A Node Positioning Algorithm in Wireless Sensor Networks Based on Improved Particle Swarm Optimization

Sun Shunyuan,Yu Quan,Xu Baoguo 보안공학연구지원센터 2016 International Journal of Future Generation Communi Vol.9 No.4

The estimation error of the least square method in traditional Distance Vector-Hop (DV-Hop) algorithm is too large and the Particle Swarm Optimization (PSO) algorithm is easy to trap into local optimum. In order to overcome the problems, a fusion algorithm of improved particle swarm algorithm and DV-Hop algorithm was presented. Firstly, PSO algorithm was improved from aspects of particle velocity, inertia weight, learning strategy and variation, which enhanced the ability to jump out of local optimum of the algorithm and increased the search speed of the algorithm in later iterative stage. Then, the node localization result was optimized by using the improved PSO algorithm in the third stage of the DV-Hop algorithm. The simulation results show that compared with the traditional DV-Hop algorithm, the improved DV-Hop based on chaotic PSO algorithm and the DV-Hop algorithm based on improved PSO, the proposed algorithm has higher positioning accuracy and better stability, which is suitable for high positioning accuracy and stability requirements scenes.

Optimization of Grey Neural Network Model Based on Mind Evolutionary Algorithm

Zhang, Yong-Li(장영예),Na, Sang-Gyun(나상균),Wang, Bao-Shuai(왕바오슈아이) 한국산업경제학회 2017 산업경제연구 Vol.30 No.4

회색신경망모형은 제품의 주문 수량을 예측할 때 소 표본과 정보부족 등의 문제를 해결하는데 유용하게 사용할 수 있지만, 예측 시 무작위로 파라미터를 초기화하면 예측정확도가 낮아지는 단점이 있다. 따라서 본 연구에서는 전통적인 회색신경망모형의 단점을 보완하기 위해서 MEA(Mind Evolution Algorithm)를 적용한 최적화된 회색 신경망모형을 제시하였다. 본 연구 결과는 다음과 같다. 제품의 주문수량을 예측하기 위해서 MEA(Mind Evolution Algorithm)를 이용한 최적화된 회색신경망모형, PSO(Particle Swarm Optimization)를 이용한 최적화된 회색신경망모형, GA(Genetic Algorithm)을 이용한 최적화된 회색신경망모형, BP(Back Propagation)을 이용한 회색신경망모형에 대해 비교분석을 하였다. 분석결과를 보면, MEA(Mind Evolution Algorithm)를 이용한 최적화된 회색신경망모형의 예측 총오차 4457.6000, 평균루트오차 920.7372, 평균오차백분율 4.04%, 운영시간은 1.1509초로 분석되었다. 선행연구에서 제시한 가장 우수한 PSO(Particle Swarm Optimization)를 이용한 최적화된 회색 신경망모형 보다, MEA(Mind Evolution Algorithm)를 이용한 최적화된 회색신경망모형의 예측 총 오차가 16.12%, 평균루트오차 12.39%, 평균오차백분율 16.87%가 감소되어, 결과적으로 운영시간이 감소되면서 계산속도가 21.61%가 증가 되었다. 따라서 다른 지능알고리즘과 비교하면, MEA(Mind Evolution Algorithm)를 이용한 최적화된 회색신경망모형은 최고의 예측 정확도와 계산속도가 높다는 장점을 보유하고 있다. 따라서 기업에서 제품의 주문 수량을 예측할 때 소 표본과 정보부족 등의 문제가 발생할 경우, 최적화된 회색신경망모형을 이용하는 것이 필요하다. For the problem of randomized parameters and large error, the grey neural network model optimized by mind evolutionary algorithm(MEA-GNNM) was presented. The experiment results showed that the total error, root mean square error, mean percentage error, and running time of MEA-GNNM were 4457.6000, 920.7372, 4.04% and 1.1509 seconds; Compared with GA-GNNM, the total error, root mean square error and mean percentage error of MEA-GNNM had decreased by 16.12%, 12.39% and 16.87%, and the calculation speed increased by 21.61%. Compared with PSO-GNNM, the total error, root mean square error and mean percentage error of MEA-GNNM had decreased by 9.13%, 11.33% and 12.17%, but running time increased by 104.50%. The paper presents a new approach to optimize the parameters of grey neural network model, and also provides a new method having higher prediction accuracy for the time series prediction.

