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Boosting the 3D thermal-aware floorplanning problem through a master-worker parallel MOEA

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2013年第8期25卷 1089-1103页

作者： Arnaldo, Ignacio Cuesta-Infante, Alfredo Manuel Colmenar, J. Risco-Martin, Jose L. Ayala, Jose L. Univ Complutense Madrid DACYA Madrid Spain Univ Complutense Madrid CES Felipe 2 Madrid Aranjuez Spain

The increasing transistor scale integration poses, among others, the thermal-aware floorplanning problem consisting of how to place the hardware components in order to reduce overheating by dissipation. Because of the huge amount of feasible floorplans, most of the solutions found in the literature include an evolutionary algorithm for, either partially or completely, carrying out the task of floorplanning. evolutionary algorithms usually have a bottleneck in the fitness evaluation. In the problem of thermal-aware floorplanning, the layout evaluation by the thermal model takes 99.5% of the computational time for the best floorplanning algorithm proposed so far. The contribution of this paper is to present a parallelization of this evaluation phase in a master-worker model to achieve a dramatic speed-up of the thermal-aware floorplanning process. Exhaustive experimentation was carried out over 3D integrated circuits, with 48 and 128 cores, outperforming previous published works. Copyright (c) 2012 John Wiley & Sons, Ltd.

关键词： thermal-aware floorplanning optimization multi-objective evolutionary algorithm parallelization

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Reconnaissance and Confirmation Task Planning of multiple Fixed-Wing UAVs with Specific Payloads: A Comparison Study

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2022年第4期26卷 570-580页

作者： Zhang, Hao Dou, Lihua Xin, Bin Zhang, Ruowei Wang, Qing Beijing Inst Technol Sch Automat 5 Zhongguancun South St Beijing 100081 Peoples R China Beijing Inst Technol Beijing Adv Innovat Ctr Intelligent Robots & Syst 5 Zhongguancun South St Beijing 100081 Peoples R China

In this study, the reconnaissance and confirmation task planning of multiple fixed-wing unmanned aerial vehicles (UAV) with specific payloads, which is an NP-hard problem with strong constraints and mixed variables, is decomposed into two subproblems, task allocation with "payload-target" matching constraints, and fast path planning of the UAV group, for which two mathematical models are respectively established. A bilayer collaborative solution framework is also proposed. The outer layer optimizes the allocation scheme between the UAVs and targets, whereas the inner layer generates the UAV path and evaluates the outer scheme. In the outer layer, a unified encoding based on the grouping and pairing relationship between UAVs and targets is proposed. The corresponding combinatorial mutation operators are then designed for the representative NSGA-II, MOEA/D-AWA, and DMOEA-epsilon C algorithms. In the inner layer, an efficient heuristic algorithm is used to solve the path planning of each UAV group. The simulation results verify the effectiveness of the cooperative bi-layer solution scheme and the combined mutation operators. At the same time, compared with the NSGA-II and MOEA/D-AWA, DMOEA-epsilon C can obtain a significantly better Pareto front and can weigh the assigned number of UAVs and the total task completion time to generate more diversified reconnaissance confirmation execution schemes.

关键词： multi-objective evolutionary algorithm heterogeneous UAVs task planning task allocation path planning

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Impact of selection methods on the diversity of many-objective Pareto set approximations 21

Impact of selection methods on the diversity of many-objecti...

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21st International Conference on Knowledge - Based and Intelligent Information and Engineering Systems (KES)

作者： Marti, Luis Segredo, Eduardo Sanchez-Pi, Nayat Hart, Emma Univ Fed Fluminense Inst Comp Niteroi RJ Brazil Univ Paris Saclay CNRS INRIA LRI TAU Team Paris France Edinburgh Napier Univ Sch Comp Edinburgh Midlothian Scotland Univ La Laguna Dpto Ingn Informat & Sistemas San Cristobal Laguna Spain Univ Estado Rio De Janeiro DICC IME Rio De Janeiro RJ Brazil

Selection methods are a key component of all multi-objective and, consequently, many-objective optimisation evolutionary algorithms. They must perform two main tasks simultaneously. First of all, they must select individuals that are as close as possible to the Pareto optimal front (convergence). Second, but not less important, they must help the evolutionary approach to provide a diverse population. In this paper, we carry out a comprehensive analysis of state-of-the-art selection methods with different features aimed to determine the impact that this component has on the diversity preserved by well-known multi-objective optimisers when dealing with many-objective problems. The algorithms considered herein, which incorporate Pareto-based and indicator based selection schemes, are analysed through their application to the Walking Fish Group (WFG) test suite taking into account an increasing number of objective functions. algorithmic approaches are assessed via a set of performance indicators specifically proposed for measuring the diversity of a solution set, such as the Diversity Measure and the Diversity Comparison Indicator. Hypervolume, which measures convergence in addition to diversity, is also used for comparison purposes. The experimental evaluation points out that the reference-point-based selection scheme of the Non-dominated Sorting Genetic algorithm III (NSGA-III) and a modified version of the Non-dominated Sorting Genetic algorithm II (NSGA-II), where the crowding distance is replaced by the Euclidean distance, yield the best results. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： multi-objective evolutionary algorithm many-objective optimisation selection mechanism diversity preservation

