Development and Comparison of Hybrid Genetic Algorithms for Network Design Problem in Closed Loop Supply Chain

This paper presents four different hybrid genetic algorithms for network design problem in closed loop supply chain. They are compared using a complete factorial experiment with two factors, viz. problem size and algorithm. Based on the significance of the factor “algorithm”, the best algorithm is identified using Duncan’s multiple range test. Then it is compared with a mathematical model in terms of total cost. It is found that the best hybrid genetic algorithm identified gives results on par with the mathematical model in statistical terms. So, the best algorithm out of four algorithm proposed in this paper is proved to be superior to all other algorithms for all sizes of problems and its performance is equal to that of the mathematical model for small size and medium size problems.

Manufacturing Supply Chain Optimization Problem with Time Windows Based on Improved Orthogonal Genetic Algorithm

Aim to the manufacturing supply chain optimization problem with time windows,presents an improved orthogonal genetic algorithm to solve it. At first,we decompose this problem into two sub-problems (distribution and routing) plus an interface mechanism to allow the two algorithms to collaborate in a master-slave fashion,with the distribution algorithm driving the routing algorithm. At second,we describe the proposed improved orthogonal genetic algorithm for solving giving problem detailedly. Finally,the examples suggest that this proposed approach is feasible,correct and valid.

THE DECISION OF THE OPTIMAL PARAMETERS IN MARKOV RANDOM FIELDS OF IMAGES BY GENETIC ALGORITHM

This paper introduces the principle of genetic algorithm and the basic method of solving Markov random field parameters.Focusing on the shortcomings in present methods,a new method based on genetic algorithms is proposed to solve the parameters in the Markov random field.The detailed procedure is discussed.On the basis of the parameters solved by genetic algorithms,some experiments on classification of aerial images are given.Experimental results show that the proposed method is effective and the classification results are satisfactory.

Elitist Reconstruction Genetic Algorithm Based on Markov Random Field for Magnetic Resonance Image Segmentation

In this paper, elitist reconstruction genetic algorithm （ERGA） based on Markov random field （MRF） is introduced for image segmentation. In this algorithm, a population of possible solutions is maintained at every generation, and for each solution a fitness value is calculated according to a fitness function, which is constructed based on the MRF potential function according to Metropolis function and Bayesian framework. After the improved selection, crossover and mutation, an elitist individual is restructured based on the strategy of restructuring elitist. This procedure is processed to select the location that denotes the largest MRF potential function value in the same location of all individuals. The algorithm is stopped when the change of fitness functions between two sequent generations is less than a specified value. Experiments show that the performance of the hybrid algorithm is better than that of some traditional algorithms.

Discrete channel modelling based on genetic algorithm and simulated annealing for training hidden Markov model

Hidden Maxkov models （HMMs） have been used to model burst error sources of wireless channels. This paper proposes a hybrid method of using genetic algorithm （GA） and simulated annealing （SA） to train HMM for discrete channel modelling. The proposed method is compared with pure GA, and experimental results show that the HMMs trained by the hybrid method can better describe the error sequences due to SA＇s ability of facilitating hill-climbing at the later stage of the search. The burst error statistics of the HMMs trained by the proposed method and the corresponding error sequences are also presented to validate the proposed method.

Assessing supply chain performance using genetic algorithm and support vector machine

The rough set-genetic support vector machine(SVM) model is applied to supply chain performance evaluation. First, the rough set theory is used to remove the redundant factors that affect the performance evaluation of supply chain to obtain the core influencing factors. Then the support vector machine is used to extract the core influencing factors to predict the level of supply chain performance. In the process of SVM classification, the genetic algorithm is used to optimize the parameters of the SVM algorithm to obtain the best parameter model, and then the supply chain performance evaluation level is predicted. Finally, an example is used to predict this model, and compared with the result of using only rough set-support vector machine to predict. The results show that the method of rough set-genetic support vector machine can predict the level of supply chain performance more accurately and the prediction result is more realistic, which is a scientific and feasible method.

基于改进Markov算法的电力线载波通信网络安全态势感知仿真研究

针对电力线载波通信网络安全态势感知单位运算时间较长且误差较大等问题,基于改进Markov算法研究一种新型通信网络安全态势感知方法。采用分区采集与降维运算数据预处理,去除电力线载波信号干扰因素。利用隶属关联矩阵挖掘网络安全要素特征,构建层次化Markov网络安全态势感知模型。利用BW算法寻找目标参数最优解,来确定感知目标点位置,缩短挖掘时间,提高感知精准度。经过试验验证,所提方法单位感知时间只有60~90 ms,多组并行感知均方误差不超过2%,表明所提方法能够满足电力线载波通信网络安全态势感知应用需求。

An adaptive genetic algorithm with diversity-guided mutation and its global convergence property

An adaptive genetic algorithm with diversity-guided mutation, which combines adaptive probabilities of crossover and mutation was proposed. By means of homogeneous finite Markov chains, it is proved that adaptive genetic algorithm with diversity-guided mutation and genetic algorithm with diversity-guided mutation converge to the global optimum if they maintain the best solutions, and the convergence of adaptive genetic algorithms with adaptive probabilities of crossover and mutation was studied. The performances of the above algorithms in optimizing several unimodal and multimodal functions were compared. The results show that for multimodal functions the average convergence generation of the adaptive genetic algorithm with diversity-guided mutation is about 900 less than that of (adaptive) genetic algorithm with adaptive probabilities and genetic algorithm with diversity-guided mutation, and the adaptive genetic algorithm with diversity-guided mutation does not lead to premature convergence. It is also shown that the better balance between overcoming premature convergence and quickening convergence speed can be gotten.

