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An archive-based multi-objective evolutionary algorithm with adaptive search space partitioning to deal with expensive optimization problems: Application to process eco-design

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COMPUTERS & CHEMICAL ENGINEERING 2016年 87卷 95-110页

作者： Ahmadi, Aras Tiruta-Barna, Ligia Capitanescu, Florin Benetto, Enrico Marvuglia, Antonino Univ Toulouse INSA UPS INPLISBP 135 Ave Rangueil F-31077 Toulouse France INRA UMR792 Lab Ingn Syst Biol & Proc F-31400 Toulouse France CNRS UMR5504 F-31400 Toulouse France LIST Environm Res & Innovat ERIN Dept 41 Rue Brill L-4422 Belvaux Luxembourg

In eco-design, the integration of environmental aspects into the earliest stage of design is considered with the aim of reducing adverse environmental impacts throughout a product's life cycle. An eco-design problem is therefore multi-objective, where several objectives (environmental, economic, and technological) are to be simultaneously optimized. The optimization of industrial processes usually requires solving expensive multi-objective optimization problems (MOPs). Aiming to solve efficiently MOPs, with a limited computational budget, this paper proposes a new framework called AMOEA-MAP. The framework relies on the structure of the NSGAII algorithm and possesses two novel operators: a memory-based adaptive partitioning strategy, which provides an adaptive reticulation of the search space for a quick identification of optimal zones with less computational effort;and a bi-population evolutionary algorithm, tailored for expensive optimization problems. To ascertain its generality, the framework is first tested on several tough benchmarks. Its performance is subsequently validated on a real-world eco-design problem. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Expensive simulation-based multi-objective optimization multi-objective evolutionary algorithms Convergence improvement heuristics Industrial eco-design

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A Pareto-based Ensemble with Feature and Instance Selection for Learning from multi-Class Imbalanced Datasets

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INTERNATIONAL JOURNAL OF NEURAL SYSTEMS 2017年第6期27卷 1750028-1750028页

作者： Fernandez, Alberto Jose Carmona, Cristobal Jose del Jesus, Maria Herrera, Francisco Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain Univ Burgos Dept Civil Engn Burgos 09006 Spain De Montfort Univ Leicester Sch Pharm Leicester LE1 9BH Leics England Univ Jaen Dept Comp Sci Jaen 23071 Spain King Abdulaziz Univ Fac Comp & Informat Technol North Jeddah Jeddah 80200 Saudi Arabia

Imbalanced classification is related to those problems that have an uneven distribution among classes. In addition to the former, when instances are located into the overlapped areas, the correct modeling of the problem becomes harder. Current solutions for both issues are often focused on the binary case study, as multi-class datasets require an additional effort to be addressed. In this research, we overcome these problems by carrying out a combination between feature and instance selections. Feature selection will allow simplifying the overlapping areas easing the generation of rules to distinguish among the classes. Selection of instances from all classes will address the imbalance itself by finding the most appropriate class distribution for the learning task, as well as possibly removing noise and difficult borderline examples. For the sake of obtaining an optimal joint set of features and instances, we embedded the searching for both parameters in a multi-objective evolutionary Algorithm, using the C4.5 decision tree as baseline classifier in this wrapper approach. The multi-objective scheme allows taking a double advantage: the search space becomes broader, and we may provide a set of different solutions in order to build an ensemble of classifiers. This proposal has been contrasted versus several state-of-the-art solutions on imbalanced classification showing excellent results in both binary and multi-class problems.

关键词： Imbalanced classification multi-class overlapping feature selection instance selection multi-objective evolutionary algorithms ensembles

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Efficiency determination of induction motors using multi-objective evolutionary algorithms

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ADVANCES IN ENGINEERING SOFTWARE 2010年第2期41卷 255-261页

作者： Cunkas, Mehmet Sag, Tahir Selcuk Univ Dept Elect & Comp Educ Fac Tech Educ TR-42003 Konya Turkey

This paper introduces a method based on multi-objective evolutionary algorithms for the determination of in-service induction motor efficiency. In general, the efficiency is determined by accumulating multiple objectives into one objective by a linear combination and optimizing the resulting single-objective problem. The approach has some drawbacks such that exact information about solution alternatives will not be readily visible. In this paper the multi-objective evolutionary optimization algorithms, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Strength Pareto evolutionary Algorithm-2 (SPEA2), are successfully applied to the efficiency determination problem in induction motor. The performances of algorithms are compared on the basis of the obtained results. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： multi-objective evolutionary algorithms Induction motor In-situ efficiency determination

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Finite Satisfiability of Interval Temporal Logic Formulas with multi-objective Metaheuristics

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JOURNAL OF multiPLE-VALUED LOGIC AND SOFT COMPUTING 2017年第2-3期28卷 217-249页

作者： Bresolin, Davide Jimenez, Fernando Sanchez, Gracia Sciavicco, Guido Univ Bologna Dept Comp Sci & Engn Bologna Italy Univ Murcia Dept Informat Engn & Commun Murcia Spain

Interval temporal logics provide a natural framework for reasoning about interval structures over linearly ordered domains. Despite being relevant for a broad spectrum of application domains, ranging from temporal databases to artificial intelligence and verification of reactive systems, interval temporal logics still miss tools capable of efficiently supporting them. We approach the finite satisfiability problem for one of the simplest meaningful interval temporal logic, namely A (also known as Right Propositional Neighborhood Logic) and we propose three different multi-objective evolutionary algorithms to solve it by means of a metaheuristic for multi-objective optimization. The resulting semidecision procedure, although incomplete, turns out to be easier to implement and more scalable with respect to classical complete algorithms.