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Parallelization of Corner Sort with CUDA for Many-objective Optimization

Parallelization of Corner Sort with CUDA for Many-Objective ...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Bharti, Vandana Singhal, Aryan Saxena, Anant Biswas, Bhaskar Shukla, Kaushal Kumar Indian Inst Technol BHU Varanasi Uttar Pradesh India

ISBN: (纸本)9781450392372

Due to advancements in hardware capabilities and computation power, multi-objective optimization has recently been used in many industrial problems. These problems usually have multiple objectives of conflicting nature. To solve such problems, multi-objective evolutionary algorithms (MOEAs) utilize Non-dominated Sorting (NDS) algorithms to rank the viability of the solutions efficiently. Researchers have focused on improving the time complexity of NDS algorithms for a long time due to their wide range of applications. With the increase in the use of GPUs for general-purpose scientific computing, it is now becoming possible to reduce the computation cost of such algorithms. In this paper, we have analyzed one such NDS algorithm, Corner Sort, and highlighted two areas within it with a high scope of parallelism. We propose a highly efficient, parallelized version of Corner Sort, implemented using CUDA framework. Utilizing the thousands of cores in a GPU, our algorithm is able to break the solution set into smaller chunks and simultaneously process them. Furthermore, the comparison between two solutions across all the objectives is done parallelly as well. On benchmark datasets, our algorithm performs up to 10x faster than the serial algorithm, and its performance improves for larger datasets, irrespective of the number of objectives.

关键词： Pareto Dominance Non-dominated Sorting multi-objective evolutionary algorithm Parallelism CUDA

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A multi-objective genetic algorithm with controllable convergence on knee regions

A multi-objective genetic algorithm with controllable conver...

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IEEE Congress on evolutionary Computation

作者： Rachmawati, L. Srinivasan, D. Natl Univ Singapore Dept Elect & Comp Engn 4 Engn Dr 3 Singapore 117576 Singapore

ISBN: (纸本)9780780394872

A knee region on the Pareto-optimal front of a multi-objective optimization problem consists of solutions with the maximum marginal rates of return, i.e. solutions for which an improvement on one objective is accompanied by a severe degradation in another. The trade-off characteristic renders such solutions of particular interest in practical applications. This paper presents a multi-objective evolutionary algorithm focused on the knee regions. The algorithm facilitates better decision making in contexts where high marginal rates of return are desirable by providing the Decision Makers with a high concentration of solutions on the knee regions of the Pareto-front approximation. The proposed approach computes a transformation of the original objectives based on weighted-sum functions. The transformed functions identify niches which correspond to knee regions in the objective space. The extent and density of coverage of the knee regions are controllable by the niche strength and pool size parameters. Although based on weighted-sums, the algorithm is capable of finding solutions in the non-convex regions of the Pareto-front. The application of the algorithm on test problems with multiple knee regions and skew on the Pareto-optimal front produces promising results.

关键词： Knee region knee niches algorithms multi-objective evolutionary algorithm decision makers test problem Skew

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MOSCFRA: A multi-objective Genetic Approach for Simultaneous Clustering and Gene Ranking

MOSCFRA: A Multi-objective Genetic Approach for Simultaneous...

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7th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2010)

作者： Mondal, Kartick Chandra Mukhopadhyay, Anirban Maulik, Ujjwal Bandhyapadhyay, Sanghamitra Pasquier, Nicolas Univ Nice Sophia Antipolis Lab I3S CNRS UMR 6070 F-06108 Nice France Univ Kalyani Dept Comp Sci & Engn Kalyani 741235 W Bengal India Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 W Bengal India Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India

ISBN: (纸本)9783642219450

Microarray experiments generate a large amount of data which is used to discover the genetic background of diseases and to know the characteristics of genes. Clustering the tissue samples according to their co-expressed behavior and characteristics is an important tool for partitioning the dataset. Finding the clusters of a given dataset is a difficult task. This task of clustering is even more difficult when we try to find the rank of each gene, which is known as Gene Ranking, according to their abilities to distinguish different classes of samples. In the literature, many algorithms are available for sample clustering and gene ranking or selection, separately. A few algorithms are also available for simultaneous clustering and feature selection. In this article, we have proposed a new approach for clustering the samples and ranking the genes, simultaneously. A novel encoding technique for the chromosomes is proposed for this purpose and the work is accompleshed using a multi-objective evolutionary technique. Results have been demonstrated for both artificial and real-life gene expression data sets.

