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检索条件"主题词=Evolutionary multi-objective optimization"

共 283 条记录，以下是31-40 订阅

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A Kind of evolutionary multi-objective optimization Algorithm Based on AIS

A Kind of Evolutionary Multi-objective Optimization Algorith...

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International Conference on Materials Engineering and Mechanical Automation (MEMA 2013)

作者： Li, Ming-song Shaoguan Univ Shaoguan Guangdong Peoples R China

ISBN: (纸本)9783037859018

Problem of multi-objective optimization based on Artificial Immune System (AIS) is an important research area of current evolutionary computing. Starting from the intelligent information processing mechanism of immune theory and the immune system itself, a kind of evolutionary multi-objective optimization algorithm based on AIS is proposed. Clonal selection, scattered crossover and hypermutation based on the learning mechanism are characteristics of the algorithm. Algorithm implements clonal selection according to the distribution of individuals in the objective space, which benefit obtaining Pareto optimal boundary distributed more widely and speed up the convergence. Compared with the existing algorithms, the algorithm has been greatly improved in convergence, diversity, and distribution of solutions.

关键词： Artificial Immune System evolutionary multi-objective optimization Clonal selection Scattered crossover Hypermutation

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A Knee Point based evolutionary multi-objective optimization for Mission Planning Problems 17

A Knee Point based Evolutionary Multi-objective Optimization...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Ramirez-Atencia, Cristian Mostaghim, Sanaz Camacho, David Univ Autonoma Madrid Dept Ingn Informat Madrid Spain Univ Magdeburg Fac Comp Sci Magdeburg Germany

ISBN: (纸本)9781450349208

The current boom of Unmanned Aerial Vehicles (UAVs) is increasing the number of potential industrial and research applications. One of the most demanded topics in this area is related to the automated planning of a UAVs swarm, controlled by one or several Ground Control Stations (GCSs). In this context, there are several variables that influence the selection of the most appropriate plan, such as the makespan, the cost or the risk of the mission. This problem can be seen as a multi-objective optimization Problem (MOP). On previous approaches, the problem was modelled as a Constraint Satisfaction Problem (CSP) and solved using a multi-objective Genetic Algorithm (MOGA), so a Pareto Optimal Frontier (POE) was obtained. The main problem with this approach is based on the large number of obtained solutions, which hinders the selection of the best solution. This paper presents a new algorithm that has been designed to obtain the most significant solutions in the POF. This approach is based on Knee Points applied to MOGA. The new algorithm has been proved in a real scenario with different number of optimization variables, the experimental results show a significant improvement of the algorithm performance.

关键词： UAVs Mission Planning Constraint Satisfaction Problems evolutionary multi-objective optimization Knee Point

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Periodical Weight Vector Update Using an Unbounded External Archive for Decomposition-Based evolutionary multi-objective optimization

Periodical Weight Vector Update Using an Unbounded External ...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Chen, Longcan Pang, Lie Meng Ishibuchi, Hisao Shang, Ke Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen Peoples R China

ISBN: (纸本)9781728190488

Decomposition-based evolutionary multi-objective optimization (EMO) algorithms are very popular. The basic idea of decomposition-based EMO algorithms is to decompose a multi/many-objective optimization problem into several single-objective subproblems using a set of weight vectors and a scalarizing function. The weight vector specification plays an important role in decomposition-based EMO algorithms for obtaining a set of well-distributed solutions. In this paper, we propose a new method to update weight vectors for decomposition-based EMO algorithms. The proposed method uses an unbounded external archive to store all the non-dominated solutions among examined solutions during the execution of an EMO algorithm, and periodically select a set of uniformly distributed solutions from the archive. Then, the selected solutions are projected to the weight vector space and used as a set of weight vectors for decomposition-based algorithms. The usefulness of the proposed weight vector update method is demonstrated by integrating it into a most frequently used decomposition-based EMO algorithm, i.e., MOEA/D. Experimental results show that the proposed weight vector update method works well on MOEA/D. Our experimental results also show that MOEA/D with the proposed weight vector update method outperforms other weight vector adaptation-based algorithms on many-objective test problems.

关键词： evolutionary multi-objective optimization many-objective optimization unbounded external archive

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An Interactive evolutionary multi-objective optimization Method Based on Polyhedral Cones

An Interactive Evolutionary Multi-objective Optimization Met...

