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2010 IEEE World Congress on Computational Intelligence

作者： Datta, Rituparna Majumder, Anima Indian Inst Technol Dept Mech Engn Kanpur Genet Algorithms Lab KanGAL Kanpur 208016 Uttar Pradesh India Indian Inst Technol Dept Elect Engn Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)9781424481262

Machining parameters optimization is very crucial in any machining process. This research focuses on Multi-objective evolutionary algorithm based optimization technique, to determine optimal cutting parameters (cutting speed, feed, and depth of cut) in turning operation. Two conflicting objectives (operation time and tool life) with three constraints, which depends on the turning parameters, are optimized using Genetic algorithm (GAs). The Pareto-optimal front of the bi-objective problem is obtained using Non-dominated Sorting Genetic algorithm (NSGA-II). The extreme and intermediate points of Pareto optimal front is verified using Real coded Genetic algorithm (RGA) as well as epsilon-constraint method. The performance of NSGA-II is found to be more effective and efficient as compared to micro-GA. Innovization study carried out to correlate cutting parameters with the aforementioned objective functions. The effect of cutting speed is found more as compared to feed rate and depth of cut.

关键词： Pareto optimal front Pareto optimisation cutting genetic algorithms machining parameters optimization machining process multiobjective evolutionary algorithm nondominated sorting genetic algorithm optimal cutting real coded genetic algorithm turning (machining) turning process parameters optimization

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Multi-Objective Optimisation by Reinforcement Learning

Multi-Objective Optimisation by Reinforcement Learning

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2010 IEEE World Congress on Computational Intelligence

作者： Liao, H. L. Wu, Q. H. Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England

ISBN: (纸本)9781424481262

This paper presents a multi-objective optimisation by reinforcement learning, called MORL, to solve complex multi-objective optimisation problems, in particular those in a high-dimensional space. In MORL, the search is undertaken on individual dimension in a high-dimensional space via a path selected by an estimated path value. Path values, estimated by weighting the state values on the selected path, represent the potentiality of finding a better solution if searching on the paths, and are used to memorize the quality of previously visited states. In MORL, visited states are assigned with different immediate rewards by comparing the objective vector of current state with those of the Pareto optimal solutions found previously. These Pareto optimal solutions are stored in an elite list, which keeps track of the non-dominated solutions found so far and is used to construct the Pareto front at the end of the optimisation process. MORL is compared with a promising multi-objective evolutionary algorithm based on decomposition (MOEA/D) on four widely-used benchmark functions. The simulation results have demonstrated that MORL is superior over MOEA/D with respect to the accuracy and the range of the Pareto fronts, especially in solving high-dimensional multi-objective optimisation problems.

关键词： Pareto front Pareto optimal solution Pareto optimisation evolutionary computation learning (artificial intelligence) multiobjective evolutionary algorithm multiobjective optimisation objective vector path value estimation reinforcement learning

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Determining Crop-Production Functions using Multi-Objective evolutionary algorithms

Determining Crop-Production Functions using Multi-Objective ...

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2010 IEEE World Congress on Computational Intelligence

作者： de Paly, Michael Schuetze, Niels Zell, Andreas Univ Tubingen Ctr Bioinformat Tuebingen ZBIT Tubingen Germany Tech Univ Dresden Inst Hydrol & Meterol Dresden Germany

ISBN: (纸本)9781424481262

The determination of crop production functions which describe the relationship between irrigation water and crop yield under the assumption of optimal irrigation scheduling is a major building block for a more efficient and sustainable water management. In this paper we introduce a methodology to determine the entire crop production function for a given scenario within a single run of a multi-objective evolutionary algorithm. Further we compare the performance of four major algorithms (NSGA-II, NSDE, DEMO, and MO-CMA-ES), and a single-objective approach based on differential evolution on three different scenarios and two different population initialization methods on this problem. We show that the combination of a problem specific initialization with MO-CMA-ES is able to determine crop production functions which are extremely close to actual ones.

