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检索条件"主题词=multi-objective evolutionary algorithm"

共 536 条记录，以下是231-240 订阅

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multi-objective evolutionary Hindsight Experience Replay for Robot Manipulation Tasks

Multi-Objective Evolutionary Hindsight Experience Replay for...

引用

Genetic and evolutionary Computation Conference (GECCO)

作者： Sayar, Erdi Iacca, Giovanni Knoll, Alois Tech Univ Munich Munich Germany Univ Trento Trento Italy

ISBN: (纸本)9798400704949

Reinforcement learning (RL) algorithms often face challenges in efficiently learning effective policies for sparse-reward multi-goal robot manipulation tasks, thus requiring a vast amount of experiences. The state-of-the-art algorithm in the field, Hindsight Experience Replay (HER), addresses this issue by using failed trajectories and replacing the desired goal with hindsight goals. However, HER performs poorly when the desired goal is distant from the initial state. To address this limitation, Hindsight Goal Generation (HGG) has been proposed, which generates a curriculum of goals from already visited states. This curriculum generation is based on a single objective, and does not take obstacles into account. Here, we make a step forward by proposing multi-objective evolutionary Hindsight Experience Replay (MOEHER), a novel curriculum RL algorithm that reformulates curriculum generation considering multiple objectives and obstacles. MOEHER utilizes NSGA-II to generate a curriculum that is optimized w.r.t. four objectives, namely the Q-function, the goal-proximity function, and two distance metrics, while simultaneously satisfying constraints on the obstacles. We evaluate MOEHER on four different sparse-reward robot manipulation tasks, with and without obstacles, and compare it with HER and HGG. The results demonstrate that MOEHER surpasses or performs on par with these methods on the tested tasks.

关键词： multi-objective evolutionary algorithm Hindsight Experience Replay Reinforcement Learning Curriculum Learning Robot Manipulation

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Survey of multi objective evolutionary algorithms

Survey of Multi Objective Evolutionary Algorithms

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Vachhani, Vimal L. Dabhi, Vipul K. Prajapati, Harshadkumar B. Dharmsinh Desai Univ Dept Informat Technol Nadiad India

ISBN: (纸本)9781479970759

multi-objective optimization aims at simultaneously optimizing two or more objectives of a problem. multi-objective evolutionary algorithms (MOEAs) are widely accepted and useful for solving real world multi-objective problems. When we have two or more conflicting objectives of a problem then we can apply MOEA. MOEA generates a set of non-dominated solutions at the end of run, which is called Pareto set. The Pareto front contains set of Pareto solutions. Any MOEA aims to improve (i) convergence of population towards true Pareto front and (ii) diversity of solutions belonging to Pareto set. Generally, an external archive is used by MOEAs to maintain a set of non-dominated Pareto set solutions. Sometimes, Pareto set contains more number of solutions than the size of archive. This paper presents survey of various methods used by different MOEAs for reducing the size of Pareto set while maintaining solutions diversity. It presents comparison of these methods along with their advantages and disadvantages. The paper concludes by giving limitation of crowding distance based method in various scenarios.

关键词： multi-objective evolutionary algorithm Pareto front Convergence Diversity

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Time-Jerk Synthetic Optimal Trajectory Planning of Robot Using Strength Pareto evolutionary algorithm

Time-Jerk Synthetic Optimal Trajectory Planning of Robot Usi...

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International Conference on Advanced Measurement and Test (AMT 2010)

作者： Yan, Jie Gong, Daoxiong Beijing Univ Technol Sch Elect Informat & Control Engn Beijing 100124 Peoples R China

ISBN: (纸本)9780878492695

The Strength Pareto evolutionary algorithm (SPEA) is adopted to find time-jerk synthetic optimal trajectory of a hexapod robot in the joint space. In order to get the optimal trajectory, cubic splines are employed and derived under the constraint condition of via points to assure overall continuity of velocity and acceleration. Taken both the execution time and minimax approach of jerk as objectives, and expressed the kinematics constraints as upper bounds on the absolute values of velocity and acceleration, the mathematic model of time-jerk synthetic optimal trajectory planning is built. Finally, SPEA is adopted to optimize the stair-climbing trajectory of a hexapod robot, the simulation results show that this method can solve the trajectory planning problem effectively, and the stair-climbing trajectory can meet the contradictory objective functions of high speed and low robot vibration well.

