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4th International Workshop on Hybrid Artificial Intelligence Systems

作者： Antonelli, Michela Ducange, Pietro Lazzerini, Beatrice Marcelloni, Francesco Univ Pisa Dipartimento Ingn Informaz I-56122 Pisa Italy

ISBN: (纸本)9783642023187

In the last years, several papers have proposed to adopt multi-objective evolutionary algorithms (MOEAs) to generate Mamdani fuzzy rule-based systems with different trade-offs between interpretability and accuracy. Since interpretability is difficult to quantify because of its qualitative nature, several measures have been introduced, but there is no general agreement on any of them. In this paper, we propose all MOEA to learn concurrently rule base and membership function parameters by optimizing accuracy and interpretability, which is measured in terms of number of conditions in the antecedents of rules and partition integrity. Partition integrity is evaluated by using a purposely-defined index based on the piecewise linear transformation exploited to learn membership function parameters. Results on a real-world regression problem are shown and discussed.

关键词： Accuracy-Interpretability Trade-off Interpretability Index multi-objective evolutionary algorithms Piecewise Linear Transformation

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evolutionary multiobjective multicast Virtual Network Function Placement in NFV-SDN Networks 50

Evolutionary Multiobjective Multicast Virtual Network Functi...

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50th Latin American Computing Conference (CLEI)

作者： Canete, Carlos Medina, Cristhian More, Luis G. Colbes, Jose Pinto-Roa, Diego P. Univ Nacl Asuncion Fac Politecn Asuncion Paraguay

ISBN: (纸本)9798331540982;9798331540975

Software-defined networking (SDN) and network functions virtualization (NFV) are promising technologies for demand services that require building flexible multicast transmission mechanisms with requirements for data processing functions at the network nodes. The multicast routing problem in NFV-SDN networks seeks to compute multicast-routing trees and place virtual network functions (VNFs), satisfying the traffic demand with optimal resource use and fair data transmission. Since the problem is computationally complex with conflicting objective functions, this paper approaches multicast routing and VNF placement as a multiobjective optimization problem (MOP), minimizing the total resource cost and the maximum transmission delay variance. In this context, this study develops solutions based on multiobjective evolutionary algorithms (MOEAs). Simulations performed on test instances show that the proposals are promissory by computing efficient and non-dominated solutions when compared to a state-of-the-art mono-objective approach.

关键词： multicast Routing Software-Defined Networks Network Functions Virtualization multi-objective evolutionary algorithms

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Scheduling the English Football League with a multi-objective evolutionary Algorithm

Scheduling the English Football League with a Multi-objectiv...

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13th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： While, Lyndon Kendall, Graham Univ Western Australia Comp Sci & Software Engn Nedlands WA 6009 Australia Univ Nottingham Sch Comp Sci ASAP Res Grp Semenyih Selangor Malaysia Univ Nottingham Nottingham NG7 2RD England

ISBN: (纸本)9783319107622;9783319107615

We describe a multi-objective evolutionary algorithm that derives schedules for the English Football League over the busy New Year period according to seven objectives. The two principal objectives are to minimise travel distances for teams and supporters, and to minimise so-called "pair clashes" where teams which are geographically close play at home simultaneously, which can cause problems for police, and other logistical issues. The other five objectives implement various problem constraints. The schedules derived are often superior both to those used in the relevant years, and to those previously published in the literature, especially for the harder problem instances. In addition, the system returns a set of schedules offering different trade-offs between the main objectives, any of which might be of interest to the authorities.

关键词： Sports scheduling 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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A hybrid multi-objective immune algorithm for a flow shop scheduling problem with bi-objectives: Weighted mean completion time and weighted mean tardiness

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INFORMATION SCIENCES 2007年第22期177卷 5072-5090页

作者： Tavakkoli-Moghaddam, Reza Rahimi-Vahed, Alireza Mirzaei, Ali Hossein Univ Tehran Fac Engn Dept Ind Engn Tehran Iran Tarbiat Modares Univ Dept Ind Engn Tehran Iran