关键词： DEMO MO-CMA-ES NSDE NSGA-II crop yield crop-production function crops differential evolution evolutionary computation irrigation irrigation water multiobjective evolutionary algorithm optimal irrigation scheduling scheduling single-objective approach sustainable water management water

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Runtime Analysis of (1+1)-EA on a Biobjective Test Function in Unbounded Integer Search Space

Runtime Analysis of (1+1)-EA on a Biobjective Test Function ...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Rudolph, Gunter TU Dortmund University Department of Computer Science Dortmund Germany

Runtime results of multiobjective evolutionary algorithms in unbounded integer spaces are scarce at present. In order to advance this research field we consider two versions of the (1+1)- EA and analyze their runtime ... 详细信息

ISBN: (纸本)9781665430654

关键词： multiobjective evolutionary algorithm runtime analysis unbounded integer space

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Parallelism on Multicore Processors using ***

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1st International Conference on Parallel, Distributed and Grid Computing for Engineering

作者： Marquez, A. L. Gil, C. Banos, R. Gomez, J. Department of Computer Architecture and Electronics University of Almeria Spain Department of Languages and Computation University of Almeria Spain

ISBN: (纸本)9781905088294

The *** Task Parallel Library is the latest tool developed for multicore parallelism optimization using the NET technology. It is a managed concurrency library that provides optimized managed code for multicore processors using a new thread pool that supports cancellation, waiting and pool isolation, among many other features. The Task Parallel Library also uses dynamic work stealing techniques for superior scalability. In this paper we analyze the performance improvement in a timetable planner implemented with a multiobjective evolutionary algorithm using the Task Parallel Library of ***.

关键词： multicore task parallel library C# parallel.FX multiobjective evolutionary algorithm timetabling

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Deep Reinforcement Learning with Fuzzy Inference System for Prediction of Crop-sowing Windows 2

Deep Reinforcement Learning with Fuzzy Inference System for ...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Balakrishnan, D. Mariappan, Umasree Chowdary, Pokuri Manoj Krishna, Muvva Hari Kumar, Maddu Venkata Naga Jeswanth Abhishekh, P. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnankoil626126 India Sri Vidya College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Virudhunagar626005 India

ISBN: (纸本)9798350308846

Advanced farming techniques leverage Internet of Thing (IoT)-compatible crop monitoring to collect real-time data on crop conditions, enabling precision agriculture by optimizing resource usage and enhancing decision-making for increased crop yield and sustainability. An IoT monitoring system was instrumental in analyzing data on the correlation between crop yield, weather parameters, and insect reproduction, revealing the environmental factors conducive to increased pest breeding conditions. An Optimized Fuzzy Inference System (OFIS) was proposed for IoT-compatible crop monitoring to identify the crop-sowing window. In the beginning, a wireless sensor network was set up in the field to collect meteorological information, which was then evaluated using a Fuzzy Inference System (FIS). Within this analysis, optimization of linguistic variables was achieved through the utilization of a Multi-Objective evolutionary algorithm (MOEA). However, MOEA computationally expensive and require more manual parameter tuning to fine tune the linguistic variables in FIS. So in this article, Deep Reinforcement Learning (DRL) is introduced which adapt and learn complex linguistic variable optimizations with minimal manual parameter tuning, making it more versatile and potentially efficient for FIS optimization. DRL, a sub domain within machine learning, integrates Deep Neural Networks (DNN) with reinforcement learning techniques. Within the realm of DRL, an agent engages with its surroundings, makes decisions, gains rewards, and gradually refines its actions for optimization. The experimental results prove that the proposed DRL-FIS has high accuracy, True Positive (TP) rate and low False Positive (FP) rate than OFIS for identification of cropsowing window. © 2023 IEEE.

关键词： advanced farming deep reinforcement learning fuzzy inference system multiobjective evolutionary algorithm

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Identification of Supersonic Inlet Unstarted State and Optimal Sensor Configuration Based on EI-SPEA2

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JOURNAL OF AEROSPACE ENGINEERING 2023年第2期36卷 04022131-04022131页

作者： Zhao, Yong-Ping Yang, Tian-Lin Tan, Hui-Jun Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn 29 Yudao St Nanjing 210016 Peoples R China

