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PITAGORAS-PSP: Including domain knowledge in a multi-objective approach for protein structure prediction

NEUROCOMPUTING 2011年第16期74卷 2675-2682页

作者： Calvo, J. C. Ortega, J. Anguita, M. Univ Granada CITIC UGR Dept Comp Architecture & Technol E-18071 Granada Spain Univ Granada Dept Comp Technol & Architecture E-18071 Granada Spain

Whenever evolutionary algorithms are used to solve certain classes of problems such as those that present a huge search space, the incorporation of problem-specific knowledge is required to achieve adequate levels of performance. in this paper, we propose a multi-objective optimization-based procedure that includes such a domain-specific knowledge to cope with a difficult problem, the protein structure prediction (PSP). This problem is considered to be an open problem as there is no recognized "best" procedure to find solutions. It presents a vast search space and the analysis of each protein conformation requires significant amount of computing time. In our procedure, we provide a reduction of the search space by using the dependent rotamer library and include new heuristics to improve a multi-objective approach to PSP based on the PAES algorithm. As it is shown in the paper, by using benchmark proteins from the CASP8 set, this hybrid PSP procedure provides competitive results when it is compared with some of the better proposals appeared up to now. Crown Copyright (C) 2011 Published by Elsevier B.V. All rights reserved.

关键词： Protein structure prediction evolutionary multi-objective optimization Domain knowledge representations

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Automatic generation of controllers for embodied legged organisms: A pareto evolutionary multi-objective approach

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evolutionary COMPUTATION 2004年第3期12卷 355-394页

作者： Teo, J Abbass, HA Univ Malaysia Sabah Sch Engn & Informat Technol Artificial Intelligence Res Grp Kota Kinabalu 88999 Sabah Malaysia Univ New S Wales ALAR Lab Canberra ACT 2600 Australia

In this paper, we investigate the use of a self-adaptive Pareto evolutionary multi-objective optimization (EMO) approach for evolving the controllers of virtual embodied organisms. The objective of this paper is to demonstrate the trade-off between quality of solutions and computational cost. We show empirically that evolving controllers using the proposed algorithm incurs significantly less computational cost when compared to a self-adaptive weighted sum EMO algorithm, a self-adaptive single-objective evolutionary algorithm (EA) and a hand-tuned Pareto EMO algorithm. The main contribution of the self-adaptive Pareto EMO approach is its ability to produce sufficiently good controllers with different locomotion capabilities in a single run, thereby reducing the evolutionary computational cost and allowing the designer to explore the space of good solutions simultaneously. Our results also show that self-adaptation was found to be highly beneficial in reducing redundancy when compared against the other algorithms. Moreover, it was also shown that genetic diversity was being maintained naturally by virtue of the system's inherent multi-objectivity.

关键词： evolutionary multi-objective optimization embodied cognition physics-based simulation fitness landscapes

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A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers

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APPLIED ENERGY 2015年 151卷 102-118页

作者： Fotouhi Ghazvini, Mohammad Ali Soares, Joao Horta, Nuno Neves, Rui Castro, Rui Vale, Zita Polytech Porto IPP GECAD Res Grp Intelligent Engn & Comp Adv Innovat & Dev P-4200072 Oporto Portugal Univ Lisbon Inst Super Tecn Inst Telecomunicacoes P-1699 Lisbon Portugal Univ Lisbon INESC ID IST P-1699 Lisbon Portugal

In this paper, we formulate the electricity retailers' short-term decision-making problem in a liberalized retail market as a multi-objective optimization model. Retailers with light physical assets, such as generation and storage units in the distribution network, are considered. Following advances in smart grid technologies, electricity retailers are becoming able to employ incentive-based demand response (DR) programs in addition to their physical assets to effectively manage the risks of market price and load variations. In this model, the DR scheduling is performed simultaneously with the dispatch of generation and storage units. The ultimate goal is to find the optimal values of the hourly financial incentives offered to the end-users. The proposed model considers the capacity obligations imposed on retailers by the grid operator. The profit seeking retailer also has the objective to minimize the peak demand to avoid the high capacity charges in form of grid tariffs or penalties. The non-dominated sorting genetic algorithm II (NSGA-II) is used to solve the multi-objective problem. It is a fast and elitist multi-objective evolutionary algorithm. A case study is solved to illustrate the efficient performance of the proposed methodology. Simulation results show the effectiveness of the model for designing the incentive-based DR programs and indicate the efficiency of NSGA-II in solving the retailers' multi-objective problem. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Electricity retail market evolutionary multi-objective optimization Retailer NSGA-II