Global Convergence Analysis of Non-Crossover Genetic Algorithm and Its Application to Optimization

Selection, crossover, and mutation are three main operators of the canonical genetic algorithm (CGA). This paper presents a new approach to the genetic algorithm. This new approach applies only to mutation and selection operators. The paper proves that the search process of the non-crossover genetic algorithm (NCGA) is an ergodic homogeneous Markov chain. The proof of its convergence to global optimum is presented. Some nonlinear multi-modal optimization problems are applied to test the efficacy of the NCGA. NP-hard traveling salesman problem (TSP) is cited here as the benchmark problem to test the efficiency of the algorithm. The simulation result shows that NCGA achieves much faster convergence speed than CGA in terms of CPU time. The convergence speed per epoch of NCGA is also faster than that of CGA.

DYNAMIC RELOCATION OF PLANT/WAREHOUSE FACILITIES: A FAST COMPACT GENETIC ALGORITHM APPROACH

The problem of dynamic relocation and phase-out of combined manufacturingplant and warehousing facilities in the supply chain are concerned. A multiple time/multipleobjective model is proposed to maximize total profit during the time horizon, minimize total accesstime from the plant/warehouse facilities to its suppliers and customers and maximize aggregatedlocal incentives during the time horizon. The relocation problem keeps the feature of NP-hard andwith the traditional method the optimal result cannot be got easily. So a compact genetic algorithm(CGA) is introduced to solve the problem. In order to accelerate the convergence speed of the CGA,the least square approach is introduced and a fast compact genetic algorithm (fCGA) is proposed.Finally, simulation results with the fCGA are compared with the CGA and classical integerprogramming (IP). The results show that the fCGA proposed is of high efficiency for Paretooptimality problem.

Stochastic analysis and convergence velocity estimation of genetic algorithms

Formulizations of mutation and crossover operators independent of representation of solutions are proposed. A kind of precisely quantitative Markov chain of populations of standard genetic algorithms is modeled. It is proved that inadequate parameters of mutation and crossover probabilities degenerate standard genetic algorithm to a class of random search algorithms without selection bias toward any solution based on fitness. After introducing elitist reservation, the stochastic matrix of Markov chain of the best-so-far individual with the highest fitness is derived.The average convergence velocity of genetic algorithms is defined as the mathematical expectation of the mean absorbing time steps that the best-so-far individual transfers from any initial solution to the global optimum. Using the stochastic matrix of the best-so-far individual, a theoretic method and the computing process of estimating the average convergence velocity are proposed.

基于Markov过程天气预测的共享单车调度优化研究

针对某大学城共享单车资源分配不平衡问题,建立了与调度成本和用户满意度相关的单目标数学规划模型,对现有共享单车调度策略进行分析,并制定改善方案.通过调查问卷分析单车调度过程中存在的问题,利用Python获取单车的相关数据.在此基础上,基于齐次Markov过程对天气状况进行预测,采用LSTM时间序列对区域内未来单车数量的变化情况进行预测,利用遗传算法求解最优调度路径和调度数量,提出优化方案.通过敏感性分析验证本文所提方案的可行性.结果表明,优化后的调度方案缩短了单次调度时间并提高了用户的满意度,对共享单车管理部门具有指导意义.

Training Kohonen Networks by Using an Improved Genetic Algorithm

ＴｒａｉｎｉｎｇＫｏｈｏｎｅｎＮｅｔｗｏｒｋｓｂｙＵｓｉｎｇａｎＩｍｐｒｏｖｅｄＧｅｎｅｔｉｃＡｌｇｏｒｉｔｈｍＳｏｎｇＡｉｇｕｏ（宋爱国）ＬｕＪｉｒｅｎ（陆佶人）（ＤｅｐａｒｔｍｅｎｔｏｆＲａｄｉｏＥｎｇｉｎｅｅｒｉｎｇ，ＳｏｕｔｈｅａｓｔＵｎｉ...

