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Design and Implementation of a hybrid intelligent System Based on Particle Swarm Optimization, Hill Climbing and Distributed Genetic algorithm for Node Placement Problem in WMNs: A Comparison Study 32

Design and Implementation of a Hybrid Intelligent System Bas...

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32nd IEEE International Conference on Advanced Information Networking and Applications (AINA)

作者： Sakamoto, Shinji Barolli, Admir Barolli, Leonard Takizawa, Makoto FIT Grad Sch Engn Higashi Ku 3-30-1 Wajirohigashi Fukuoka Fukuoka 8110295 Japan Aleksander Moisiu Univ Durres Dept Informat Technol L-1Rruga Currilave Durres Albania FIT Dept Informat & Commun Engn Higashi Ku 3-30-1 Wajirohigashi Fukuoka Fukuoka 8110295 Japan Hosei Univ Fac Sci & Engn Dept Adv Sci Kajino Machi Koganei Tokyo 1848584 Japan

ISBN: (纸本)9781538621950

The Wireless Mesh Networks (WMNs) have low cost and high speed wireless Internet connectivity, therefore they are becoming an important networking infrastructure. In our previous work, we implemented a hybrid intelligent system based on Particle Swarm Optimization (PSO) and Hill Climbing (HC), called WMN-PSOHC, and a simulation system based on Genetic algorithm (GA), called WMN-GA, for solving node placement problem in WMNs. In this paper, we implement a new hybrid simulation system based on PSOHC and distributed GA (DGA), called WMN-PSOHC-DGA. We evaluate WMN-PSOHC-DGA system by computer simulations. The simulation results show that the WMN-PSOHC-DGA system has a better performance compared with WMN-PSODGA.

关键词： Wireless Mesh Networks Particle Swarm Optimization Hill Climbing Distributed Genetic algorithm hybrid intelligent algorithm

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Study on optimization of economic dispatching of electric power system based on hybrid intelligent algorithms (PSO and AFSA)

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ENERGY 2019年 183卷 926-935页

作者： Yuan, Guanghui Yang, Weixin Shanghai Univ Finance & Econ Fintech Res Inst Sch Informat Management & Engn Shanghai 200433 Peoples R China Univ Shanghai Sci & Technol Business Sch Shanghai 200093 Peoples R China

In order to optimize the economic dispatching of the electric power system, this paper has proposed a new hybrid intelligent algorithm based on the Particle Swarm Optimization (PSO) and Artificial Fish Swarm algorithm (AFSA). Basing on a comprehensive dispatching optimization model with the goal of minimizing coal consumption, pollution emission and purchasing cost, we have utilized this hybrid intelligent algorithms to solve the integrated weighted dispatching optimization model for five units and ten units respectively, considering the node flow balance of the power system, as well as the system's active power balance, positive and negative reserve constraints, transmission capacity constraints, unit output constraints, node voltage constraints, unit output power rise rate constraints, unit minimum runtime and downtime constraints, etc. The calculation results in the case study of five units are that coal consumption is 10,074.17 hundred-yuan, carbon emission is 11,280.75 kg, and electric power cost is 12,827.54 hundred-yuan. The calculation results in the case study of ten units, given a population size of 200, are that coal consumption is 31,305.45 hundred-yuan, carbon emission is 13,982.06 kg, and electric power cost is 16,754.79 hundred-yuan;while given a population size of 30, coal consumption is 29,221.16 hundred-yuan, carbon emission is 10,921.21 kg, and electric power cost is 16,521.56 hundred-yuan. Moreover, we have obtained the above results with an improvement of 35.56% in calculation efficiency. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： PSO AFSA hybrid intelligent algorithm Electrical power system Dispatching and optimization

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A new intelligent algorithm for dynamic facility layout problem in state of fuzzy constraints

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NEURAL COMPUTING & APPLICATIONS 2014年第5期24卷 1179-1190页

作者： Kaveh, Mojtaba Dalfard, Vahid Majazi Amiri, Sajjad Islamic Azad Univ Firoozabad Branch Dept Business Adm Firoozabad Iran Islamic Azad Univ Kerman Branch Young Res & Elites Club Kerman Iran Sirjan Technol Univ Dept Math & Comp Sci Sirjan Iran

In the present paper, the dynamic facilities layout problem is studied in presence of ambiguity of information flow. Product demand (and consequently material flow) is defined as fuzzy numbers with different membership functions. The problem is modeled in fuzzy programming. Three models of expected value, chance-constrained programming and dependent-chance programming and two hybrid intelligent algorithms are then presented. At the end, efficiency of algorithms for solving fuzzy models of dynamic facilities layout is shown through some numerical examples.