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Dynamic server placement in edge computing toward Internet of Vehicles

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COMPUTER COMMUNICATIONS 2021年 178卷 114-123页

作者： Shen, Bowen Xu, Xiaolong Qi, Lianyong Zhang, Xuyun Srivastava, Gautam Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Peoples R China Minist Educ Engn Res Ctr Digital Forens Nanjing 210044 Peoples R China Qufu Normal Univ Sch Informat Sci & Engn Jining Peoples R China Macquarie Univ Dept Comp N Ryde NSW Australia Brandon Univ Dept Math & Comp Sci 270 18th St Brandon MB Canada China Med Univ Res Ctr Interneural Comp Taichung 40402 Taiwan

Facing serious challenges in bandwidth and delay, currently adopted cloud computing is no longer effective for providing real-time Internet of Vehicles (IoV) services in the intelligent transportation system (ITS). A newly introduced computing paradigm with a distributed feature, i.e., edge computing, can be a complement to the centralized cloud computing with computation offloaded to the distributed edge servers (ESs). Typically, edge servers are the host of IoV services in edge computing, which requires an appropriate quantification and placement before the implementation of computation offloading. To pursue a higher quality of service (QoS), the quantity and locations of the ESs need to be thoroughly discussed ahead. Otherwise, additional delay and network congestion will occur. Simultaneously, as the ITS is continuously developing, the existing placement of ESs ought to be adjusted to be in line with the dynamic change of IoV traffic. However, ES placement schemes are often devised by clustering methods with a fixed ES quantity and are unaware of the extensibility of the placement. To address the problems that mentioned, a dynamic ES placement approach (DEP) is developed. Technically, DEP leverages the non-dominated sorting genetic algorithm III (NSGA-III) for placements with better performance and less reconstruction of existing placement. The population of NSGAIII is initialized with clustering algorithms for a higher accuracy and convergence speed, and the fitness of minimum reconstruction cost is calculated based on Kuhn-Munkres bipartite graph matching algorithm. A real world traffic data with 436 deployed roadside units in the provincial capital city Nanjing, China, is leveraged for comparative experiments. The experimental results verify the effectiveness of DEP with a 17.64% lower latency and 25.82% lower workload standard deviation comparing to the clustering method.

关键词： Internet of Vehicles Edge server placement evolutionary multi-objective optimization Bipartite graph matching

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multi-objective optimization and decision making approaches to cricket team selection

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APPLIED SOFT COMPUTING 2013年第1期13卷 402-414页

作者： Ahmed, Faez Deb, Kalyanmoy Jindal, Abhilash Indian Inst Technol Kanpur Genet Algorithms Lab KanGAL Kanpur 208016 Uttar Pradesh India Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Aalto Univ Sch Econ Helsinki Finland

Selection of players for a sports team within a finite budget is a complex task which can be viewed as a constrained multi-objective optimization and a multiple criteria decision making problem. The task is specially challenging for the game of cricket where a team requires players who are efficient in multiple roles. In the formation of a good and successful cricket team, batting strength and bowling strength of a team are major factors affecting its performance and an optimum trade-off needs to be reached. We propose a novel gene representation scheme and a multi-objective approach using the NSGA-II algorithm to optimize the overall batting and bowling strength of a team with 11 players as variables. Fielding performance and a number of other cricketing criteria are also used in the optimization and decision-making process. Using the information from the trade-off front obtained, a multi-criteria decision making approach is then proposed for the final selection of team. Case studies using a set of players auctioned in Indian Premier League (IPL) 4th edition are illustrated and players' current statistical data is used to define performance indicators. The proposed computational techniques are ready to be extended according to individualistic preferences of different franchises and league managers in order to form a preferred team within the budget constraints. It is also shown how such an analysis can help in dynamic auction environments, like selecting a team under player-by-player auction. The methodology is generic and can be easily extended to other sports like American football, baseball and other league games. Published by Elsevier B.V.