Resolution of Resource Contentions in the CCPM-MPL Using Simulated Annealing and Genetic Algorithm

This research aims to plan a “good-enough” schedule with leveling of resource contentions. We use the existing critical chain project management-max-plus linear framework. Critical chain project management is known as a technique used to both shorten the makespan and observe the due date under limited resources;the max-plus linear representation is an approach for modeling discrete event systems as production systems and project scheduling. If a contention arises within a single resource, we must resolve it by appending precedence relations. Thus, the resolution framework is reduced to a combinatorial optimization. If we aim to obtain the exact optimal solution, the maximum computation time is longer than 10 hours for 20 jobs. We thus experiment with Simulated Annealing (SA) and Genetic Algorithm (GA) to obtain an approximate solution within a practical time. Comparing the two methods, the former was beneficial in computation time, whereas the latter was better in terms of the performance of the solution. If the number of tasks is 50, the solution using SA is better than that using GA.

On Finding the Smallest Generalized Eigenpair Using Markov Chain Monte Carlo Algorithm

This paper proposes a new technique based on inverse Markov chain Monte Carlo algorithm for finding the smallest generalized eigenpair of the large scale matrices. Some numerical examples show that the proposed method is efficient.

AN IMPROVED GENETIC ALGORITHM FOR SEARCHING OPTIMAL PARAMETERS IN n-DIMENSIONAL SPACE

An improved genetic algorithm for searching optimal parameters in n-dimensional space is presented, which encodes movement direction and distance and searches from coarse to precise. The algorithm can realize global optimization and improve the search efficiency, and can be applied effectively in industrial optimization, data mining and pattern recognition.

Potential-Decomposition Strategy in Markov Chain Monte Carlo Sampling Algorithms

We introduce the potential-decomposition strategy （PDS）, which can be used in Markov chain Monte Carlo sampling algorithms. PDS can be designed to make particles move in a modified potential that favors diffusion in phase space, then, by rejecting some trial samples, the target distributions can be sampled in an unbiased manner. Furthermore, if the accepted trial samples are insumcient, they can be recycled as initial states to form more unbiased samples. This strategy can greatly improve efficiency when the original potential has multiple metastable states separated by large barriers. We apply PDS to the 2d Ising model and a double-well potential model with a large barrier, demonstrating in these two representative examples that convergence is accelerated by orders of magnitude.

Using Genetic Algorithm for Identification of Diabetic Retinal Exudates in Digital Color Images

Blood vessels in ophthalmoscope images play an important role in diagnosis of some serious pathology on retinal images. Hence, accurate extraction of vessels is becoming a main topic of this research area. In this paper, a new hybrid approach called the (Genetic algorithm and vertex chain code) for blood vessel detection. And this method uses geometrical parameters of retinal vascular tree for diagnosing of hypertension and identified retinal exudates automatically from color retinal images. The skeletons of the segmented trees are produced by thinning. Three types of landmarks in the skeleton must be detected: terminal points, bifurcation and crossing points, these points are labeled and stored as a chain code. Results of the proposed system can achieve a diagnostic accuracy with 96.0% sensitivity and 98.4% specificity for the identification of images containing any evidence of retinopathy.

Implementation of Hybrid Genetic Algorithm for CLSC Network Design Problem—A Case Study on Fashion Leather Goods Industry

The implementation of closed loop supply chain system is becoming essential for fashion leather products industry to ensure an economically sustainable business model and eco-friendly industrial practice as demanded by the environmental regulations, consumer awareness and the prevailing social consciousness. In this context, this research work addresses a closed loop supply chain network problem of fashion leather goods industry, with an objective of minimizing the total cost of the entire supply chain and also reducing the total waste from the end of life product returns. The research work commenced with a literature review on the reverse and closed loop supply chain network design problems of fashion and leather goods industry dealt in the past. Then, the identified CLSCND problem is solved using a mathematical model based on Mixed Integer Non-Linear Programme (MINLP) and then a suitable Hybrid Genetic Algorithm (HGA) developed for the CLSCND is implemented for obtaining optimum solution. Both the MINLP model and HGA are customized as per the CLSCND problem chosen and implemented for the industrial case of an Indian Fashion Leather Goods Industry. Finally, the solutions obtained for MINLP model in LINGO 15 and for HGA in VB.NET platform are compared and presented. The optimum solution obtained from the suitable HGA is illustrated as an optimum shipment pattern for the closed loop supply chain network design problem of the fashion leather goods industry case.

Heterogeneous Network Selection Optimization Algorithm Based on a Markov Decision Model

A network selection optimization algorithm based on the Markov decision process(MDP)is proposed so that mobile terminals can always connect to the best wireless network in a heterogeneous network environment.Considering the different types of service requirements,the MDP model and its reward function are constructed based on the quality of service(QoS)attribute parameters of the mobile users,and the network attribute weights are calculated by using the analytic hierarchy process(AHP).The network handoff decision condition is designed according to the different types of user services and the time-varying characteristics of the network,and the MDP model is solved by using the genetic algorithm and simulated annealing(GA-SA),thus,users can seamlessly switch to the network with the best long-term expected reward value.Simulation results show that the proposed algorithm has good convergence performance,and can guarantee that users with different service types will obtain satisfactory expected total reward values and have low numbers of network handoffs.