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A biologically constrained model of the whole basal ganglia addressing the paradoxes of connections and selection

JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2014年第3期36卷 445-468页

作者： Lienard, Jean Girard, Benoit Univ Paris 06 Inst Syst Intelligents & Robot F-75005 Paris France CNRS UMR7222 F-75005 Paris France

The basal ganglia nuclei form a complex network of nuclei often assumed to perform selection, yet their individual roles and how they influence each other is still largely unclear. In particular, the ties between the external and internal parts of the globus pallidus are paradoxical, as anatomical data suggest a potent inhibitory projection between them while electrophysiological recordings indicate that they have similar activities. Here we introduce a theoretical study that reconciles both views on the intra-pallidal projection, by providing a plausible characterization of the relationship between the external and internal globus pallidus. Specifically, we developed a mean-field model of the whole basal ganglia, whose parameterization is optimized to respect best a collection of numerous anatomical and electrophysiological data. We first obtained models respecting all our constraints, hence anatomical and electrophysiological data on the intrapallidal projection are globally consistent. This model furthermore predicts that both aforementioned views about the intra-pallidal projection may be reconciled when this projection is weakly inhibitory, thus making it possible to support similar neural activity in both nuclei and for the entire basal ganglia to select between actions. Second, we predicts that afferent projections are substantially unbalanced towards the external segment, as it receives the strongest excitation from STN and the weakest inhibition from the striatum. Finally, our study strongly suggests that the intrapallidal connection pattern is not focused but diffuse, as this latter pattern is more efficient for the overall selection performed in the basal ganglia.

关键词： Basal ganglia Globus pallidus multiobjective evolutionary algorithms Mean-field models Single-axon tracing studies Antagonist deactivations

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Participatory Framework for Assessment and Improvement of Tools (ParFAIT): Increasing the impact and relevance of water management decision support research

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ENVIRONMENTAL MODELLING & SOFTWARE 2017年 95卷 432-446页

作者： Smith, Rebecca Kasprzyk, Joseph Dilling, Lisa Univ Colorado Dept Civil Environm & Architectural Engn 428 UCB Boulder CO 80309 USA Univ Colorado Dept Environm Studies 397 UCB Boulder CO 80309 USA Univ Colorado Western Water Assessment CIRES 216 UCB Boulder CO 80309 USA

This paper proposes the Participatory Framework for Assessment and Improvement of Tools (ParFAIT) as a way to address low uptake of Water Resources Systems Optimization (WRSO) tools. ParFAIT is a transdisciplinary process conducted in five stages, two of which are participatory modeling (PM) exercises. Herein we describe the framework, introduce our candidate tool- multiobjective evolutionary Algorithm (MOEA)-assisted optimization, and present the results of our first PM workshop. MOEA-assisted optimization has been put forth as a planning and decision making aid for utilities facing a large number of decisions and highly uncertain futures. The PM workshop, designed to solicit input on a tool testbed, was held in February 2015 with representatives from six Front Range, Colorado, water utilities. Our results include an expanded characterization of the decision making landscape, feedback on water utility decisions and performance goals commonly employed in WRSO studies, and new questions that warrant future investigation by researchers. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Participatory modeling Water management Optimization Water resources systems analysis multiobjective evolutionary algorithms Transdisciplinary

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Multiple objective optimisation for antenna diversity on airborne platforms

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2023年第1期17卷 1-10页

作者： Emmett, Christian B. Flint, James A. Seager, Robert D. Loughborough Univ Wolfson Sch Mech Elect & Mfg Engn Loughborough Leics England

Vehicles such as automobiles, ships, satellites, and aircraft have a limited amount of physical space to install antennas for communications and navigation systems. This is exacerbated by the use of modern materials, like carbon fibre, and that large areas of the vehicles structure cannot be used to mount antenna, due to aerodynamic or other requirements. Therefore, it is necessary to be able to quickly and accurately find the optimum locations to mount a number of antenna systems, in a restricted space, whilst considering a number of different and sometimes contradictory antenna performance parameters. Thus, defining the optimum antenna locations is a multi-objective problem (MOP) and lends itself to the use of multi-objective evolutionary algorithms (MOEA). This paper presents a MOEA methodology that can be used to accurately, quickly, and robustly define the antenna locations. It will also define an appropriate MOEA and the fitness functions for predicting the radio frequency (RF) interoperability/mutual coupling between antenna systems and antenna RF radiation pattern installed performance.