关键词： multi-objective evolutionary algorithm Gene Ranking Clustering Gene Expression Data

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A multi-objective Planning Framework for Optimal Integration of Distributed Generations

A Multi-Objective Planning Framework for Optimal Integration...

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3rd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies Europe (ISGT Europe)

作者： Pokharel, Keshav Mokhtar, Maizura Howe, Joe Univ Cent Lancashire Sch Comp Engn & Phys Sci Ctr Energy & Power Management Preston PR1 2HE Lancs England

ISBN: (纸本)9781467325950;9781467325967

This paper presents an evolutionary algorithm for analyzing the best mix of distributed generations (DG) in a distribution network. The multi-objective optimization aims at minimizing the total cost of real power generation, line losses and CO2 emissions, and maximizing the benefits from the DG over a 20 years planning horizon. The method assesses the fault current constraint imposed on the distribution network by the existing and new DG in order not to violate the short circuit capacity of existing switchgear. The analysis utilizes one of the highly regarded evolutionary algorithm, the Strength Pareto evolutionary algorithm 2 (SPEA2) for multi-objective optimization and MATPOWER for solving the optimal power flow problems.

关键词： multi-objective evolutionary algorithm strength pareto evolutionary algorithm 2 distributed generation distribution generation planning three phase symmetrical fault

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Computational Complexity Measures for Many-objective Optimization Problems

Computational Complexity Measures for Many-objective Optimiz...

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Conference on Conquering Complexity - Challenges and Opportunities

作者： Curry, David M. Dagli, Cihan H. Missouri Univ Sci & Technol Dept Engn Management & Syst Engn Rolla MO 65409 USA

multi-objective Optimization Problems (MOPs) are commonly encountered in the study and design of complex systems. Pareto dominance is the most common relationship used to compare solutions in MOPs, however as the number of objectives grows beyond three, Pareto dominance alone is no longer satisfactory. These problems are termed "Many-objective Optimization Problems (MaOPs)". While most MaOP algorithms are modifications of common MOP algorithms, determining the impact on their computational complexity is difficult. This paper defines computational complexity measures for these algorithms and applies these measures to a multi-objective evolutionary algorithm (MOEA) and its MaOP counterpart. (C) 2014 The Authors. Published by Elsevier B.V.

关键词： multi-objective optimization problem MOP many-objective optimization problem MaOP computational complexity multi-objective evolutionary algorithm MOEA

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multi-objective Learning of Neural Network Time Series Prediction Intervals 18th

Multi-objective Learning of Neural Network Time Series Predi...

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18th EPIA Conference on Artificial Intelligence (EPIA)

作者： Pereira, Pedro Jose Cortez, Paulo Mendes, Rui Univ Minho Dept Informat Syst ALGORITMI Ctr P-4804533 Guimaraes Portugal Univ Minho Ctr Biol Engn ALGORITMI Ctr Dept Informat P-4710057 Braga Portugal

ISBN: (纸本)9783319653402;9783319653396

In this paper, we address multi-step ahead time series Prediction Intervals (PI). We extend two Neural Network (NN) methods, Lower Upper Bound Estimation (LUBE) and multi-objective evolutionary algorithm (MOEA) LUBE (MLUBE), for multi-step PI. Furthermore, we propose two new MOEA methods based on a 2-phase gradient and MOEA based learning: M2LUBET1 and M2LUBET2. Also, we present a robust evaluation procedure to compare PI methods. Using four distinct seasonal time series, we compared all four PI methods. Overall, competitive results were achieved by the 2-phase learning methods, in terms of both predictive performance and computational effort.

关键词： multi-objective evolutionary algorithm multilayer perceptron multi-step ahead prediction Prediction intervals Time series

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Automatic Code Generation for Microcontroller-Based System Using multi-objective Linear Genetic Programming

Automatic Code Generation for Microcontroller-Based System U...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Ferrel Serruto, Wildor Alfaro Casas, Luis Univ Nacl San Agustin Arequipa Dept Acad Ingn Elect Arequipa Peru

ISBN: (纸本)9781538626528

Microcontroller-based systems often include peripheral devices such as matrix keyboard and character LCD module among others. We propose the application of the multi-objective linear genetic programming, for automatic generation of the assembly driver routines for these devices, to perform the operations: matrix keyboard scan, LCD module initialization and character display on LCD. For fitness evaluation, we assign a function to be maximized to each bit of the binary result or to the timing diagram of each used microcontroller Port pins. This decomposition of the problem used in a multi-objective evolutionary algorithm allows generating programs, in some cases, with smaller code size or shorter execution time than programs written by a human programmer.

关键词： multi-objective evolutionary algorithm Linear genetic programming Automatic code generation Microcontroller-based system

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