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A diagnostic assessment of evolutionary algorithms for multi-objective surface water reservoir control

ADVANCES IN WATER RESOURCES 2016年 92卷 172-185页

作者： Salazar, Jazmin Zatarain Reed, Patrick M. Herman, Jonathan D. Giuliani, Matteo Castelletti, Andrea Cornell Univ Sch Civil & Environm Engn Ithaca NY 14853 USA Univ Calif Davis Dept Civil & Environm Engn Davis CA 95616 USA Politecn Milan Dept Elect Informat & Bioengn I-20133 Milan Italy

Globally, the pressures of expanding populations, climate change, and increased energy demands are motivating significant investments in re-operationalizing existing reservoirs or designing operating policies for new ones. These challenges require an understanding of the tradeoffs that emerge across the complex suite of multi-sector demands in river basin systems. This study benchmarks our current capabilities to use evolutionary multi-objective Direct Policy Search (EMODPS), a decision analytic framework in which reservoirs' candidate operating policies are represented using parameterized global approximators (e. g., radial basis functions) then those parameterized functions are optimized using multi-objective evolutionary algorithms to discover the Pareto approximate operating policies. We contribute a comprehensive diagnostic assessment of modern MOEAs' abilities to support EMODPS using the Conowingo reservoir in the Lower Susquehanna River Basin, Pennsylvania, USA. Our diagnostic results highlight that EMODPS can be very challenging for some modern MOEAs and that epsilon dominance, time-continuation, and auto-adaptive search are helpful for attaining high levels of performance. The epsilon-MOEA, the auto-adaptive Borg MOEA, and epsilon-NSGAII all yielded superior results for the six-objective Lower Susquehanna bench-marking test case. The top algorithms show low sensitivity to different MOEA parameterization choices and high algorithmic reliability in attaining consistent results for different random MOEA trials. Overall, EMODPS poses a promising method for discovering key reservoir management tradeoffs;however algorithmic choice remains a key concern for problems of increasing complexity. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： multi-purpose reservoir control Direct policy search multi-objective evolutionary algorithm Benchmark

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A New evolutionary multi-objective algorithm for Convex Hull Maximization

A New Evolutionary Multi-objective Algorithm for Convex Hull...

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IEEE Congress on evolutionary Computation (CEC)

作者： Hong, Wenjing Lu, Guanzhou Yang, Peng Wang, Yong Tang, Ke Univ Sci & Technol China Sch Comp Sci & Technol USTC Birmingham Joint Res Inst Intelligent Comput Hefei 230027 Peoples R China Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

ISBN: (纸本)9781479974924

Many real-world problems often have several, usually conflicting objectives. Traditional multi-objective optimization problems (MOPs) usually search for the Pareto-optimal solutions for this predicament. A special class of MOPs, the convex hull maximization problems which prefer solutions on the convex hull, has posed a new challenge for existing approaches for solving traditional MOPs, as a solution on the Pareto front is not necessarily a good solution for convex hull maximization. In this work, the difference between traditional MOPs and the convex hull maximization problems is discussed and a new evolutionary Convex Hull Maximization algorithm (ECHMA) is proposed to solve the convex hull maximization problems. Specifically, a Convex Hull-based sorting with Convex Hull of Individual Minima (CH-CHIM-sorting) is introduced, as well as a novel selection scheme, Extreme Area Extract-based selection (EAE-selection). Experimental results show that ECHMA significantly outperforms the existing approaches for convex hull maximization and evolutionary multi-objective optimization approaches in achieving a better approximation to the convex hull more stably and with a more uniformly distributed set of solutions.

关键词： multi-objective optimization convex hull maximization multi-objective evolutionary algorithm

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A fuzzy based approach for fitness approximation in multi-objective evolutionary algorithms

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2015年第5期29卷 2111-2131页

作者： Pourbahman, Zahra Hamzeh, Ali Shiraz Univ Dept Elect & Comp Engn Shiraz Iran

evolutionary algorithm provides a framework that is largely applicable to particular problems including multiobjective optimization problems, basically for the ease of their implementation and their capability to perform efficient parallel search. Indeed, in some cases, expensive multiobjective optimization evaluations might be a challenge to restrict the number of explicit fitness evaluations in multiobjective evolutionary algorithms. Accordingly, this article presents a novel approach that tackles this problem so as to not only decrease the number of fitness evaluations but also to improve the performance. During evolution, our proposed approach selects fit individuals based on the knowledge acquired throughout the search, and performs explicit fitness evaluations on these individuals. A comprehensive comparative analysis of a wide range of well-established test problems, selected from both traditional and state-of-the-art benchmarks, has been presented. Afterward, the effectiveness of the obtained results is compared with some of the state-of-the-art methods using two well-known metrics-i.e. Hypervolume and Inverted Generational Distance (IGD). The experiments of our implemented approach is performed to illustrate that our proposal seems to be promising and would prove more efficient than other approaches in terms of both the performance and the computational cost.