关键词： multi-objective evolutionary algorithm minimum time-jerk cubic splines hexapod robot stair-climbing

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A Novel Approach to multi-level evolutionary Design Optimization of a MEMS Device

A Novel Approach to Multi-level Evolutionary Design Optimiza...

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9th International Conference on Electron Spectroscopy

作者： Farnsworth, Michael Benkhelifa, Elhadj Tiwari, Ashutosh Zhu, Meiling Cranfield Univ Decis Engn Ctr Coll Rd Cranfield MK43 0AL Beds England Cranfield Univ Microsyst Nanotechnol Ctr Cranfield MK43 0AL Beds England

ISBN: (纸本)9783642153228

This paper introduces a novel approach to the evolutionary design optimisation of an MEMS bandpass filter, incorporating areas of multi-disciplinary, multi-level and multi-objective design optimisation in the process. In order to demonstrate this approach a comparison is made to previous attempts to design similar bandpass filters, providing comparable results at a significant reduction in functional evaluations. In this endeavour, a circuit equivalent of the MEMS bandpass filter is evolved extrinsically using the SPICE Simulator.

关键词： multi-Disciplinary Optimisation multi-objective evolutionary algorithm multi-Level Optimisation MEMS Micro-Electro-Mechanical Systems Extrinsic Evolution

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Balancing Operation Cost and Carbon Emissions in multi-Energy Microgrid Scheduling Using Joint Operator evolutionary algorithm 36

Balancing Operation Cost and Carbon Emissions in Multi-Energ...

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36th Chinese Control and Decision Conference (CCDC)

作者： Li, Bin Li, Qincheng Li, Jia Liu, Zhitao Su, Hongye Zhejiang Univ Inst Cyber Syst & Control State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

ISBN: (纸本)9798350387780;9798350387797

A multi-energy microgrid (MEMG), incorporating a combined heat and power (CHP) system and electric vehicles (EVs), is an important infrastructure to meet multiple energy demands and reduce carbon emissions. However, operation cost minimization and carbon emissions minimization in the MEMG conflict with each other. Therefore, this paper proposes a multi-objective optimal scheduling model for the MEMG, aimed at achieving a balance between operation cost and carbon emissions. Moreover, a novel multi-objective evolutionary algorithm (MOEA) called joint operator evolutionary algorithm (JOEA) is developed to solve this model. Experimental results indicate that JOEA has excellent performance on the proposed multi-objective optimal scheduling problem. In addition, JOEA is compared with several well-known MOEAs on this engineering problem and 20 benchmark problems. The comparison results show that JOEA outperforms other MOEAs and has competitive performance.

关键词： multi-energy microgrid Operation cost Carbon emissions multi-objective evolutionary algorithm

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Robustness Analysis in evolutionary multi-objective Optimization Applied to VAR Planning in Electrical Distribution Networks

Robustness Analysis in Evolutionary Multi-Objective Optimiza...

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9th European Conference on evolutionary Computation in Combinatorial Optimization

作者： Barrico, Carlos Antunes, Carlos Henggeler Pires, Dulce Fernao INESC Rua Antero Quental 199 P-3000033 Coimbra Portugal

ISBN: (纸本)9783642010088

In this paper an approach to robustness analysis in evolutionary multi-objective optimization is applied to the problem of locating and sizing capacitors for reactive power compensation (VAR planning) in electric radial distribution networks. The main goal of this evolutionary algorithm is to find a non-dominated front containing the most robust non-dominated Solutions also ensuring diversity along the front. A concept of degree of robustness is incorporated into the evolutionary algorithm, which intervenes in the Computation of the fitness value assigned to Solutions. Two objective functions of technical and economical nature are explicitly considered in the mathematical model: minimization of system losses and minimization of capacitor installation costs. Constraints refer to quality of service, power How, and technical requirements. It is assumed that some input data are subject to perturbations, both concerning the objective functions and the constraints coefficients.

关键词： robustness analysis multi-objective evolutionary algorithm reactive power compensation problem

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A multi-objective evolutionary Approach to Optimize the Morphology of a Six Articulated-Wheeled Robot 4

A Multi-Objective Evolutionary Approach to Optimize the Morp...