This paper investigates a novel multi-objective model for a no-wait flow shop scheduling problem that minimizes both the weighted mean completion time ((C) over bar (w)) and weighted mean tardiness ((T) over bar (w)). Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. This paper presents a new hybrid multi-objective algorithm based on the features of a biological immune system (IS) and bacterial optimization (BO) to find Pareto optimal solutions for the given problem. To validate the performance of the proposed hybrid multi-objective immune algorithm (HMOIA) in terms of solution quality and diversity level, various test problems are examined. Further, the efficiency of the proposed algorithm, based on various metrics, is compared against five prominent multi-objective evolutionary algorithms: PS-NC GA, NSGA-II, SPEA-II, MOIA, and MISA. Our computational results suggest that our proposed HMOIA outperforms the five foregoing algorithms, especially for large-sized problems. (C) 2007 Elsevier Inc. All rights reserved.

关键词： multi-objective no-wait flow shop scheduling completion time tardiness multi-objective evolutionary algorithms immune system bacterial optimization

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multi-objective grey wolf optimizer based on decomposition

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 120卷 357-371页

作者： Zapotecas-Martinez, Saul Garcia-Najera, Abel Lopez-Jaimes, Antonio Univ Autonoma Metropolitana Dept Matemat Aplicadas & Sistemas Unidad Cuajimalpa Ave Vasco de Quiroga 4871 Ciudad De Mexico 05348 Mexico

An optimization technique aims to find the best solution to an optimization problem. If the problem considers only one objective function, the best solution will provide the optimal value for such objective. However, if the problem considers two or more objectives, the selection of solutions will not be that straightforward since such objective functions are usually in conflict. For this kind of optimization problems, the use of analytical or exact methods becomes impractical. Thus, heuristic or metaheuristic approaches have to be applied for finding the optimal solutions or, at least, approximate solutions to the optimum. As a consequence, a wide variety of metaheuristics inspired by nature has been proposed for solving optimization problems. Among them, the Grey Wolf Optimizer is a metaheuristic of recent creation that in the last few years has attracted the attention of many researchers. Furthermore, a multi-objective extension of this technique was recently introduced proving its high performance comparable to other multi-objective optimization methods. In this paper, a multi-objective grey wolf optimizer based on the decomposition is introduced. Our proposed algorithm approximates Pareto solutions cooperatively by defining a neighborhood relation among the scalarizing subproblems in which the multi-objective problem is decomposed. The performance of our proposed method is compared against those achieved by six state-of-the-art bio-inspired techniques showing its high performance in both, well-known benchmark problems and two real-life engineering problems. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： multi-objective evolutionary algorithms multi-objective grey wolf optimizer Decomposition-based MOEAs

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multi-objective trajectory optimization of Space Manoeuvre Vehicle using adaptive differential evolution and modified game theory

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ACTA ASTRONAUTICA 2017年第Jul.期136卷 273-280页

作者： Chai, Runqi Savvaris, Al Tsourdos, Antonios Chai, Senchun Cranfield Univ Sch Aerosp Transport & Mfg Cranfield MK43 0AL Beds England Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

Highly constrained trajectory optimization for Space Manoeuvre Vehicles (SMV) is a challenging problem. In practice, this problem becomes more difficult when multiple mission requirements are taken into account. Because of the nonlinearity in the dynamic model and even the objectives, it is usually hard for designers to generate a compromised trajectory without violating strict path and box constraints. In this paper, a new multi objective SMV optimal control model is formulated and parameterized using combined shooting-collocation technique. A modified game theory approach, coupled with an adaptive differential evolution algorithm, is designed in order to generate the pareto front of the multi-objective trajectory optimization problem. In addition, to improve the quality of obtained solutions, a control logic is embedded in the framework of the proposed approach. Several existing multi-objective evolutionary algorithms are studied and compared with the proposed method. Simulation results indicate that without driving the solution out of the feasible region, the proposed method can perform better in terms of convergence ability and convergence speed than its counterparts. Moreover, the quality of the pareto set generated using the proposed method is higher than other multi-objective evolutionary algorithms, which means the newly proposed algorithm is more attractive for solving multi-criteria SMV trajectory planning problem.