关键词： Fitness approximation multi-objective evolutionary algorithm fuzzy inference system hypervolume approximation hypervolume contribution

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Convex hull-based multi-objective evolutionary computation for maximizing receiver operating characteristics performance

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MEMETIC COMPUTING 2016年第1期8卷 35-44页

作者： Hong, Wenjing Tang, Ke Univ Sci & Technol China Sch Comp Sci & Technol USTC Birmingham Joint Res Inst Intelligent Comput Hefei 230026 Peoples R China

The receiver operating characteristics (ROC) analysis has gained increasing popularity for analyzing the performance of classifiers. In particular, maximizing the convex hull of a set of classifiers in the ROC space, namely ROCCH maximization, is becoming an increasingly important problem. In this work, a new convex hull-based evolutionary multi-objective algorithm named ETriCM is proposed for evolving neural networks with respect to ROCCH maximization. Specially, convex hull-based sorting with convex hull of individual minima (CH-CHIM-sorting) and extreme area extraction selection (EAE-selection) are proposed as a novel selection operator. Empirical studies on 7 high-dimensional and imbalanced datasets show that ETriCM outperforms various state-of-the-art algorithms including convex hull-based evolutionary multi-objective algorithm (CH-EMOA) and non-dominated sorting genetic algorithm II (NSGA-II).

关键词： ROC convex hull multi-objective evolutionary algorithm evolutionary neural networks Selection operator

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multi-objective Energy Optimization Of Locomotions For Underwater Snake Robot

Multi-objective Energy Optimization Of Locomotions For Under...

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作者： Kenny Duy Luong Norwegian University of Science and Technology

学位级别：硕士

Biologically inspired Autonomous Underwater Vehicles (AUVs) have been developed in the recent decades. Tis thesis focuses on the AUVs that are biologically inspired by snakes, called Underwater Snake Robots (USRs). A well-known issue of the USRs, or any AUVs, is the long- term autonomy. To achieve this, energy efcient approaches are required. Many studies have considered single-objective optimization problems regarding the energy efciency of the USR, but almost none with multi-objective Optimization Problems (MOPs). Tis thesis presents MOPs of diferent locomotions of the USR. Te presented MOPs consider the energy efcient optimiza- tion of maximizing the forward velocity, while minimizing the power consumption of the USR. For computing the efcient motion paterns, two multi-objective evolutionary algorithms (MOEAs) called Non-dominated Sort Genetic algorithm II (NSGA-II), and Hypervolume Estimation Algo- rithm for multi-objective Optimization (HypE) are applied. A challenging topic of the USR, is their adaptability of diferent locomotions. Diferent locomotions of the USR give rise to diferent search spaces for optimization. We present simulation studies of the two most common snake locomotions: (i) lateral undulation and (ii) eel-like motion. Furthermore, we also present and in- vestigate three altered motion patern of the USR. Te aim of the altered locomotions is to let the MOEAs generate efcient locomotions through evolutionary, which we do not know the gait of. From the simulation results, it turns out that one of the altered motion patern approximates a motion similar to the lateral undulation. Tis motion patern is generated based on Fourier se- ries. Te obtained simulation results are based on optimization with optimal Genetic algorithm (GA) parameters, found by numerous presimulations of the MOPs. Since this is multi-objective optimization problems, the end results will be in the form of Pareto fronts. Tese Pareto fronts can be used as trade-ofs for selecting th

关键词： Underwater snake robot Energy efciency multi-objective evolutionary algorithm Non-dominated Sort Genetic algorithm II Hypervolume Estimation algorithm for multi-objective Optimization Principal Component Analysis Partial Least Square Regression multi-objective Optimization Problem Pareto fronts Patern generators

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A fast multi-objective evolutionary algorithm based on a tree structure

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APPLIED SOFT COMPUTING 2010年第2期10卷 468-480页

作者： Shi, Chuan Yan, Zhenyu Shi, Zhongzhi Zhang, Lei Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing 100876 Peoples R China Univ Virginia Dept Syst & Informat Engn Charlottesville VA 22903 USA Chinese Acad Sci Inst Comp Technol Beijing 100080 Peoples R China

This paper proposes a fast evolutionary algorithm based on a tree structure for multi-objective optimization. The tree structure, named dominating tree (DT), is able to preserve the necessary Pareto dominance relations among individuals effectively, contains the density information implicitly, and reduces the number of comparisons among individuals significantly. The evolutionary algorithm based on dominating tree (DTEA) integrates the convergence strategy and diversity strategy into the DT and employs a DT-based eliminating strategy that realizes elitism and preserves population diversity without extra time and space costs. Numerical experiments show that DTEA is much faster than SPEA2, NSGA-II and an improved version of NSGA-II, while its solution quality is competitive with those of SPEA2 and NSGA-II. Crown Copyright (C) 2009 Published by Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithm Pareto dominance Fitness assignment Eliminating strategy