关键词： Dynamic facilities layout problem Fuzzy programming hybrid intelligent algorithm Genetic algorithm Simulated annealing algorithm

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Two-agent scheduling problem under fuzzy environment

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JOURNAL OF intelligent MANUFACTURING 2017年第3期28卷 739-748页

作者： Ni, Yaodong Zhao, Zhaojun Univ Int Business & Econ Sch Informat Technol & Management Beijing 100029 Peoples R China

The two-agent scheduling problem has a wide range of applications in real world. Although uncertainty is ubiquitous in real world, research of the two-agent scheduling problem under uncertain environment is rare. For applications where historical data is not abundant, investigation of this problem under fuzzy environment is necessary. This paper studies the two-agent scheduling problem under fuzzy environment. We assume that the processing time of each job and the weights that the agents assign to the jobs are fuzzy variables, and focus on the problem where the cost of one agent is the maximum weighted completion time of her jobs and the cost of the other agent is the sum of the weighted completion time of her jobs. Based on three different decision criteria, we present three concepts of schedule and three fuzzy programming models respectively. In order to solve the proposed models, we design a hybrid intelligent algorithm that integrates fuzzy simulation with genetic algorithm. Numerical experiments are given to show the effectiveness of the models and the algorithm.

关键词： Two-agent scheduling Credibility measure Fuzzy programming hybrid intelligent algorithm

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Fuzzy random dependent-chance programming

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2001年第5期9卷 721-726页

作者： Liu, BD Tsinghua Univ Dept Math Sci Uncertain Syst Lab Beijing 100084 Peoples R China

This paper presents the concepts of uncertain environment, event, chance function and principle of uncertainty for fuzzy random decision systems, thus offering a theoretical framework of fuzzy random dependent-chance programming. A hybrid intelligent algorithm is applied to solving fuzzy random dependent-chance programming models. Some numerical examples are also provided to illustrate the effectiveness of hybrid intelligent algorithm.

关键词： fuzzy programming genetic algorithm hybrid intelligent algorithm neural network (NN) simulation stochastic programming

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Capacitated location allocation problem with stochastic location and fuzzy demand: A hybrid algorithm

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APPLIED MATHEMATICAL MODELLING 2013年第7期37卷 5109-5119页

作者： Mousavi, Seyed Mohsen Niaki, Seyed Taghi Akhavan Islamic Azad Univ Dept Ind Engn Fac Ind & Mech Engn Qazvin Branch Qazvin Iran Sharif Univ Technol Dept Ind Engn Tehran *** Iran

In this article, a capacitated location allocation problem is considered in which the demands and the locations of the customers are uncertain. The demands are assumed fuzzy, the locations follow a normal probability distribution, and the distances between the locations and the customers are taken Euclidean and squared Euclidean. The fuzzy expected cost programming, the fuzzy beta-cost minimization model, and the credibility maximization model are three types of fuzzy programming that are developed to model the problem. Moreover, two closed-form Euclidean and squared Euclidean expressions are used to evaluate the expected distance between customers and facilities. In order to solve the problem at hand, a hybrid intelligent algorithm is applied in which the simplex algorithm, fuzzy simulation, and a modified genetic algorithm are integrated. Finally, in order to illustrate the efficiency of the proposed hybrid algorithm, some numerical examples are presented. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Location allocation problem Fuzzy programming Fuzzy simulation hybrid intelligent algorithm Evaluating expected distance

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Fuzzy random chance-constrained programming

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2001年第5期9卷 713-720页

作者： Liu, BD Tsinghua Univ Dept Math Sci Uncertain Syst Lab Beijing 100084 Peoples R China

By fuzzy random programming, we mean the optimization theory dealing with fuzzy random decision problems. This paper presents a new concept of chance of fuzzy random events and then constructs a general framework of fuzzy random chance-constrained programming (CCP). We also design a spectrum of fuzzy random simulations for computing uncertain functions arising in the area of fuzzy random programming. To speed up the process of handling uncertain functions, we train a neural network to approximate uncertain functions based on the training data generated by fuzzy random simulation. Finally, we integrate fuzzy random simulation, neural network, and genetic algorithm to produce a more powerful and effective hybrid intelligent algorithm for solving fuzzy random programming models and illustrate its effectiveness by some numerical examples.