关键词： Cricket team selection evolutionary multi-objective optimization NSGA-II multi-criteria decision making Trade-off solutions

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How to Specify a Reference Point in Hypervolume Calculation for Fair Performance Comparison

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evolutionary COMPUTATION 2018年第3期26卷 411-440页

作者： Ishibuchi, Hisao Imada, Ryo Setoguchi, Yu Nojima, Yusuke Southern Univ Sci & Technol Shenzhen Key Lab Computat Intelligence Dept Comp Sci & Engn Shenzhen 518005 Peoples R China Osaka Prefecture Univ Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan

The hypervolume indicator has frequently been used for comparing evolutionary multi-objective optimization (EMO) algorithms. A reference point is needed for hypervolume calculation. However, its specification has not been discussed in detail from a viewpoint of fair performance comparison. A slightly worse point than the nadir point is usually used for hypervolume calculation in the EMO community. In this paper, we propose a reference point specification method for fair performance comparison of EMO algorithms. First, we discuss the relation between the reference point specification and the optimal distribution of solutions for hypervolume maximization. It is demonstrated that the optimal distribution of solutions strongly depends on the location of the reference point when a multi-objective problem has an inverted triangular Pareto front. Next, we propose a reference point specification method based on theoretical discussions on the optimal distribution of solutions. The basic idea is to specify the reference point so that a set of well-distributed solutions over the entire linear Pareto front has a large hypervolume and all solutions in such a solution set have similar hypervolume contributions. Then, we examine whether the proposed method can appropriately specify the reference point through computational experiments on various test problems. Finally, we examine the usefulness of the proposed method in a hypervolume-based EMO algorithm. Our discussions and experimental results clearly show that a slightly worse point than the nadir point is not always appropriate for performance comparison of EMO algorithms.

关键词： evolutionary multi-objective optimization hypervolume reference point performance comparison

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Utopian point based decomposition for multi-objective optimization problems with complicated Pareto fronts

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APPLIED SOFT COMPUTING 2017年 61卷 844-859页

作者： Qi, Yutao Zhang, Qingsai Ma, Xiaoliang Quan, Yining Miao, Qiguang Xidian Univ Sch Comp Sci & Technol Xian Shaanxi Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

multi-objective evolutionary algorithm based on decomposition (MOEA/D) has been considered as a promising method for solving multi-objective optimization problems (MOPs). It devotes most of its effort on convergence by optimizing a set of scalar optimization subproblems in a collaborative manner, while maintaining the diversity by using a set of uniformly distributed weight vectors. However, more recent studies illustrated that MOEA/D faces difficulties on MOPs with complicated Pareto fronts, mainly because the uniformity of weight vectors no longer lead to an evenly scattered approximation of the Pareto fronts in these cases. To remedy this, we suggest replacing the ideal point in the reciprocal Tchebycheff decomposition method with a more optimistic utopian point, with the aim of alleviating the sensitivity of MOEA/D to the Pareto front shape of MOPs. Experimental studies on benchmark and real-world problems have shown that such simple modification can significantly improve the performances of MOEA/D with reciprocal Tchebycheff decomposition on MOPs with complicated Pareto fronts. (C) 2017 Elsevier B.V. All rights reserved.