사업 초기단계에서 공동주택 공사비의 예측정확도 비교에 관한 연구 - 유전자 알고리즘과 PSO방법을 중심으로 -

이상춘 대한건축학회지회연합회 2011 대한건축학회연합논문집 Vol.13 No.3

최근에 진화연산방법은 건설 분야의 여러 문제에 성공적으로 적용되어 왔으며 대표적인 방법으로는 유전자 알고리즘과 Particle Swarm Optimization이 있다. 유전자 알고리즘은 생물유전학과 자연선택이론에 바탕을 둔 병렬적인 최적화 탐색방법이며 PSO는 새나 물고리 무리의 집단적인 행동에서 영감을 얻은 진화형 통계탐색방법이다. 두 방법은 많은 유사한 점을 공유하고 있지만, 어느 한 방법이 다른 방법도 효율과 효과 면에서 항상 뛰어나다는 것을 증명할 수 없다. 이에 본 연구는 유전자 알고리즘과 PSO방법 중 어떤 방법이 공동주택 사업초기단계에서의 공사비 예측문제에서 더 뛰어난 예측성능을 보여주는 지를 조사하였다. 한국건설감리협회의 감리자 입찰공고를 통해 수집한 219개의 공동주택 사업자료를 가지고 NeuroShell Predictor 소프트웨어에서 두 방법을 이용한 예측모델을 제시하였고 모델의 예측성능을 점검하였다. 소프트웨어에서 제공하는 높은 R-squared 및 상관계수값을 통해서 두 예측모델의 성능은 우수한 것으로 판명되었으나 PSO 기반 예측모델이 유전자 알고리즘 기반 모델보다 평가시험그룹에서의 예측정확도면에서 약간 더 우수한 것으로 나타났다. In recent years, evolutionary computation methods have been successfully applied to various problems in the construction field. Two of these popular methods are Genetic Algorithm(GA) and Particle Swarm Optimization(PSO). GA is a parallel search method for an optimal solution based on the genetics and natural selection and PSO is a population-based stochastic optimization method inspired by social behavior of bird flocking or fish schooling. While two methods share many similarities, one cannot be proven to always outperform the other in terms of efficiency and effectiveness. From this point, this paper examined whether GA or PSO is superior at a problem to predict construction cost on apartment housing projects at the early stage. With 219 apartment housing project data obtained from bidding announcements for construction supervisors in the Korea Construction Consulting Engineers Association, prediction models using two methods were suggested in the NeuroShell Predictor software and prediction performances of two models were checked. From high R-squared and correlation coefficient values provided by the software, performances of two prediction models were found to be good. However, the findings also showed that the PSO-based model is slightly better than the GA-based one in the comparison of prediction accuracy on the testing dataset.

순서화 문제에서 이산적 Particle Swarm Optimization들의 성능 비교

임동순 한국산업경영시스템학회 2010 한국산업경영시스템학회지 Vol.33 No.4

Particle Swarm Optimization (PSO) which has been well known to solve continuous problems can be applied to discrete combinatorial problems. Several DPSO (Discrete Particle Swarm Optimization) algorithms have been proposed to solve discrete problems such a

Recent Advances in Adaptive Particle Swarm Optimization Algorithms

The Jin Ai,Voratas Kachitvichyanukul 대한산업공학회 2008 대한산업공학회 추계학술대회논문집 Vol.2008 No.11

This paper reviews recent literature on the mechanisms for adapting parameters of particle swarm optimization (PSO) algorithm. The review covers the mechanisms for adaptively setting such parameters as inertia weight, acceleration constants, number of particles and number of iterations. In additions, a more thorough review of the mechanisms for adapting inertia weight and acceleration constants is made. Detailed description of two specific mechanisms that are reviewed in detailed include the velocity index pattern for adapting the inertia weight and the relative gaps between various learning terms and the best objective function values for adapting the acceleration constants. An example to demonstrate the mechanisms are illustrated by using the GLNPSO for a specific optimization problem, namely, the vehicle routing problem. The preliminary experiment indicates that the addition of the proposed adaptive mechanisms can provide good algorithm performance in terms of solution quality with a slightly slower computational time.

