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Comparison of multiobjective Optimization Methods Applied to Urban Drainage Adaptation Problems

JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2018年第11期144卷 04018070-04018070页

作者： Wang, Qi Zhou, Qianqian Lei, Xiaohui Savic, Dragan A. Guangdong Univ Technol Sch Civil & Transportat Engn Guangzhou 510006 Guangdong Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River Beijing 100038 Peoples R China Univ Exeter Coll Engn Math & Phys Sci Ctr Water Syst North Pk Rd Exeter EX4 4QF Devon England

This article compares three multiobjective evolutionary algorithms (MOEAs) with application to the urban drainage system (UDS) adaptation of a capital city in North China. Particularly, we consider the well-known NSGA-II, the built-in solver in the MATLAB Global Optimization Toolbox (MLOT), and a newly-developed hybrid MOEA called GALAXY. A variety of parameter combinations of each MOEA is systemically applied to examine their impacts on optimization efficiency. Results suggest that the traditional MOEAs suffer from severe parameterization issues. For NSGA-II, the distribution indexes of crossover and mutation operators were found to have dominant impacts, while the probabilities of the two operators played a secondary role. For MLOT, the two-point and the scattered crossover operators accompanied by the adaptive-feasible mutation operator gained the best Pareto fronts, provided the crossover fraction is set to lower values. In contrast, GALAXY was the most robust and easy-to-use tool among the three MOEAs, owing to its elimination of various associated parameters of searching operators for substantially alleviating the parameterization issues. This study contributes to the literature by showing how to improve the robustness of identifying optimal solutions through better selection of operators and associated parameter settings for real-world UDS applications.

关键词： Urban drainage system adaptation multiobjective evolutionary algorithm Method selection Parameter setting

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Curses, Tradeoffs, and Scalable Management: Advancing evolutionary multiobjective Direct Policy Search to Improve Water Reservoir Operations

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2016年第2期142卷 4015050-1-4015050-17页

作者： Giuliani, Matteo Castelletti, Andrea Pianosi, Francesca Mason, Emanuele Reed, Patrick M. Politecn Milan Dept Elect Informat & Bioengn Pza Leonardo da Vinci 32 I-20133 Milan Italy ETH Inst Environm Engn Ramistr 101 CH-8092 Zurich Switzerland Univ Bristol Dept Civil Engn Queens BldgUniv Walk Bristol BS8 1TR Avon England Cornell Univ Sch Civil & Environm Engn Ithaca NY 14853 USA

Optimal management policies for water reservoir operation are generally designed via stochastic dynamic programming (SDP). Yet, the adoption of SDP in complex real-world problems is challenged by the three curses of dimensionality, modeling, and multiple objectives. These three curses considerably limit SDP's practical application. Alternatively, this study focuses on the use of evolutionary multiobjective direct policy search (EMODPS), a simulation-based optimization approach that combines direct policy search, nonlinear approximating networks, and multiobjective evolutionary algorithms to design Pareto-approximate closed-loop operating policies for multipurpose water reservoirs. This analysis explores the technical and practical implications of using EMODPS through a careful diagnostic assessment of the effectiveness and reliability of the overall EMODPS solution design as well as of the resulting Pareto-approximate operating policies. The EMODPS approach is evaluated using the multipurpose Hoa Binh water reservoir in Vietnam, where water operators are seeking to balance the conflicting objectives of maximizing hydropower production and minimizing flood risks. A key choice in the EMODPS approach is the selection of alternative formulations for flexibly representing reservoir operating policies. This study distinguishes between the relative performance of two widely-used nonlinear approximating networks, namely artificial neural networks (ANNs) and radial basis functions (RBFs). The results show that RBF solutions are more effective than ANN ones in designing Pareto approximate policies for the Hoa Binh reservoir. Given the approximate nature of EMODPS, the diagnostic benchmarking uses SDP to evaluate the overall quality of the attained Pareto-approximate results. Although the Hoa Binh test case's relative simplicity should maximize the potential value of SDP, the results demonstrate that EMODPS successfully dominates the solutions derived via SDP. (C) 2015 America

关键词： Water management Direct policy search multiobjective evolutionary algorithm

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Solving the exam timetabling problem via a multi-objective evolutionary algorithm - A more general approach

Solving the exam timetabling problem via a multi-objective e...

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IEEE Symposium on Computational Intelligence in Scheduling

作者： Cheong, C. Y. Tan, K. C. Veeravalli, B. Natl Univ Singapore Dept Elect & Comp Engn 4 Engn Dr 3 Singapore 117576 Singapore

ISBN: (纸本)9781424407040

This paper studies a multi-objective instance of the university exam timetabling problem. On top of satisfying universal hard constraints such as seating capacity and no overlapping exams, the solution to this problem requires the minimization of the timetable length as well as the number of occurrences of students having to take exams in consecutive periods within the same day. While most existing approaches to the problem, as well as the more popular single-objective instance, require prior knowledge of the desired timetable length, the multi-objective evolutionary algorithm proposed in this paper is able to generate feasible solutions even without the information. The effectiveness of the proposed algorithm is benchmarked against a few recent and established optimization techniques and is found to perform well in comparison.