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4th Conference on Learning and Intelligent optimization

作者： Sinha, Ankur Korhonen, Pekka Wallenius, Jyrki Deb, Kalyanmoy Aalto Univ Sch Econ Dept Business Technol Helsinki 00076 Finland

ISBN: (纸本)9783642137990

This paper suggests a preference based methodology, where the information provided by the decision maker in the intermediate runs of an evolutionary multi-objective optimization algorithm is used to construct a polyhedral cone. This polyhedral cone is used to eliminate a part of the search space and conduct a more focussed search. The domination principle is modified, to look for better solutions lying in the region of interest. The search is terminated by using a local search based termination criterion. Results have been presented on two to five objective problems and the efficacy of the procedure has been tested.

关键词： evolutionary multi-objective optimization multiple criteria decision-making interactive multi-objective optimization sequential quadratic programming preference based multi-objective optimization

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Energy-Efficient Differentiated Coverage of Dynamic Objects using an Improved evolutionary multi-objective optimization Algorithm with Fuzzy-Dominance

Energy-Efficient Differentiated Coverage of Dynamic Objects ...

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IEEE Congress on evolutionary Computation (CEC)

作者： Sengupta, Soumyadip Das, Swagatam Nasir, Md. Vasilakos, Athanasios V. Pedrycz, Witold Jadavpur Univ Dept Elect & Telecommun Eng Kolkata India

ISBN: (纸本)9781467315098

We present an energy efficient sensor manager for differentiated coverage of dynamic object group changing their positions with time. The information about the location of the object group is provided to the sensor manager. The manager invokes optimization algorithm whenever the obtained coverage falls below a threshold to sleep schedule the sensor network. multi-objective optimization (MO) algorithms help in finding a better trade-off among energy consumption, lifetime, and coverage. Here the motion of the particle is modeled to follow a polynomial variation and with a constant acceleration. We formulate the scheduling problem as a combinatorial, constrained and multi-objective optimization problem with energy and non-coverage as the two objectives to be minimized. The proposed scheme uses a recent variant of a powerful MO algorithm known as Decomposition based multi-objective evolutionary Algorithm (MOEA/D). Systematic comparison with the original MOEA/D and another well-known MO algorithm, NSGA-II (Non-dominated Sorting Genetic Algorithm) quantifies the superiority of the proposed approach.

关键词： Wireless sensor networks differentiated coverage node deployment density control evolutionary multi-objective optimization

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A Hybrid Development Platform for evolutionary multi-objective optimization

A Hybrid Development Platform for Evolutionary Multi-Objecti...

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IEEE Congress on evolutionary Computation (CEC)

作者： Shen, Ruimin Zheng, Jinhua Li, Miqing Xiangtan Univ Sch Math & Computat Sci Xiangtan Hunan Peoples R China Xiangtan Univ Inst Informat Engn Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan Hunan Peoples R China Brunel Univ Dept Informat Syst & Comp Uxbridge Middx England

ISBN: (纸本)9781479974924

This paper introduces an optimization template library (OTL), a cross-platform C++ template library for multi-objective optimization. OTL has an object-oriented architecture, which allows that different modules can be arbitrarily combined with each other. Moreover, the C++ template technique is used to increase the flexibility of OTL. Meanwhile, generic programming is widely used in OTL, and the generic algorithms can be used to process different data structures. However, compared with C++, the Python script is more suitable for building the experimental platform. To ensure that all attributes of the experimental results can be fully maintained, a database is used to store the experimental data. Moreover, batch experiments can be easily defined in a set of configuration files;thus, the experiments can be executed automatically without human intervention. In addition, serial and various parallel execution modes are supported, and the user can easily switch the running mode to distributed computing to increase the computing speed. Finally, a highly customizable data visualization tool is created to play back the data sample stored in the database. From a series of comparative studies, the accuracy and running performance of OTL are verified by the statistical results.

关键词： evolutionary multi-objective optimization object-oriented architecture generic programming

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Indicator-Aware Talking Face Generation Based on evolutionary multi-objective optimization

Indicator-Aware Talking Face Generation Based on Evolutionar...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Guo, Ge Hong, Wenjing Zhou, Chaoyong Yao, Xin Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Ping An Technol Shenzhen 518055 Peoples R China

ISBN: (纸本)9781665487689

Audio-driven talking face generation (ATFG) is typically multifaceted, requiring high-quality faces, lip movements synchronized with the audio, and plausible facial expressions. Previous works have mainly focused on minimizing a single loss function constructed on the basis of consideration of the multiple performance requirements for an ATFG model. However, as a proxy, the loss function does not always yield optimal performance when minimised. Moreover, it is unlikely that there is a single model that can be optimal in terms of all relevant quality indicators. In this paper, we formulate the training of an ATFG model as an indicator-aware multi-objective optimization problem and propose a novel approach, namely evolutionary multi-objective Indicator-aware audio-driven talking face Generation (EMIG), to solve this problem. EMIG explicitly uses the quality indicators in the design process of ATFG models and can be easily adapted to the preferences of users. Experimental studies demonstrate the potential advantages of evolutionary multi-objective optimization for solving the task of ATFG and the effectiveness of EMIG over five state-of-the-art ATFG methods.