关键词： Trajectory optimization Space Manoeuvre Vehicles Modified game theory Adaptive differential evolution multi-objective evolutionary algorithms

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VMODEX: A novel visualization tool for rapid analysis of heuristic-based multi-objective design space exploration of heterogeneous MPSoC architectures

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SIMULATION MODELLING PRACTICE AND THEORY 2012年 22卷 166-196页

作者： Taghavi, Toktam Pimentel, Andy D. Sabeghi, Mojtaba Univ Amsterdam Inst Informat Comp Syst Architecture Grp Amsterdam Netherlands Delft Univ Technol Comp Engn Lab Delft Netherlands

System-level computer architecture simulations create large volumes of simulation data to explore alternative architectural solutions. Interpreting and drawing conclusions from this amount of simulation results can be extremely cumbersome. In other domains that also struggle with interpreting large volumes of data, such as scientific computing, data visualization is an invaluable tool. Such visualization is often domain specific and has not become widely studied and utilized for evaluating the results of computer architecture simulations. In this paper, we introduce our novel interactive visualization tool, called VMODEX, which is developed to support system-level design space exploration of MPSoC architectures. In our tool, the design space is modeled as a tree in which both the design parameters and criteria are shown in a single view. VMODEX is able to handle large design spaces and allows designers to look at the data from different perspectives and at multiple levels of abstraction. Due to the exponential design space in real problems and multiple criteria to be considered, heuristic searching algorithms are often used to trim down a large design space into a finite set of points and provide the designer a set of tradable solutions with respect to the design criteria. In VMODEX, besides the techniques provided for visualizing the DSE results, additional capabilities are developed to understand the dynamic search behavior of heuristic searching algorithms. (C) 2011 Elsevier B.V. All rights reserved.

关键词： multi-objective design space exploration Visualization multi-objective evolutionary algorithms MPSoC architectures

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Modelling energy storage systems using Fourier analysis: An application for smart grids optimal management

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APPLIED SOFT COMPUTING 2014年第PartC期14卷 469-481页

作者： Di Silvestre, Maria Luisa Sanseverino, Eleonora Riva Univ Palermo DEIM I-90128 Palermo Italy

In this paper, a new and efficient model for variables representation, named F-coding, in optimal power dispatch problems for smart electrical distribution grids is proposed. In particular, an application devoted to optimal energy dispatch of Distributed Energy Resources including ideal storage devices is here considered. Electrical energy storage systems, such as any other component that must meet an integral capacity constraint in optimal dispatch problems, have to show the same energy level at the beginning and at the end of the considered timeframe for operation. The use of zero-integral functions, such as sinusoidal functions, for the synthesis of the charge and discharge course of batteries is thus consequential. The issue is common to many other engineering problems, such as any dispatch problem where resources must be allocated within a given amount in a considered timeframe. Many authors have proposed different methods to deal with such integral constraints in the literature on smart grids management, but all of them do not seem very efficient. The paper is organized as follows. First, the state of the art on the optimal management problem is outlined with special attention to treatment of integral constraints, then the proposed new model for variables representation is described. Finally, the multiobjective optimization method and its application to the optimal dispatch problem considering different variables representations are considered. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Integral constraints Electrical energy storage systems Fourier analysis Optimal dispatch of resources multi-objective evolutionary algorithms Smart grids

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Optimization of ballast plan in launch jacket load-out

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STRUCTURAL AND multiDISCIPLINARY OPTIMIZATION 2009年第3期38卷 267-288页

作者： Kurniawan, Adi Ma, Guowei Nanyang Technol Univ Sch Civil & Environm Engn Maritime Res Ctr Singapore 639798 Singapore

The calculation of ballast plan in load-out operations is generally performed assuming a rigid barge. This assumption may not be reliable since in reality the barge is flexible. Having incorrect ballast plan may lead to overstressing of the loaded-out structure. We present a method of finding a more accurate ballast plan, taking into account the flexibility of the barge. This method makes use of a multi-objective evolutionary algorithm to find the optimum ballast arrangement at every load-out stage. We model the load-out configuration as a beam on elastic foundation, loaded with distributed trapezoidal loading representing the load from the structure. Minimizing deflection and curvature of the beam, as well as maximizing the ballast transfer efficiency between the load-out stages are chosen as the objectives of the algorithm. It is shown that the proposed method is better than the conventional rigid barge method in terms of minimizing the deflection and curvature as well as maximizing the ballast transfer efficiency.

关键词： Load-out Ballast Barge Beam on elastic foundations multi-objective evolutionary algorithms

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