关键词： education evolutionary computation minimisation scheduling minimization multiobjective evolutionary algorithm seating capacity universal hard constraints university exam timetabling problem Seating capacity evolutionary algorithm multi-objective evolutionary algorithm

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Multi-objective Mobile Agent-based Sensor Network Routing using MOEA/D

Multi-objective Mobile Agent-based Sensor Network Routing us...

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2010 IEEE World Congress on Computational Intelligence

作者： Konstantinidis, Andreas Charalambous, Christoforos Zhou, Aimin Zhang, Qingfu Frederick Univ Dept Comp Sci & Engn Nicosia Cyprus East China Normal Univ Dept Comp Sci & Technol Shanghai Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester Essex CO4 3SQ England

ISBN: (纸本)9781424481262

Mobile agents are often used in wireless sensor networks for distributed target detection with the goal of minimizing the transmission of non-critical data that negatively affects the performance of the network. A challenge is to find optimal mobile agent routes for minimizing the data path loss and the sensors energy consumption as well as maximizing the data accuracy. Existing approaches deal with the objectives individually, or by optimizing one and constraining the others or by combining them into a single objective. This often results in missing "good" tradeoff solutions. Only few approaches have tackled the Mobile Agent-based Distributed Sensor Network Routing problem as a multiobjective Optimization Problem (MOP) using conventional Multi-Objective evolutionary algorithms (MOEAs). It is well known that the incorporation of problem specific knowledge in MOEAs is a difficult task. In this paper, we propose a problem-specific MOEA based on Decomposition (MOEA/D) for optimizing the three objectives. Experimental studies have shown that the proposed problem-specific approach performs better than two conventional MOEAs in several WSN test instances.

关键词： MOEA/D WSN data communication data path loss minimization decomposition based MOEA distributed target detection energy consumption evolutionary computation mobile agent based sensor network mobile agents multiobjective evolutionary algorithm multiobjective optimization problem network routing network routing object detection wireless sensor network wireless sensor networks

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An Efficient Multi-Objective evolutionary algorithm for combinational circuit design

An Efficient Multi-Objective Evolutionary Algorithm for comb...

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1st NASA/ESA Conference on Adaptive Hardware and Systems

作者： Liu, Rui Zeng, Sang-you Ding, Lixin Kang, Lishan Li, Hui Chen, Yuping Liu, Yong Han, Yueping China Univ Geosci Dept Comp Sci & Technol Wuhan 430074 Peoples R China Zhuzhou Inst Technol Dept Comp Sci & Technol Zhuzhou 412008 Peoples R China Wuhan Univ State Key Lab Software Engn Wuhan 430072 Peoples R China Shandong Inst Aerosp Elect Technol Shandong 26400 Peoples R China

ISBN: (纸本)0769526144

In this paper we introduce an Efficient Multi-Objective evolutionary algorithm (EMOEA) to design circuits. The algorithm is based on non-dominated set for keeping diversity of the population and therefore, avoids trapping in local optimal. Encoding of the chromosome is based on J. F. Miller's implementation[1], but we use efficient methods to evaluate and evolve circuits for speeding up the convergence of the algorithm. This algorithm evolves complex combinational circuits (such as 3-bit multiplier and 4 bit full adder) without too much long time evolution (commonly less than 5, 000, 000).

关键词： gartesian genetic programming combinational logic circuit multiobjective evolutionary algorithm

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Solving the Biobjective Selective Pickup and Delivery Problem with Memetic algorithm

Solving the Biobjective Selective Pickup and Delivery Proble...

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IEEE Symposium on Computational Intelligence in Production and Logistics Systems (CIPLS)

作者： Liao, Xin-Lan Ting, Chuan-Kang Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Chiayi 621 Taiwan Natl Chung Cheng Univ Adv Inst Mfg High Tech Innovat Chiayi 621 Taiwan

ISBN: (纸本)9781467359054

The pickup and delivery problem (PDP) arises in many real-world scenarios such as logistics and robotics. This problem combines the traveling salesman problem (or the vehicle routing problem) and object distribution. The selective pickup and delivery problem (SPDP) is a novel variant of the PDP that enables selectivity of pickup nodes for particular applications. Specifically, the SPDP seeks a shortest route that can supply all delivery nodes with required commodities from some pickup nodes. The two key factors in the SPDP travel distance and vehicle capacity required form a tradeoff in essence. This study formulates the biobjective selective pickup and delivery problem (BSPDP) for minimization of travel distance and vehicle capacity required. To resolve the BSPDP, we propose a multiobjective memetic algorithm (MOMA) based on NSGA-II and local search. Furthermore, a repair strategy is developed for the MOMA to handle the constraint on vehicle load. Experimental results validate the efficacy of the proposed algorithm in approaching the lower bounds of both objectives. Moreover, the results demonstrate the characteristics of the BSPDP.

关键词： Pickup and delivery selectivity multiobjective optimization multiobjective evolutionary algorithm memetic algorithm logistics

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Designing comminution circuits with a multi-objective evolutionary algorithm

Designing comminution circuits with a multi-objective evolut...