关键词： genetic algorithms Optimisation techniques airborne platforms Pareto optimisation modern materials optimum antenna locations automobiles multiobjective problem evolutionary computation Antenna arrays aerodynamics optimum locations carbon fibre aerodynamic requirements restricted space Single antennas multiobjective evolutionary algorithms multiple objective optimisation antenna systems contradictory antenna performance parameters navigation systems physical space vehicles structure antenna RF radiation pattern installed performance antenna radiation patterns optimisation antenna diversity other requirements

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An evolutionary approach with surrogate models and network science concepts to design optical networks

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2015年 43卷 67-80页

作者： de Araujo, Danilo R. B. Bastos-Filho, Carmelo J. A. Martins-Filho, Joaquim F. Univ Fed Pernambuco Dept Elect & Syst BR-50740550 Recife PE Brazil Univ Fed Rural Pernambuco Dept Stat & Informat BR-52171900 Recife PE Brazil Univ Pernambuco BR-50720001 Recife PE Brazil

Physical topology design of optical networks is frequently accomplished by using evolutionary approaches. However, fitness evaluation for this type of problems is time consuming and the overall optimization process presents a huge execution time. In this paper we propose a new method that uses a multi-objective evolutionary approach to handle the design of all-optical networks. We focused on the simultaneous optimization of the network topology and the device specifications in order to minimize both the capital expenditure of the network and the network performance. Our method uses surrogate models to accelerate the fitness evaluation and a novel network generative model based on preferential attachment to generate the seeds for the evolutionary process. Our approach can provide high quality solutions with a very small execution time when compared to the previous approaches. In order to assess our proposal we performed a set of simulations aiming to analyze the convergence ability and the diversity of the generated solutions for scenarios considering uniform and non-uniform traffic matrices. From our results, we obtained an evolutionary approach that presents better solutions than previous proposals for all analyzed scenarios. Our proposal presents an execution time that is up to 84% and 88% lower than the execution time needed by the previous approaches for uniform and non-uniform traffic, respectively. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： multiobjective evolutionary algorithms Artificial neural networks Optical networks Physical topology design Network science Surrogate models

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MRQAR: A generic MapReduce framework to discover quantitative association rules in big data problems

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KNOWLEDGE-BASED SYSTEMS 2018年 153卷 176-192页

作者： Martin, D. Martinez-Ballesteros, M. Garcia-Gil, D. Alcala-Fdez, J. Herrera, F. Riquelme-Santos, J. C. Technol Univ Havana JA Echeverria Dept Artificial Intelligence & Infrastruct Inform Havana Cuba Univ Seville Dept Comp Sci Seville Spain Univ Granada Comp Sci & Artificial Intelligence Granada Spain King Abdulaziz Univ Fac Comp & Informat Technol Jeddah Saudi Arabia

Many algorithms have emerged to address the discovery of quantitative association rules from datasets in the last years. However, this task is becoming a challenge because the processing power of most existing techniques is not enough to handle the large amount of data generated nowadays. These vast amounts of data are known as Big Data. A number of previous studies have been focused on mining boolean or nominal association rules from Big Data problems, nevertheless, the data in real-world applications usually consist of quantitative values and designing data mining algorithms able to extract quantitative association rules presents a challenge to workers in this research field. In spite of the fact that we can find classical methods to discover boolean or nominal association rules in the most well-known repositories of Big Data algorithms, such repositories do not provide methods to discover quantitative association rules. Indeed, no methodologies have been proposed in the literature without prior discretization in Big Data. Hence, this work proposes MRQAR, a new generic parallel framework to discover quantitative association rules in large amounts of data, designed following the MapReduce paradigm using Apache Spark. MRQAR performs an incremental learning able to run any sequential quantitative association rule algorithm in Big Data problems without needing to redesign such algorithms. As a case study, we have integrated the multiobjective evolutionary algorithm MOPNAR into MRQAR to validate the generic MapReduce framework proposed in this work. The results obtained in the experimental study performed on five Big Data problems prove the capability of MRQAR to obtain reduced set of high quality rules in reasonable time.

