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Reservoir flood control operation using multi-objective evolutionary algorithm with decomposition and preferences

APPLIED SOFT COMPUTING 2017年 50卷 21-33页

作者： Qi, Yutao Yu, Jusheng Li, Xiaodong Wei, Yanxi Miao, Qiguang Xidian Univ Sch Comp Sci & Technol Xian Peoples R China RMIT Univ Sch Comp Sci & IT Melbourne Vic Australia

In this paper we propose a preference-based multi-objective optimization model for reservoir flood control operation (RFCO). This model takes the water preserving demand into consideration while optimizing two conflicting flood control objectives. A preference based multi-objective evolutionary algorithm with decomposition, named MOEA/D-PWA, is developed for solving the proposed RFCO model. For RFCO, it is challenging to define the preferred region formally, as the preference information is implicit and difficult to formulate. MOEA/D-PWA estimates the preferred region dynamically according to the final water level of solutions in the population, and then guides the search by propelling solutions towards the preferred region. Experimental results on four types of floods at the Ankang reservoir have illustrated that the suggested MOEA/D-PWA can successfully produce solutions in the preferred region of the Pareto front. The schedules obtained by MOEA/D-PWA can significantly reduce the flood peak and guarantee the dam safety as well. The proposed MOEA/D-PWA is also efficient in term of computational cost. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Reservoir flood control operation multi-objective evolutionary algorithm MOEA/D Preference

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A multi-objective evolutionary algorithm with Dynamic Topology and its Application to Network-Wide Flight Trajectory Planning

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2017年第1期10卷 1345-1354页

作者： Yan, Su Cai, Kaiquan Swaid, Majed Beihang Univ Sch Elect & Informat Engn Natl Key Lab CNS ATM Beijing 100191 Peoples R China Hamburg Univ Technol Inst Air Transportat Syst D-21079 Hamburg Germany

Although conventional multi-objective evolutionary optimization algorithms (MOEAs) are proven to be effective in general, they are less superior when applied to solve a large-scale combinational real-world optimization problem with tightly coupled decision variables. For the purpose to enhance the capability of MOEAs in such scenarios, one may consider the importance of interaction topology in information exchange among individuals of MOEAs. From this standpoint, this article proposes a non-dominated sorting genetic algorithm II with dynamic topology (DTNSGAII), which applies a dynamic individual interaction network topology to improve the crossover operation. The dynamic topology and inter-individual interaction are determined by the solution spread criterion in the objective space as well as the solution relationships and similarities in the decision space. The combination of two aspects contributes to the balance of the exploitation and exploration capability of the algorithm. Finally, as an example to real-world applications, the DTNSGAII is used to solve a network-wide flight trajectory planning problem, which demonstrates that the application of dynamic topology can improve the performance of the NSGA-II.

关键词： multi-objective evolutionary algorithm complex network flight trajectory planning

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An effective hybrid multi objective evolutionary algorithm for solving real time event in flexible job shop scheduling problem

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MEASUREMENT 2018年 114卷 78-90页

作者： Reddy, M. B. S. Sreekara Ratnam, Ch. Rajyalakshmi, G. Manupati, V. K. KL Univ Dept Mech Engn Green Fields Vaddeswaram Andhra Pradesh India AUCE Dept Mech Engn Visakha Patnam Andhra Pradesh India VIT Univ Dept Mfg Engn Vellore Tamil Nadu India VIT Univ Dept Mech Engn Vellore Tamil Nadu India

This paper addresses the multi-objective model for a flexible job shop scheduling problem (FJSSP) to improve the system performance under the condition of machines break down as a real time event. It is important to identify the relevant performance measures to the mentioned problem for examining the system performance. Therefore, minimization of make span and minimization of total machine load variation is considered as two performance measures. Generally, it is very difficult to develop a mathematical model for the real-time situations in FJSSP. Hence, in this paper we divided the research work into two folds: Primarily, a mixed-integer non-linear programming (MINLP) model has been developed to represent the above-mentioned multi-objectives that subjected to constraints without considering machines break down. Secondarily, by incorporating the machines break down as the real-time event the performance of the system is examined. Solving conflicting objectives simultaneously for finding the optimal/near optimal solutions in a reasonable time is a challenge. In this paper, we proposed a new evolutionary based multi-objective teacher learning-based optimization algorithm (MOTLBO) to solve the above-mentioned complex problem. Moreover, to improve the obtained solutions a local search technique has been incorporated in the MOTLBO and comparisons has been made with existing multi-objective particle swarm optimization (MOPSO) and conventional non-dominated sorting genetic algorithm (CNSGA-II). Results found that the proposed multi-objective-based hybrid meta-heuristic algorithm produced high-quality solutions as proved by the tests we performed over a number of randomly generated test problems. Finally, comparisons also made with how the machines break down can affect the proposed systems performance.

关键词： Flexible job shop multi-objective evolutionary algorithm NP-hard Optimization

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A multi-objective evolutionary algorithm for Emergency Logistics Scheduling in Large-scale Disaster Relief

A Multi-objective Evolutionary Algorithm for Emergency Logis...