关键词： Interval logics satisfiability metaheuristics multi-objective evolutionary algorithms

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Designing the Optical Network of Haiti Using a multiobjective evolutionary Approach

Designing the Optical Network of Haiti Using a Multiobjectiv...

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3rd IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Dupleix, Vital Araujo, Danilo R. B. Bastos-Filho, Carmelo J. A. Univ Pernambuco BR-50720001 Recife PE Brazil Univ Fed Rural Pernambuco BR-52171900 Recife PE Brazil

ISBN: (纸本)9781509051052

The Haitian Republic currently does not have a suitable telecommunication network to support the novel information technologies in all the principal cities of the country. Therefore, it is a real concern to design an appropriate telecommunication infrastructure to cover all the national territory aiming to support the offer of these services in the country. The optical networks of Digicel and Natcom were the first two highspeed networks designed in the metropolitan area of Haiti, but these systems are not sufficient to satisfy the population needs regarding access to a high-quality broadband Internet services. This paper aims to design a suitable optical backbone network for Haiti based on the recent advances in multi-objective evolutionary algorithms. According to our results, it is possible to create suitable physical topologies successfully by using socio-economics data from the cities and the geographical locations of the terminal nodes.

关键词： Optical networks physical topology design multi-objective evolutionary algorithms graph theory geographic graph models

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Network power saving based on Pareto optimal control with evolutionary approach

Network power saving based on Pareto optimal control with ev...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Akishita, Yosuke Ohsita, Yuichi Murata, Masayuki Osaka Univ Grad Sch Informat Sci & Technol Suita Osaka Japan

ISBN: (纸本)9781509045884

The power consumption of networks has been increasing as the service over the Internet becomes popular, and has become a serious problem. Many methods to reduce the power consumption by shutting down unnecessary network devices following the environmental changes have been proposed. These methods consider only simple objectives such as the number of powered-on nodes and the maximum link utilization. However, multiple complex objectives such as delay and reliability should be also considered in the actual network. In this paper, we propose a network power saving method that handles multiple complex objectives, following the environmental changes. In this method, we store the candidate network configurations, and evolve them, following the environmental changes. Then, we select the network configuration from the candidate network configurations. We combine two approaches to evolve the network configurations. The first approach is based on Pareto optimal, and evolves the network configurations so as to be close to the Pareto optimal solutions, considering multiple objectives. Another approach is based on the diversity of the network configurations. By storing the diverse network configurations, we can handle the significant environmental changes. We evaluate our method by simulation, and demonstrate that our method reduces the power consumption without violating the constraints, following the traffic changes. In addition, we also demonstrate that our method can keep the connectivity in case of failures, and recover the performance and the small power consumption soon after the failure occurs.

关键词： Network power saving evolutionary approach Pareto optimal solutions Pareto front multi-objective evolutionary algorithms

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Using multiobjective evolutionary algorithms to Understand Parkinson's Disease

Using Multiobjective Evolutionary Algorithms to Understand P...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Vallejo, Marta Cosgrove, Jeremy Alty, Jane E. Smith, Stephen L. Corne, David W. Lones, Michael A. Heriot Watt Univ Dept Comp Sci Edinburgh EH14 4AS Midlothian Scotland Leeds Gen Infirm Dept Neurol Leeds W Yorkshire England Univ York Dept Elect York YO10 5DD N Yorkshire England

ISBN: (纸本)9781450343237

The incidence of neurodegenerative diseases such as Parkinson's is increasing rapidly around the world, yet the symptoms and pathology of these diseases remain incompletely understood. As a consequence, it is challenging for clinicians to provide patients with accurate diagnoses or prognoses. In this work, we use multi-objective evolutionary algorithms to explore recordings of patients drawing neurological assessment figures, with the aim of identifying patterns of cognitive and motor signals that discriminate different disease states. As a proof of principle, we demonstrate how this approach can be used to explore the trade-off between predicting clinical measures of motor and cognitive deficit.

关键词： multi-objective evolutionary algorithms Predictive modelling Parkinson's disease Polynomial regression

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Methodologies for Solving Complex multi-objective Combinatorial Problems in Engineering: An evolutionary Approach

Methodologies for Solving Complex Multi-Objective Combinator...