关键词： fuzzy programming genetic algorithm hybrid intelligent algorithm neural network simulation stochastic programming

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Remanufacturing production planning with compensation function approximation method

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APPLIED MATHEMATICS AND COMPUTATION 2015年 256卷 742-753页

作者： Wen, Haijun Liu, Mingzhou Liu, Changyi Liu, Conghu Hefei Univ Technol Sch Mech & Automot Engn Hefei 230009 Peoples R China North Univ China Sch Mech & Power Engn Taiyuan 030051 Peoples R China

Remanufacturing is becoming a strategic emerging industry in China. However, there are many uncertain factors such as remanufacturing rate of recycling products, reprocessing costs, quantity of recycling products during a remanufacturing process. Hence, it is difficult to make an accurate production planning. This paper aims at studying a new remanufacturing production planning model in view of some possible uncertain factors in a remanufacturing enterprise according to the features and characteristics of remanufacturing. Considering the production capacity constraint of recycling, reprocessing and reassembly under the condition of uncertain reprocessing amount, unpredictable reprocessing cost, unknown purchase volume of new parts, and uncertain customer demand, this paper develops a two-stage, multi-period hybrid programming model with compensation function based on uncertainty theory to minimize the total remanufacturing cost. A hybrid intelligent algorithm is designed combined with compensation function approximation, neural network training, and virus particle algorithm to optimize this two-stage uncertain remanufacturing production planning. By use of compensation function approximation method, it is to convert an infinite optimization problem in this algorithm into that of a finite one. Finally, one remanufacturing simulation case is studied to validate the efficiency and rationality of the proposed approach. Crown Copyright (C) 2015 Published by Elsevier Inc. All rights reserved.

关键词： Remanufacturing Two-stage uncertain programming Production planning Approximation method hybrid intelligent algorithm

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Fuzzy multi-objective chance-constrained programming model for hazardous materials transportation

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INTERNATIONAL JOURNAL OF GENERAL SYSTEMS 2016年第3期45卷 286-310页

作者： Du, Jiaoman Yu, Lean Li, Xiang Beijing Univ Chem Technol Sch Econ & Management Beijing 100029 Peoples R China

Hazardous materials transportation is an important and hot issue of public safety. Based on the shortest path model, this paper presents a fuzzy multi-objective programming model that minimizes the transportation risk to life, travel time and fuel consumption. First, we present the risk model, travel time model and fuel consumption model. Furthermore, we formulate a chance-constrained programming model within the framework of credibility theory, in which the lengths of arcs in the transportation network are assumed to be fuzzy variables. A hybrid intelligent algorithm integrating fuzzy simulation and genetic algorithm is designed for finding a satisfactory solution. Finally, some numerical examples are given to demonstrate the efficiency of the proposed model and algorithm.

关键词： Hazardous materials transportation multi-objective programming fuzzy chance-constrained programming hybrid intelligent algorithm

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A credibilistic decision support system for portfolio optimization

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APPLIED SOFT COMPUTING 2017年 59卷 512-528页

作者： Jalota, Hemant Thakur, Manoj Mittal, Garima Indian Inst Technol Mandi Mandi 175001 Himachal Prades India Indian Inst Management Lucknow Lucknow 226013 Uttar Pradesh India

In this paper, a Decision Support System (DSS) for generating a suitable portfolio for an investor in an uncertain multi-criteria framework is proposed. We model uncertain parameters like return and illiquidity of various assets using L-R fuzzy numbers belonging to a power reference function family. Such usage of L-R fuzzy numbers is more generic as compared to the conventional triangular or trapezoidal fuzzy numbers and is a closer representation of uncertain behavior of the asset parameters. The credibility measure which has an advantage of being self-dual as compared to usual possibility measure marks the uncertain context of the entire setup and adds a new dimension to existing studies. We use an "Entropy-Cross Entropy (ECE) algorithm", for finding the solution of the optimization problem meant for finding the best fit L-R fuzzy number corresponding to uncertain asset parameters. This automates the entire subjective exercise where, otherwise, a human intervention is required for feeding the parameters required for fitting L-R fuzzy number. Once the L-R fuzzy number are created around various assets available for the portfolio formation, the portfolio optimization problem is solved using hybrid intelligent algorithm (HIA). HIA is designed by embedding fuzzy simulation within the "MIBEX-SM" genetic algorithm. To demonstrate the entire solution approach, four portfolio optimization models are solved using historical data from the National Stock Exchange (NSE) of India. The performance of the models is compared using a modified Sharpe ratio in the fuzzy context, namely the "Credibilistic Sharpe Ratio (CrSR)". To carry out the empirical study, firstly we use the data from the period of 2008-2013 for training the optimization models. Thereafter, to test the performance of the models, the data set from the two year period 2013-2015 is used. The process of validation of the results is then carried out using the data set of the one year period from 2015 to 2016. (C) 2017

关键词： Credibilistic portfolio selection L-R fuzzy numbers Credibilistic Sharpe Ratio hybrid intelligent algorithm Portfolio optimization

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