关键词： Quantitative association rules multiobjective evolutionary algorithms Big Data MapReduce Spark

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Surrogate-Assisted Reliability Optimisation of an Aircraft Wing with Static and Dynamic Aeroelastic Constraints

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INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES 2020年第3期21卷 723-732页

作者： Wansaseub, K. Sleesongsom, S. Panagant, N. Pholdee, N. Bureerat, S. KhonKaen Univ Fac Engn Sustainable & Infrastruct Dev Ctr Dept Mech Engn Khon Kaen 40002 Thailand King Mongkuts Inst Technol Ladkrabang Int Acad Aviat Ind Dept Aeronaut Engn Bangkok 10520 Thailand

This paper presents a numerical strategy for reliability-based design optimisation of an aircraft wing structure using a surrogate-assisted approach. The design problem is set to minimise aircraft wing mass subject to structural and aeroelastic constraints, while design variables are structural dimensions. The problem has uncertainties in the material properties. The Kriging model is used for estimating the values of design functions. Two strategies of sampling technique are used, i.e., optimum Latin hypercube sampling (OLHS) with and without infill sampling. Uncertainty quantification is achieved by means of optimum normal distribution Latin hypercube sampling. The original design problem is converted to be a multiobjective optimisation problem. Optimum results show that OLHS with infill sampling gives a more accurate surrogate model;however, OLHS without infill sampling results in the better design solutions based on actual function evaluations.

关键词： Reliability-based design optimisation Aeroelasticity Aircraft wing multiobjective evolutionary algorithms

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Multi-objective evolutionary design of robust controllers on the grid

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年 27卷 17-27页

作者： Shenfield, Alex Fleming, Peter J. Manchester Metropolitan Univ Sch Engn Manchester M15 6BH Lancs England Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

Coupling conventional controller design methods, model based controller synthesis and simulation, and multi-objective evolutionary optimisation methods frequently results in an extremely computationally expensive design process. However, the emerging paradigm of grid computing provides a powerful platform for the solution of such problems by providing transparent access to large-scale distributed high-performance compute resources. As well as substantially speeding up the time taken to find a single controller design satisfying a set of performance requirements this grid-enabled design process allows a designer to effectively explore the solution space of potential candidate solutions. An example of this is in the multi-objective evolutionary design of robust controllers, where each candidate controller design has to be synthesised and the resulting performance of the compensated system evaluated by computer simulation. This paper introduces a grid-enabled framework for the multi-objective optimisation of computationally expensive problems which will then be demonstrated using and example of the multi-objective evolutionary design of a robust lateral stability controller for a real-world aircraft using H-infinity loop shaping. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Aerospace stability control H-infinity controller design multiobjective evolutionary algorithms High performance computing

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Data classification through an evolutionary approach based on multiple criteria

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KNOWLEDGE AND INFORMATION SYSTEMS 2012年第1期33卷 35-56页

作者： Garcia-Piquer, A. Fornells, A. Orriols-Puig, A. Corral, G. Golobardes, E. La Salle Univ Ramon Llull Grp Recerca Sistemes Intelligents Barcelona 08022 Spain

Real-world problems usually present a huge volume of imprecise data. These types of problems may challenge case-based reasoning systems because the knowledge extracted from data is used to identify analogies and solve new problems. Many authors have focused on organizing case memory in patterns to minimize the computational burden and deal with uncertainty. The organization is usually determined by a single criterion, but in some problems, a single criterion can be insufficient to find accurate clusters. This work describes an approach to organize the case memory in patterns based on multiple criteria. This new approach uses the searching capabilities of multiobjective evolutionary algorithms to build a Pareto set of solutions, where each one is a possible organization based on the relevance of objectives. The system shows promising capabilities when it is compared with a successful system based on self-organizing maps. Due to the data set geometry influences, the clustering building process results are analyzed taking into account it. For this reason, some complexity measures are used to categorize data sets according to their topology.