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IEEE Congress on evolutionary Computation

作者： Huband, S Barone, L Hingston, P While, L Tuppurainen, D Bearman, R Edith Cowan Univ Sch Comp & Informat Sci Mt Lawley 6050 Australia

ISBN: (纸本)0780393635

Mining is an important industry in Australia, contributing billions of dollars to the economy. The performance of a processing plant has a large impact on the profitability of a mining operation, yet plant design decisions are typically guided more by intuition and experience than by analysis. In this paper, we motivate the use of an evolutionary algorithm to aid in the design of such plants. We formalise plant design in terms suitable for application in a multi-objective evolutionary algorithm and create a simulation to assess the performance of candidate solutions. Results show the effectiveness of this approach with our algorithm producing designs superior to those used in practice today, promising significant financial benefits.

关键词： evolutionary computation facilities layout industrial plants mining process design comminution circuit design multiobjective evolutionary algorithm plant design

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Application of evolutionary algorithm on a transportation scheduling problem - The Mass Rapid Transit

Application of Evolutionary Algorithm on a transportation sc...

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IEEE Congress on evolutionary Computation

作者： Kwan, CM Chang, CS Natl Univ Singapore Dept Elect & Comp Engn Singapore 119260 Singapore

ISBN: (纸本)0780393635

This paper deals with the application of a heuristic-based evolutionary algorithms (EAs) on a specific industrial problem-the scheduling of rapid transit systems. The system under consideration is a medium-sized mass rapid transit (MRT) system and the dual objectives of minimizing operating costs and passenger dissatisfaction are considered. Making use of concepts such as Pareto-optimality and multiobjective evolutionary algorithms, the authors applied a multiobjective evolutionary algorithm (MOEA) to solve the problem. Comparison studies were done with current method, obtaining satisfactory results.

关键词： Pareto optimisation costing evolutionary computation heuristic programming rail traffic scheduling transportation MOEA MRT system Pareto-optimality heuristic algorithm mass rapid transit multiobjective evolutionary algorithm passenger dissatisfaction tra mass rapid transit evolutionary algorithm Heuristic programming Pareto optimisation rail traffic Heuristic algorithms transport Pareto optimality Triangulum Australe

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A Multi-objective evolutionary algorithm with an Interpretability Improvement Mechanism for Linguistic Fuzzy Systems with Adaptive Defuzzification

A Multi-objective Evolutionary Algorithm with an Interpretab...

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2010 IEEE World Congress on Computational Intelligence

作者： Marquez, Antonio A. Marquez, Francisco A. Peregrin, Antonio Univ Huelva Dept Informat Technol Palos De La Fra Huelva 21819 Spain

ISBN: (纸本)9781424469208

In this paper we propose a multi-objective evolutionary algorithm with a mechanism to improve the interpretability in the sense of complexity for Linguistic Fuzzy Rule based Systems with adaptive defuzzification. The use of parameters in the defuzzification operator introduces a series of values or associated weights to each rule, which improves the accuracy but increases the system complexity and therefore has an effect on the system interpretability. To this end, we use maximizing the accuracy as an unusual objective for the evolutionary process, and we defined objectives related with interpretability, using three metrics: minimizing the classical number of rules, the number of rules, with weights associated and the average number of rules triggered by each example. The proposed method was compared in an experimental study with a single objective accuracy-guided algorithm in two real problems showing that many solutions in the Pareto front dominate those obtained by the single objective-based one.

关键词： Pareto front Pareto optimisation adaptive defuzzification computational complexity computational linguistics evolutionary computation evolutionary process fuzzy set theory interpretability improvement mechanism linguistic fuzzy systems multiobjective evolutionary algorithm system complexity

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Integrated Location-Inventory Retail Supply Chain Design: A Multi-objective evolutionary Approach

Integrated Location-Inventory Retail Supply Chain Design: A ...

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8th International Conference of Simulated Evolution and Learning

作者： Liao, Shu-Hsien Hsieh, Chia-Lin Tamkang Univ Dept Management Sci & Decis Making Taipei 251 Taiwan

ISBN: (纸本)9783642172977

A supply chain network system is to provide an optimal platform for efficient and effective supply chain management. There's increasingly competitive, multi-channel retail world calls for a radically new strategy for evaluating supply chain network design. Retailers must abandon past practices which look to optimize the number and placement of facilities within traditional networks. A multi-objective optimization procedure which permits a trade-off evaluation for an integrated model is initially presented. This model includes elements of total cost, customer service and flexibility as its objectives and integrates facility location and inventory control decisions. Inventory control issues include economic order quantity, safety stock and inventory replenishment decisions and consider the risk pooling phenomenon to be realized from collaborative initiatives such as vendor-managed inventory. The possibility of a multi-objective evolutionary approach is developed to determine the optimal facility location portfolio and is implemented on a real large retail supply chain in Taiwan to investigate the model performance. Some preliminary results are described.

关键词： multiobjective evolutionary algorithm retail supply chain facility location problem inventory control integrated supply chain model

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