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IEEE International Conference on Automatica (ICA-ACCA)

作者： Donoso, Yezid Univ Los Andes Syst & Comp Engn Dept Master Program Informat Secur Bogota Colombia

ISBN: (纸本)9781509011476

In real problems in Engineering, solving a problem is not enough;the solution of the problem must be the best solution possible. In other words, it is necessary to find the optimal solution. The solution is the best possible solution because in the real world this problem may have certain constraints by which the solutions found may be feasible, that is, they can be implemented in practice and, unfeasible or that they cannot be implemented. Some of these problems in engineering can be MOP (multi-objective Optimization Problem). A general MOP includes a set of n parameters ( decision variables), a set of k objective functions and a set of m restrictions. The objective and restriction functions are functions of the decision variables where is possible to obtain a set of optimal values. Then the MOP can be expressed as: Optimize y = f(x) = (fl( x), f2(x),..., fk(x)) Subject to e(x) = (el(x), e2(x),..., em(x)) 0 Where x = (x1, x2,..., xn) X y = (y1, y2,..., yk) Y The method evolutionary algorithm (EA) refers to searching and optimization techniques based on the evolution model proposed by Charles Darwin. Genetic algorithms are used in several areas especially for searching and optimizations. In the real case the algorithm is implemented by choosing a coding for the possible solutions to the problem. The coding is done through chains of bits, numbers or characters that represent the chromosomes. The crossing and mutation operations are applied in a very simple way through functions of vector value manipulation. The EAs are interesting given the fact that at first glance they seem especially apt to deal with the difficulties presented by MOPs. The reason for this is that they can return an entire set of solutions after a simple run and they do not have any other of the limitations of traditional techniques. In addition, some researchers have suggested that the EAs would behave better than other blind searching techniques.

关键词： Complex Problems evolutionary algorithms multi-objective Optimization Problems multi-objective evolutionary algorithms

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Optimisation of PV-wind-diesel-battery stand-alone systems to minimise cost and maximise human development index and job creation

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RENEWABLE ENERGY 2016年 94卷 280-293页

作者： Dufo-Lopez, Rodolfo Cristobal-Monreal, Ivan R. Yusta, Jose M. Univ Zaragoza Dept Elect Engn Calle Maria Luna 3 Zaragoza 50018 Spain Acad Gen Mil Ctr Univ Def Ctra Huesca S-N Zaragoza 50090 Spain

In this paper we show a multi-objective evolutionary algorithm (MOEA) for the optimisation of stand-alone (off-grid) hybrid systems (photovoltaic-wind-diesel-battery) to minimise total net present cost (NPC) and maximise human development index (HDI) and job creation (JC). Optimisation of this kind of system is usually performed considering only the minimisation of cost (NPC or the levelised cost of energy), as well as the emissions and the unmet load in some cases. In this paper, for the first time, we consider the maximisation of HDI and JC as part of optimisation. HDI depends on the consumption of electricity, so the extra energy that can supply the hybrid system can improve the HDI index. JC is different for each technology, obtaining different values for each combination of components in the system. The three objectives are often opposed, so a Pareto-optimisation MOEA is a good option to obtain a set of possible solutions in which no solution is better than another one for all three objectives (optimal Pareto set). We provide an example in the optimisation of a hybrid system to supply electricity to a small community in the Sahrawi refugee camps of Tindouf. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Renewable stand-alone systems Net present cost Human development index Job creation multi-objective evolutionary algorithms

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Dynamic Software Project Scheduling through a Proactive-Rescheduling Method

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2016年第7期42卷 658-686页

作者： Shen, Xiaoning Minku, Leandro L. Bahsoon, Rami Yao, Xin Nanjing Univ Informat Sci & Technol Sch Informat & Control B DAT & CICAEET 219 Ning Liu Rd Nanjing 210044 Jiangsu Peoples R China Univ Leicester Dept Comp Sci Univ Rd Leicester LE1 7RH Leics England Univ Birmingham CERCIA Birmingham B15 2TT W Midlands England

Software project scheduling in dynamic and uncertain environments is of significant importance to real-world software development. Yet most studies schedule software projects by considering static and deterministic scenarios only, which may cause performance deterioration or even infeasibility when facing disruptions. In order to capture more dynamic features of software project scheduling than the previous work, this paper formulates the project scheduling problem by considering uncertainties and dynamic events that often occur during software project development, and constructs a mathematical model for the resulting multi-objective dynamic project scheduling problem (MODPSP), where the four objectives of project cost, duration, robustness and stability are considered simultaneously under a variety of practical constraints. In order to solve MODPSP appropriately, a multi-objective evolutionary algorithm based proactive-rescheduling method is proposed, which generates a robust schedule predictively and adapts the previous schedule in response to critical dynamic events during the project execution. Extensive experimental results on 21 problem instances, including three instances derived from real-world software projects, show that our novel method is very effective. By introducing the robustness and stability objectives, and incorporating the dynamic optimization strategies specifically designed for MODPSP, our proactive-rescheduling method achieves a very good overall performance in a dynamic environment.

关键词： Schedule and organizational issues dynamic software project scheduling search-based software engineering multi-objective evolutionary algorithms mathematical modeling

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