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Optimum plate-fin heat sinks by using a multi-objective evolutionary algorithm

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ENGINEERING OPTIMIZATION 2010年第4期42卷 305-323页

作者： Bureerat, S. Srisomporn, S. Khon Kaen Univ Fac Engn Dept Mech Engn Khon Kaen 40002 Thailand

This article demonstrates the practical applications of a multi-objective evolutionary algorithm (MOEA) namely population-based incremental learning (PBIL) for an automated shape optimization of plate-fin heat sinks. The computational procedure of multi-objective PBIL is detailed. The design problem is posed to find heat sink shapes which minimize the junction temperature and fan pumping power while meeting predefined constraints. Three sets of shape design variables used in this study are defined as: vertical straight fins with fin height variation, oblique straight fins with steady fin heights, and oblique straight fins with fin height variation. The optimum results obtained from using the various sets of design variables are illustrated and compared. It can be said that, with this sophisticated design system, efficient and effective design of plate-fin heat sinks is achievable and the best design variables set is the oblique straight fins with fin height variation.

关键词： heat sink optimum design multi-objective evolutionary algorithm finite volume method shape optimization computational fluid dynamics

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BICLUSTERING ANALYSIS OF GENE EXPRESSION DATA USING multi-objective evolutionary algorithmS 14

BICLUSTERING ANALYSIS OF GENE EXPRESSION DATA USING MULTI-OB...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Golchin, Maryam Davarpanah, Seyed Hashem Liew, Alan Wee-Chung Griffith Univ Sch Informat & Commun Technol Nathan Qld 4111 Australia

ISBN: (纸本)9781467372206

Clustering is an unsupervised learning technique that groups data into clusters using the entire conditions. However, sometimes, data is similar only under a subsetof conditions. Biclustering allows clustering of rows and columns of a dataset simultaneously. It extracts more accurate information from sparse datasets. In recent years, biclustering has found many useful applications in different fields and many biclustering algorithms have been proposed. Using both row and column information of data, biclustering requires the optimization of two conflicting objectives. In this study, a new multi-objective evolutionary biclustering framework using SPEA2 is proposed. A heuristic local search based on the gene and condition deletion and addition is added into SPEA2 and the best bicluster is selected usinga new quantitative measure that considers both its coherence and size. The performance of our algorithm is evaluatedusing simulated and gene expression data and compared with several well-known biclustering methods. The experimental results demonstrate better performance with respect to the size and MSR of detected biclusters and significant enrichment of detected genes.

关键词： Biclustering Gene expression data multi-objective evolutionary algorithm SPEA2

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multi-objective optimisation and decision-making of space station logistics strategies

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2016年第13期47卷 3132-3148页

作者： Zhu, Yue-he Luo, Ya-zhong Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha Hunan Peoples R China

Space station logistics strategy optimisation is a complex engineering problem with multiple objectives. Finding a decision-maker-preferred compromise solution becomes more significant when solving such a problem. However, the designer-preferred solution is not easy to determine using the traditional method. Thus, a hybrid approach that combines the multi-objective evolutionary algorithm, physical programming, and differential evolution (DE) algorithm is proposed to deal with the optimisation and decision-making of space station logistics strategies. A multi-objective evolutionary algorithm is used to acquire a Pareto frontier and help determine the range parameters of the physical programming. Physical programming is employed to convert the four-objective problem into a single-objective problem, and a DE algorithm is applied to solve the resulting physical programming-based optimisation problem. Five kinds of objective preference are simulated and compared. The simulation results indicate that the proposed approach can produce good compromise solutions corresponding to different decision-makers' preferences.

关键词： Space station logistics multi-objective evolutionary algorithm physical programming differential evolution algorithm decision-making preference

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A multi-objective memetic algorithm based on locality-sensitive hashing for one-to-many-to-one dynamic pickup-and-delivery problem

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INFORMATION SCIENCES 2016年 329卷 73-89页

作者： Zhu, Zexuan Xiao, Jun He, Shan Ji, Zhen Sun, Yiwen Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England Shenzhen Univ Dept Biomed Engn Shenzhen 518060 Peoples R China

This paper presents an early attempt to solve one-to-many-to-one dynamic pickup-and-delivery problem (DPDP) by proposing a multi-objective memetic algorithm called LSH-MOMA, which is a synergy of multi-objective evolutionary algorithm and locality-sensitive hashing (LSH) based local search. Three objectives namely route length, response time, and workload are optimized simultaneously in an evolutionary framework. In each generation of LSH-MOMA, LSH-based rectification and local search are imposed to repair and improve the individual solutions. LSH-MOMA is evaluated on four benchmark DPDPs and the experimental results show that LSH-MOMA is efficient in obtaining optimal tradeoff solutions of the three objectives. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Memetic algorithm multi-objective evolutionary algorithm Dynamic pickup and delivery problem Locality-sensitive hashing

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