric used), the MIP formulation gives a best integer objective function value of 632 and a GAP value of 6.52%, both of which are less than the values obtained by using the other methods.

scholarly journals The Comparison of Performance between Contrast Source Inversion and Its Related Algorithms and Several Improved Methods

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Meng Wang ◽  
Guizhen Lu
Keyword(s):  
Convergence Rate ◽  
Objective Function ◽  
Objective Function Value ◽  
Scattering Problem ◽  
The State ◽  
Convergence Speed ◽  
Source Inversion ◽  
Contrast Source Inversion ◽  
Data Error ◽  
State Error

The contrast source inversion (CSI) is an effective method for solving microwave imaging problems which is widely utilized. The core of the CSI is to change the conventional inverse scattering problem into an optimization problem. The two items in the objective function describe the state error and data error, respectively. As it is all known, there is almost no complete performance comparison based on Fresnel data for the CSI and its related improved algorithms. In addition, the performance of the algorithm under different weights was not analyzed before and the convergence speed of original CSI is slow. Firstly, this paper compares the performance of traditional CSI and its improved algorithms from three aspects of qualitative imaging effect, convergence speed, and objective function value based on Fresnel data. Secondly, the influence of the state error and the data error under different weights on the convergence rate and the objective function value are studied. For the limitation of a slower convergence rate, the CSI with weights (W-CSI), the CSI with dynamic reduction factor (CSI-DRF), and its related algorithms, which can get better convergence rate compared with their relative original algorithms, are proposed. Eventually, the future research work is prospected.

scholarly journals Convergence Rate Analysis of the Proximal Difference of the Convex Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Xueyong Wang ◽  
Ying Zhang ◽  
Haibin Chen ◽  
Xipeng Kou
Keyword(s):  
Convergence Rate ◽  
Convex Function ◽  
Objective Function ◽  
Convex Functions ◽  
Objective Function Value ◽  
Convex Decomposition ◽  
Rate Analysis ◽  
Difference Of Convex ◽  
The Difference ◽  
Convergence Rate Analysis

In this paper, we study the convergence rate of the proximal difference of the convex algorithm for the problem with a strong convex function and two convex functions. By making full use of the special structure of the difference of convex decomposition, we prove that the convergence rate of the proximal difference of the convex algorithm is linear, which is measured by the objective function value.

scholarly journals Joint admission control and power allocation in hospital networks based on cognitive radios.

2021 ◽  
Author(s):  
Yicong Liu
Keyword(s):  
Objective Function ◽  
Admission Control ◽  
Objective Function Value ◽  
Cognitive Radios ◽  
Hospital Environment ◽  
Mixed Integer ◽  
Total System ◽  
Secondary Users ◽  
Source Power ◽  
Minlp Problem

In this thesis, we present an approach to solve the joint call admission control and power allo- cation problem in a hospital environment based on cognitive radio. Specifically, a multi-objective non-convex mixed integer non-linear programming (MINLP) problem with weighted-sum method for wireless access in an indoor hospital environment has been formulated in order to maximize the number of admitted secondary users and minimize transmit power while guaranteeing the through- put of all secondary users and satisfying the interference constraints for the protected and primary users. To solve this MINLP problem with different weights given to different objectives, we pro- pose to use the standard branch and bound algorithm as appropriately modified to find the optimal solution. We also coded a specific program using OPTI Toolbox to find the minimum objective function value, number of admitted secondary users and all related values such as total system power and throughput. To analyze the numerical results, we considered three cases with equal and non-equal weights. We also changed the values of interference and maximum source power to obtain and analyze different results comparing with the normal one. Our results indicate that more power is allocated and better throughput is guaranteed while the number of admitted users is increasing. However, as they increase, the objective function value increases steadily as well, which means that it is more difficult to reach our minimizing objective.

A Self-Study Harmony Search Algorithm for Optimization Problems

2014 ◽  
Vol 1006-1007 ◽  
pp. 1017-1020
Author(s):  
Ping Zhang ◽  
Mei Ling Li ◽  
Qian Han ◽  
Guo Jun Li
Keyword(s):  
Convergence Rate ◽  
Objective Function ◽  
Optimization Problems ◽  
Objective Function Value ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Unconstrained Optimization Problems ◽  
Self Study ◽  
Harmony Memory

A self-study harmony search (SSHS) algorithm for solving unconstrained optimization problems has presented in this paper . SSHS employs a novel self-study strategy to generate new solution vectors which can enhance accuracy and convergence rate of harmony search (HS) algorithm. SSHS algorithm as proposed, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory. a large number of experiments improved that SSHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and NGHS)

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