Rule과 PSO알고리즘을 사용한 배치 최적화

남명훈(Myung-Hoon Nam),박강(Kang Park) 대한기계학회 2021 대한기계학회 춘추학술대회 Vol.2021 No.11

The manufacturing industry is undergoing many changes with the advent of the 4th industrial revolution. As the manufacturing industry changes, new processes and facilities are needed and the efficiency of production must be increased by arranging them. However, it requires a lot of time and money to place the equipment in the actual process and conduct the test. Therefore, in this study, we propose a SW that deploys and calculates facilities using the Particle Swarm Optimization (PSO) algorithm. The PSO algorithm is a method in which randomly placed particles find the optimal solution to the objective function through data comparison. Each particle was defined as one batch, and the location and phase of the equipment constituting the batch were used as variables. Using the sum of the product logistics movement distance and the operator movement distance as the objective function. The optimal arrangement was derived by comparing the results of the objective function of each particle.

The Integrated Optimization of Quay Crane-Yard Truck Scheduling in the Container Terminal with Uncertain Factors

Yiqin Lu,Youfang Huang,Bin Yang 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.2

The key to improve the container terminal efficiency is the integrated optimization of the quay crane (QC) and the yard truck (YT) scheduling, which is normally settled separately and considered in a certain condition in classical literatures. To improve the operation efficiency and simulate the practical operation, a PSO-based integrated QC-YT scheduling optimization model with uncertain factors is established in this research, considering two uncertain factors of YT travel speed and unit time of QC loading/discharging operation that affect the operating efficiency of the terminal greatly. The goal is the minimal operated time of QCs with the coordination of YTs. To solve this difficult combinatorial problem, the PSO algorithm is developed. PSO is evaluated for combinatorial problems with uncertain factors, which represents a new application of PSO. Numerical experiments show that the model of this research gives systemic simulation for the scheduling process with uncertain factors. And the results are better than model without uncertainties in terms of the accuracy and stability.

TLBO-CS 알골리즘을 이용한 독립형 하이브리드 에너지시스템의 최적설계에 관한 연구

조재훈(Jae-Hoon Cho),홍원표(Won-Pyo Hong) 한국조명·전기설비학회 2018 조명·전기설비학회논문지 Vol.32 No.5

In this paper, a new design optimal economical sizing of a Hybrid PV/WT/Diesel/Battery energy system is presented in order to assist the designers to take into consideration both the economical and ecological aspects. The reliability and cost of a hybrid system are two main criteria for designing a stand-alone hybrid system. The aim of this design is minimization of total annual cost(TAC) considered loss power supply probability and annual fuel cost of diesel generation system. A hybrid Teaching-Learning-Based Optimization algorithm is used for choosing the optimal number and type of units and the Total Annual Cost and Loss of Power Supply Probability are taken as the multi-objective functions of the proposed optimization algorithm. The effectiveness of the proposed method is verified using Matlab software and the simulation results confirm that the proposed method is more efficient than conventional methods and a feasible solution for stand-alone applications at the remote location.

Power Quality Enhancement Using Evolutionary Algorithms in Grid-Integrated PV Inverter

Vanaja N.,Kumar N. Senthil 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.5

Researchers are now concentrating on the problem of finding the optimal P–Q control of real and reactive power in grid-connected inverters with the emergence of Solar PV systems. The provision of both real and reactive power is essential for the improvement of overall power quality; in addition to maintaining grid voltage and power factor, grid-interlinked inverters are seen as the most cost-effective means of compensating for real and reactive power. In various ecological settings, photovoltaic systems are linked to the power grid to deliver reactive power. This paper proposes the decoupled P–Q model with the Salp swarm optimization technique to manage the Grid-tied inverters. Moreover, the proposed controlling techniques reduce Total Harmonic Distortion (THD) and output controlled power more efficiently than existing controllers. Moreover, THD is calculated and exhibited. Numerical simulations demonstrate that SSO and PSO algorithms can resolve a harmonic elimination problem just as effectively as GA. When compared to SSO, PSO yields numerical simulation outcomes that are steadier and feature a smaller standard deviation. A 75 kW grid-linked converter is modelled and tested in a simulation study with MATLAB Simulink to prove the efficiency of the system. Utilizing the MATLAB Software, the proposed Salp swarm optimization approach is compared to conventional optimization approaches such as PI controller, GA, and PSO. The results of the traditional feedforward controller are compared with the suggested SSO technique in a variety of cases. A low-scale prototype model is also created for real-time implementation to authenticate the outcomes of the simulations.

실시간 이미지 획득을 통한 pRBFNNs 기반 얼굴인식 시스템 설계

오성권(Sung-Kwun Oh),석진욱(Jin-Wook Seok),김기상(Ki-Sang Kim),김현기(Hyun-Ki Kim) 제어로봇시스템학회 2010 제어·로봇·시스템학회 논문지 Vol.16 No.12

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in "If-then" format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.