关键词： Audio-driven talking face generation evolutionary multi-objective optimization Indicator-aware

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An Analysis of the Preferences of Distribution Indicators in evolutionary multi-objective optimization 13

An Analysis of the Preferences of Distribution Indicators in...

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13th IEEE Congress on evolutionary Computation, CEC 2024

作者： Falcón-Cardona, Jesús Guillermo Nezhadmoghaddam, Mahboubeh Bernal-Zubieta, Emilio School of Engineering and Sciences Tecnologico de Monterrey N.L. Monterrey64849 Mexico

ISBN: (纸本)9798350308365

The distribution of objective vectors in a Pareto Front Approximation (PFA) is crucial for representing the associated manifold accurately. Distribution Indicators (DIs) assess the distribution of a PFA numerically, utilizing concepts like distance calculation, Biodiversity, Entropy, Potential Energy, or Clustering. Despite the diversity of DIs, their strengths and weaknesses across assessment scenarios are not well-understood. This paper introduces a taxonomy for classifying DIs, followed by a preference analysis of nine DIs, each representing a category in the taxonomy. Experimental results, considering various PFAs under controlled scenarios (loss of coverage, loss of uniformity, pathological distributions), reveal that some DIs can be misleading and need cautious use. Additionally, DIs based on Biodiversity and Potential Energy are promising for PFA evaluation and comparison of multi-objective evolutionary Algorithms. © 2024 IEEE.

关键词： Distribution Indicators evolutionary multi-objective optimization Quality Indicators Uniformity and Diversity

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How to Find a Large Solution Set to Cover the Entire Pareto Front in evolutionary multi-objective optimization

How to Find a Large Solution Set to Cover the Entire Pareto ...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Pang, Lie Meng Nan, Yang Ishibuchi, Hisao Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9798350337020

Recently, it has been pointed out in many studies that the performance of evolutionary multi-objective optimization (EMO) algorithms can be improved by selecting solutions from all examined solutions stored in an unbounded external archive. This is because in general the final population is not the best subset of the examined solutions. To obtain a good final solution set in such a solution selection framework, subset selection from a large candidate set (i.e., all examined solutions) has been studied. However, since good subsets cannot be obtained from poor candidate sets, a more important issue is how to find a good candidate set, which is the focus of this paper. In this paper, we first visually demonstrate that the entire Pareto front is not covered by the examined solutions through computational experiments using MOEA/D, NSGA-III and SMS-EMOA on DTLZ test problems. That is, the examined solution set stored in the unbounded archive has some large holes (i.e., some uncovered area of the Pareto front). Next, to evaluate the quality of the examined solution set (i.e., to measure the size of the largest hole), we propose the use of a variant of the inverted generational distance (IGD) indicator. Then, we propose a simple modification of EMO algorithms to improve the quality of the examined solution set. Finally, we demonstrate the effectiveness of the proposed modification through computational experiments. © 2023 IEEE.

关键词： evolutionary multi-objective optimization Pareto front solution selection framework unbounded external archive

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multi-document Summarization using evolutionary multi-objective optimization 17

Multi-document Summarization using Evolutionary Multi-object...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Jung, Chihoon Datta, Rituparna Segev, Aviv Korea Adv Inst Sci & Technol KSE Daejeon South Korea Korea Adv Inst Sci & Technol CS Daejeon South Korea

ISBN: (纸本)9781450349390

Text summarization aims to generate condensed summary from a large set of documents on the same topic. We formulate text summarization task as a multi-objective optimization problem by defining information coverage and diversity as two conflicting objective functions. The result solutions represent summaries that ensure the maximum coverage of the original document and the diversity of the sentences in the summary among each other. The initial experiment using DUC2002 multi-document summarization task dataset and ROUGE evaluation metric shows that the proposed method generates high ROUGE score summaries and is comparable to the state-of-the-art summarization methods.

关键词： Text Summarization evolutionary multi-objective optimization

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