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IEEE Congress on evolutionary Computation (CEC)

作者： Gan, Xiaohui Liu, Jing Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9781509046010

The emergency logistics scheduling (ELS) is to enable the dispatch of emergency supplies to the victims of disasters timely and effectively, which plays a crucial role in large-scale disaster relief. In this paper, we first design a new multi-objective model that considers both the total unsatisfied time and transportation cost for the ELS problem in large-scale disaster relief (ELSP-LDR), which is on the scenery of multi-disasters and multi-suppliers with several kinds of resources and vehicles. Then, a modified non-dominated sorting genetic algorithm II (mNSGA-II) is proposed to search for a variety of optimal emergency scheduling plans for decision-makers. With the intrinsic properties of ELSP-LDR in mind, we design three repair operators to generate improved feasible solutions. Compared with the original NSGA-II, a local search operator is also designed for mNSGA-II, which significantly improves the performance. We conduct two experiments (the case of Chi-Chi earthquake and Great Sichuan Earthquake) to validate the performance of the proposed algorithm.

关键词： emergency logistics scheduling disaster relief multi-objective evolutionary algorithm non-dominated sorting

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A multi-objective evolutionary algorithm-based soft computing model for educational data mining: A distance learning experience

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Asian Association of Open Universities Journal 2017年第1期12卷 106-123页

作者： Tan, Choo Jun Lim, Ting Yee Bong, Chin Wei Liew, Teik Kooi School of Science and Technology Wawasan Open University Penang Malaysia Department Registry Directorate of Quality Assurance and External Relations Wawasan Open University Penang Malaysia

Purpose: The purpose of this paper is to propose a soft computing model based on multi-objective evolutionary algorithm (MOEA), namely, modified micro genetic algorithm (MmGA) coupled with a decision tree (DT)-based classifier, in classifying and optimising the students’ online interaction activities as classifier of student achievement. Subsequently, the results are transformed into useful information that may help educator in designing better learning instructions geared towards higher student achievement. Design/methodology/approach: A soft computing model based on MOEA is proposed. It is tested on benchmark data pertaining to student activities and achievement obtained from the University of California at Irvine machine learning repository. Additional, a real-world case study in a distance learning institution, namely, Wawasan Open University in Malaysia has been conducted. The case study involves a total of 46 courses collected over 24 consecutive weeks with students across the entire regions in Malaysia and worldwide. Findings: The proposed model obtains high classification accuracy rates at reduced number of features used. These results are transformed into useful information for the educational institution in our case study in an effort to improve student achievement. Whether benchmark or real-world case study, the proposed model successfully reduced the number features used by at least 48 per cent while achieving higher classification accuracy. Originality/value: A soft computing model based on MOEA, namely, MmGA coupled with a DT-based classifier, in handling educational data is proposed. © 2017, Choo Jun Tan, Ting Yee Lim, Chin Wei Bong and Teik Kooi Liew.

关键词： Classification Educational data mining multi-objective evolutionary algorithm Optimization Student achievement

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A novel model-based multi-objective evolutionary algorithm

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2016年第2期7卷 177-189页

作者： Wang, Maocai Dai, Guangming Peng, Lei Song, Zhiming Mo, Li China Univ Geosci Sch Comp Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Peoples R China

In multi-objective evolutionary algorithm (MOEA), modelling method is a crucial part. Moreover, variable linkages enable the modelling process more complex for multi-objective optimisation problems. The Karush-Kulm-Tucker condition shows that the Pareto set of a continuous MOP with m objectives is a piecewise continuous (m-1)-dimensional manifold. How to use this regularity property to model continuous MOP with variable linkages has been the research focus. In this paper, a model-based multi-objective evolutionary algorithm based on regression analysis (MMEA-RA) for continuous multi-objective optimisation problems with variable linkages is put forward. In the algorithm, the optimisation problem is modelled as a promising area in the decision space by a probability distribution, and the centroid of the probability distribution is (m-1)-dimensional piecewise continuous manifold. The least squares algorithm is used to build such a model. Systematic experiments have shown that, compared with two state-of-the-art algorithms, MMEA-RA performs excellent on a set of test instances with variable linkages.

关键词： multi-objective evolutionary algorithm MOEA least squares algorithm model-based algorithm regression analysis

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Selection Model Based on multi-objective evolutionary algorithm for Coordinated multi Point Schemes 22

Selection Model Based on Multi-Objective Evolutionary Algori...

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22nd Asia-Pacific Conference on Communications (APCC)

作者： Lozano-Garzon, Carlos Donoso, Yezid Vila, Pere Univ Los Andes Syst & Comp Engn Dept Bogota Colombia Univ Girona Inst Informat & Aplicac Girona Spain

ISBN: (纸本)9781509006762

The capacity, coverage and interference problems in the infrastructure deployed by mobile operators are increasing due to exponential growth of subscribers and traffic requirements of the new services. In order to solve these problems, 3GPP proposed the Coordinated multi Point (CoMP) which is a set of cooperative communication techniques between base stations. In this paper we address the decision problem as a multi-objective optimization problem with the aim of improving the user's quality of experience and the load of all base stations involved. We propose an optimization model as well as an evolutionary algorithm based on the SPEA2 algorithm, to select the best cooperation technique over the download channel. Through simulation over experimental scenarios, we demonstrate that our proposal provides optimal solutions, while being efficient and scalable.