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4th IEEE International Conference on Artificial Intelligence with applications in Engineering and Technology (ICAIET)

作者： Lim, S. H. Teo, J. Univ Malaysia Sabah Fac Comp & Informat Evolutionary Comp Lab Kota Kinabalu Malaysia

ISBN: (纸本)9781479979103

This paper proposed a multi-objective evolutionary algorithm (MOEA) in designing the morphology of a six articulated-wheeled robot (SAWR) which has the ability to perform climbing motion. The first objective is to minimize the morphology design while the second objective is to maximize the performance of the SAWR in performing the climbing motion. Results show that the proposed MOEA is capable to produce a set of Pareto optimal solutions from the smallest SAWR with poor performance to the largest SAWR with robust performance. The Pareto set of optimal solutions provide users a choice of solutions for trade-off between the two objectives.

关键词： multi-objective evolutionary algorithm hybrid mobile robot optimized hybrid mobile robot

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A Novel Hybrid evolutionary Strategy and its Periodization with multi-objective Genetic Optimizers

A Novel Hybrid Evolutionary Strategy and its Periodization w...

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2010 IEEE World Congress on Computational Intelligence

作者： Kaufmann, Paul Knieper, Tobias Platzner, Marco Univ Gesamthsch Paderborn Dept Comp Sci D-33098 Paderborn Germany

ISBN: (纸本)9781424481262

This work investigates the effects of the periodization of local and global multi-objective search algorithms. To this, we introduce a model for periodization and define a new multi-objective evolutionary algorithm adopting concepts from evolutionary Strategies and NSGAII. We show that our method, especially when periodized with standard multi-objective genetic algorithms, excels for the evolution of digital circuits on the Cartesian Genetic Programming model as well as on some standard benchmarks such as the ZDT6.

关键词： Cartesian genetic programming genetic algorithms hybrid evolutionary strategy multi-objective evolutionary algorithm multi-objective genetic optimizers multi-objective search algorithms periodization search problems

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A new optimal dispatch method for the day-ahead electricity market using a multiobjective evolutionary approach

A new optimal dispatch method for the day-ahead electricity ...

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39th North American Power Symposium

作者： Li, Dapeng Pahwa, Anil Das, Sanjoy Rodrigo, Deepal Kansas State Univ Dept Elect & Comp Engn Manhattan KS 66506 USA Midwest ISO Inc Carmel IN 46032 USA

ISBN: (纸本)9781424417254

In this paper, a novel approach to optimally operate a day-ahead electricity market with considerations of market power is proposed. First, this approach formulates the 1-hour based optimal dispatch problem as a nonlinear constrained multi-objective optimization problem which simultaneously minimizes fuel cost, emissions and the modified Herfindahl-Hirschman index. Then the Non-dominated Sorting Genetic algorithm II (NSGA-II) is used to solve this optimization problem. Simulation results illustrate that this algorithm is capable of finding the Pareto-optimal front in a single run. The best operation schedule for the whole 24 hours can be chosen by taking the ramp rate constraints of generators into account. This approach can provide the market operator with much operating feasibility and thus is more realistic and feasible.

关键词： economic dispatch market power day-ahead electricity market multi-objective evolutionary algorithm

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A Preference-based multi-objective evolutionary Strategy for Ab Initio Prediction of Proteins 5

A Preference-based Multi-objective Evolutionary Strategy for...

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2017 International Conference on Progress in Informatics and Computing(PIC)

作者： Zhenyu Song Yajiao Tang Xingqian Chen Shuangbao Song Shuangyu Song Shangce Gao Faculty of Engineering University of Toyama

ISBN: (纸本)9781538619797;9781538619780

Predicting the three-dimensional structure of a protein from its amino acid sequence is an important issue in the field of computational biology and bioinformatics. It remains as an unsolved problem and attract enormous researchers' interests. Different from most conventional methods, we model the protein structure prediction(PSP) problem as a multiobjective optimization problem. A three-objective energy function based on three physical terms is designed to evaluate a protein conformation. A multi-objective evolutionary strategy algorithm coupled with preference information is proposed in this study. The preference information is used in the survival criteria, focusing on the exploration of search process. The experimental results based on five proteins in PDB library demonstrate the effectiveness of proposed method. The analysis of Pareto fronts indicates that the preference information can make solutions diverse in genotypic space. Thus, the proposed method gives a new perspective for solving PSP problems.

关键词： Protein structure prediction multi-objective optimization multi-objective evolutionary algorithm preference information utility function

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