关键词： Case-based reasoning Case memory organization Clustered case memory multiobjective evolutionary algorithms Soft case-based reasoning

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Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems

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NEURAL COMPUTING & APPLICATIONS 2022年第8期34卷 6307-6329页

作者： Rahman, Chnoor M. Rashid, Tarik A. Ahmed, Aram Mahmood Mirjalili, Seyedali Charmo Univ Coll Med & Appl Sci Appl Comp Dept Sulaimany Iraq Univ Kurdistan Hewler Comp Sci & Engn Dept Erbil Iraq Sulaimani Polytech Univ Tech Coll Informat Dept Informat Technol Sulaimany Iraq Univ Human Dev Coll Sci & Technol Dept Informat Technol Sulaimany Iraq Torrens Univ Ctr Artificial Intelligence Res & Optimizat Adelaide SA Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea

In this work, a new multi-objective optimization algorithm called multi-objective learner performance-based behavior algorithm is proposed. The proposed algorithm is based on the process of moving graduated students from high school to college. The proposed technique produces a set of non-dominated solutions. To test the ability and efficacy of the proposed multi-objective algorithm, it is applied to a group of benchmarks and five real-world engineering optimization problems. Several widely used metrics are employed in the quantitative statistical comparisons. The proposed algorithm is compared with three multi-objective algorithms: Multi-Objective Water Cycle Algorithm (MOWCA), Non-dominated Sorting Genetic Algorithm (NSGA-II), and Multi-Objective Dragonfly Algorithm (MODA). The produced results for the benchmarks and engineering problems show that in general the accuracy and diversity of the proposed algorithm are better compared to the MOWCA and MODA. However, the NSGA-II outperformed the proposed work in some of the cases and showed better accuracy and diversity. Nevertheless, in problems, such as coil compression spring design problem, the quality of solutions produced by the proposed algorithm outperformed all the participated algorithms. Moreover, in regard to the processing time, the proposed work provided better results compared with all the participated algorithms.

关键词： multiobjective algorithms multiobjective evolutionary algorithms MOLPB LPB Learner performance-based behavior algorithm Optimization Metaheuristic optimization algorithm

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Sensitivity Versus Accuracy in Multiclass Problems Using Memetic Pareto evolutionary Neural Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2010年第5期21卷 750-770页

作者： Fernandez Caballero, Juan Carlos Jose Martinez, Francisco Hervas, Cesar Antonio Gutierrez, Pedro Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain ETEA Dept Management & Quantitat Methods Cordoba 14005 Spain

This paper proposes a multiclassification algorithm using multilayer perceptron neural network models. It tries to boost two conflicting main objectives of multiclassifiers: a high correct classification rate level and a high classification rate for each class. This last objective is not usually optimized in classification, but is considered here given the need to obtain high precision in each class in real problems. To solve this machine learning problem, we use a Pareto-based multiobjective optimization methodology based on a memetic evolutionary algorithm. We consider a memetic Pareto evolutionary approach based on the NSGA2 evolutionary algorithm (MPENSGA2). Once the Pareto front is built, two strategies or automatic individual selection are used: the best model in accuracy and the best model in sensitivity ( extremes in the Pareto front). These methodologies are applied to solve 17 classification benchmark problems obtained from the University of California at Irvine (UCI) repository and one complex real classification problem. The models obtained show high accuracy and a high classification rate for each class.

关键词： Accuracy local search multiclassification multiobjective evolutionary algorithms neural networks sensitivity

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