关键词： evolutionary multi-objective optimization Decomposition method Utopian point Complicated Pareto fronts

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Population size matters: Rigorous runtime results for maximizing the hypervolume indicator

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THEORETICAL COMPUTER SCIENCE 2015年第PA期561卷 24-36页

作者： Anh Quang Nguyen Sutton, Andrew M. Neumann, Frank Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia Univ Jena Inst Informat D-07743 Jena Germany

evolutionary multi-objective optimization is one of the most successful areas in the field of evolutionary computation. Using the hypervolume indicator to guide the search of evolutionary multi-objective algorithms has become very popular in recent years. We contribute to the theoretical understanding of these algorithms by carrying out rigorous runtime analyses. We consider multi-objective variants of the problems OneMax and LeadingOnes called OneMinMax and LOTZ, respectively, and investigate hypervolume-based algorithms with population sizes that do not allow coverage of the entire Pareto front. Our results show that LOTZ is easier to optimize than OneMinMax for hypervolume-based evolutionary multi-objective algorithms, which is contrary to the results on their single-objective variants and the well-studied (1 + 1) EA. Furthermore, we study multi-objective genetic programming using the hypervolume indicator. We show that the classical ORDER problem is easy to optimize if the population size is large enough to cover the whole Pareto front and point out situations where a small population size leads to an exponential optimization time. (C) 2014 Published by Elsevier B.V.

关键词： evolutionary multi-objective optimization Hypervolume indicator Genetic programming Theory Runtime time analysis

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The effect of different stopping criteria on multi-objective optimization algorithms

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NEURAL COMPUTING & APPLICATIONS 2023年第2期35卷 1125-1155页

作者： Abu Doush, Iyad El-Abd, Mohammed Hammouri, Abdelaziz I. Bataineh, Mohammad Qasem Amer Univ Kuwait Coll Engn & Appl Sci Salmiya Kuwait Yarmouk Univ Dept Comp Sci Irbid Jordan Al Balqa Appl Univ Dept Comp Informat Syst Al Salt 19117 Jordan

evolutionary multi-objective optimization (EMO) refers to the domain in which an evolutionary algorithm is applied to tackle an optimization problem with multiple objective functions. The literature is rich with many approaches proposed to solve multi-objective problems including the NSGA-II, MOEA/D, and MOPSO algorithms. The proposed approaches include stand-alone as well as hybrid techniques. One critical aspect of any evolutionary algorithm (EA) is the stopping criterion. The selection of a specific stopping criterion can have a considerable effect on the performance and the final solution provided by the EA. A number of different stopping criteria, specifically designed for EMO, have been proposed in the literature. In this paper, the performance of six different EMO algorithms is tested and compared using four stopping criteria. The experiments are performed using the ZDT, DTLZ, CEC2009, Tanaka and Srivana test functions. Experimental results are analyzed to highlight the proper stopping criteria for different algorithms.

关键词： evolutionary multi-objective optimization Stopping criterion Hybrid framework Performance analysis Performance comparison

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EMDID: evolutionary multi-objective discretization for imbalanced datasets

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INFORMATION SCIENCES 2018年 432卷 442-461页

作者： Tahan, Marzieh Hajizadeh Asadi, Shahrokh Univ Tehran Fac Engn Farabi Campus Tehran Iran

In recent years, imbalanced dataset classification has received significant attention due to its application in real-world problems, resulting in emergence of a new class of algorithms. Classification algorithms that work with discretized data have been shown to yield better performance. Thus, discretization is often a critical technique in data preprocessing. In this paper, a novel evolutionary multi-objective Discretization algorithm for binary Imbalanced Datasets (EMDID) is presented. The proposed algorithm takes advantage of evolutionary multi-objective optimization to simultaneously optimize three objective functions: (1) Area under the ROC curve (AUC);(2) the number of cut points;and (3) low-frequency cut points. The first objective function uses AUC, instead of classification accuracy, to choose better cut points so as to identify the minority class. The second objective function reduces the number of cut points while in the third objective function, low-frequency cut points are selected so that information loss caused by (continuous to discrete) data discretization is minimized. To evaluate the proposed algorithm, 25 imbalanced benchmark datasets are totally used and the results are compared to those of popular algorithms in the literature such as Class-Attribute Interdependence Maximization (CALM) and evolutionary multi-objective Discretization (EMD). Our findings indicate that the proposed algorithm outperforms the other techniques in terms of the number of cut points, AUC, and non parametric statistical tests. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Discretization Imbalanced datasets evolutionary multi-objective optimization Area under ROC curve

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