关键词： Coordinated multi Point Dynamic Point Selection Joint Transmission Load Balancing multi-objective evolutionary algorithm Quality of Experience

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Optimising a Waste Heat Recovery System using multi-objective evolutionary algorithm

Optimising a Waste Heat Recovery System using Multi-Objectiv...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Mokhtar, Maizura Hunt, Ian Burns, Stephen Ross, Dave Edinburgh Napier Univ Sch Engn & Built Environm Merchiston Campus Edinburgh EH10 5DT Midlothian Scotland Roslin Inst Bldg Scotlands Rural Coll Easter Bush EH25 9RG Midlothian Scotland

ISBN: (纸本)9781450343237

A waste heat recovery system (WHRS) on a process with variable output, is an example of an intermittent renewable process. WHRS recycles waste heat into usable energy. As an example, waste heat produced from refrigeration can be used to provide hot water. However, consistent with most intermittent renewable energy systems, the likelihood of waste heat availability at times of demand is low. For this reason, the WHRS may be coupled with a hot water reservoir (HWR) acting as the energy storage system that aims to maintain desired hot water temperature T-d (and therefore energy) at time of demand. The coupling of the WHRS and the HWR must be optimised to ensure higher efficiency given the intermittent mismatch of demand and heat availability. Efficiency of an WHRS can be defined as achieving multiple objectives, including to minimise the need for back-up energy to achieve T-d, and to minimise waste heat not captured (when the reservoir volume V-res is too small). This paper investigates the application of a multi objective evolutionary algorithm (MOEA) to optimise the parameters of the WHRS, including the V-res and depth of discharge (DoD), that affect the WHRS efficiency. Results show that one of the optimum solutions obtained requires the combination of high V-res, high DoD, low water feed in rate, low power external back-up heater and high excess temperature for the HWR to ensure efficiency of the WHRS.

关键词： multi-objective evolutionary algorithm waste heat recovery system process integration renewable energy system optimisation

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Use of multi-objective evolutionary algorithm Optimization for Low Impact Development Placement

Use of Multi-Objective Evolutionary Algorithm Optimization f...

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International Low Impact Development Conference (LID)

作者： Giacomoni, Marcio H. Univ Texas San Antonio Dept Civil & Environm Engn One UTSA Circle San Antonio TX 78249 USA

ISBN: (数字)9780784479025

ISBN: (纸本)9780784479025

The transformation of natural land cover to urban areas severely alters the hydrologic flow regime of watersheds. The negative impacts include the increase of surface runoff and decrease of infiltration rates, which can result in more frequent and intense flood events and the reduction of groundwater recharge. Low Impact Developments (LIDs) are strategies designed to better mimic the natural flow regime by promoting higher infiltration and the treatment of stormwater. Examples of LID structures are bio-gardens, green roofs, bio-swales and pervious pavements. While the expansion of LIDs in urban catchments would be desirable, retrofitting large urban watersheds with LIDs can be cost prohibitive. This study combines hydrologic simulation with a multi-objective evolutionary algorithm (MOEA) to find solutions in terms of LID design and location in urban catchments that maximize the environmental benefits and characterize the tradeoffs between LID performance indicators and costs. The solutions are evaluated in terms of costs, peak flow, and the Hydrologic Footprint Residence (HFR). The HFR is a new metric designed to quantify the impacts of urbanization in the hydrologic regime by representing the dynamics of inundated areas and residence time of flood waves. The results provide a basis for better stormwater management and planning because tradeoff curves provide a wider spectrum of designs and placement guidelines, improving the sustainability of urban watersheds.

关键词： flow regime Urban watersheds Hydrological regime multi-objective evolutionary algorithm low impact algorithm optimization land cover tradeoffs HFR REACTOR

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A multi-objective evolutionary algorithm for Interaction Systems Based on Laser Pointers 18

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18th European Conference on Applications of evolutionary Computation (EvoApplications) as part of EvoSTAR Conference

作者： Chavez, Francisco Clemente, Eddie Hernandez, Daniel E. De Vega, Francisco Fernandez Olague, Gustavo Univ Extremadura Dept Comp Sci C Santa Teresa De Jornet 38 Merida 06800 Spain Ctr Invest Cient & Educ Super Ensenada Carretera Ensenada Tijuana 3918 Ensenada 22860 BC Mexico

ISBN: (纸本)9783319165486;9783319165493

In this paper we face the problem of accurate location of a laser spot that is used as interaction system in real environments. The work presented is compared with previous approaches where different algorithms work with a single objective, using images that has been previously simplified to reduce computing time. Instead, the new approach presented in this paper is capable of processing whole images. The results show that the inclusion of multi-objective methods allows us not only to detect the presence of the laser spot, the single objective in previous works, but also to obtain accurate information of the laser spot in the image, and thus provide the location of the device on which the user wants to act.

关键词： multi-objective evolutionary algorithm Laser pointer Interaction systems

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