As the key starting component of supersonic flight, identification of the unstarted state of the supersonic inlet is the premise of restarting the inlet and an important link of engine protection control. In order to explore the characteristics of the dynamic pressure inside the inlet, the selection of a pressure sensor configuration scheme, that is, how to obtain more abundant pressure information with fewer sensors, is also an urgent problem to be solved. This paper combines time-frequency analysis and deep learning for flow pattern recognition of a supersonic inlet and proposes a strength-based Pareto evolutionary algorithm combined with improved extinction and migration strategies (EI-SPEA2). The signals of all sensors were preliminarily processed using continuous wavelet transform, and the dynamic signals of all sensors were transformed into two-dimensional (2D) time-frequency spectrograms, which were then used as inputs by convolutional neural network to determine the inlet flow patterns from the dynamic sensor signals. A multiobjective optimization algorithm, EI-SPEA2, was adopted, which takes the minimum classification error, minimum number of selected sensors, and optimal sensor distribution as the optimization objectives. Through the continuous iteration of the population, identification of the unstarted state and optimal configuration of sensors was carried out simultaneously. By introducing the improved EI strategy at the end of iteration, SPEA2 can get rid of the local optimum and achieve convergence faster. A distance index for measuring sensor distribution and two indexes for measuring sensor information differentiation are proposed to improve sensor configuration scheme. The experimental results demonstrated that compared with competitive methods, EI-SPEA2 has better performance on both dual-objective optimization problems and three-objective optimization problems. Moreover, three indexes proposed to measure the distribution of the sensor are benefic

关键词： Identification of unstarted state Time-frequency analysis Convolutional neural network multiobjective evolutionary algorithm Optimal sensor configuration

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Ensemble multi-objective optimization approach for heterogeneous drone delivery problem

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartA期249卷

作者： Wen, Xupeng Wu, Guohua Li, Shuanglin Wang, Ling Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Recently, driven by advancements in the payload capacity and endurance of drones, the logistics industry has shown significant interest in drone Last -Mile logistics. Efficient routing are crucial scientific challenges in drone delivery problems. In this study, we address the routing problem in heterogeneous drone delivery, which involves a large drone transporting multiple small drones to sub -regions for parcel delivery, aiming to both reduce the drones' distance costs and improve customer satisfaction, termed HDDPBO To tackle the HDDPBO problem effectively, we propose a voting -based ensemble multi -objective genetic approach, named VEMOGA, in which an improved clustering algorithm is developed to divide customers into K clusters, enabling each drone to handle multiple parcel deliveries within a sub -region. In this way, it reduces the complexity of HDDPBO by transforming it into multiple sub -problems. Secondly, a multi -objective genetic approach with heuristic operators is proposed to explore high -quality solutions, in which customized crossover and mutation operators are designed in the genetic approach, and a voting -based ensemble algorithm is designed to robustly select the Pareto frontier with high -quality convergence and diversity. Extensive experiments are conducted on synthetic instances to evaluate the proposed algorithm, and the experimental results demonstrate superior performance compared to three other baselines. Additionally, a real -world instance has been scrutinized to ascertain the applicability of Last -Mile logistics, and sensitivity analyses of pivotal factors have been conducted and several managerial insights pertinent are given to the drone -based Last -Mile logistics.

关键词： multiobjective evolutionary algorithm Ensemble algorithms Heterogeneous multi-drone Routing

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Design Methodology of a Hybrid Rocket-Powered Launch Vehicle for Suborbital Flight

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JOURNAL OF AEROSPACE ENGINEERING 2017年第6期30卷 04017071-04017071页

作者： Kanazaki, Masahiro Yoda, Hideyuki Chiba, Kazuhisa Kitagawa, Koki Shimada, Toru Tokyo Metropolitan Univ Grad Sch Syst Design Div Aerosp & Engn 6-6 Asahigaoka Hino Tokyo 1920065 Japan Univ Electrocommun Grad Sch Informat & Engn Chofu Tokyo 1828585 Japan Japan Aerosp Explorat Agcy Inst Space & Astronaut Sci Chuo Ku 3-1-1 Yoshinodai Sagamihara Kanagawa 2525210 Japan

A multidisciplinary design methodology for a launch vehicle (LV) with a hybrid rocket was proposed in order to obtain qualitative design knowledge. In this study, a propulsion system performance and aerodynamic performance were empirically evaluated using a theoretical approach, and flight evaluation was performed by solving equations of motion to evaluate the motion along the horizontal/vertical directions and rotation around the body axis. To demonstrate the applicability of the proposed method, two types of multiobjective design problems were solved using multiobjective evolutionary algorithms, and the results were visualized using the parallel coordinate plot, which is a data mining technique. The first design problem has two objective functions: the maximization of the highest altitude and the minimization of the total mass. In this case, the effect of the constraint related to the body length and diameter ratio was evaluated. The second design problem also had two objective functions and two constraints for a realistic LV design: the maximization of the downrange and the minimization of the total mass. The target altitude was different for these problems although the objective functions were identical. As a result, the trade-off information was successfully acquired for each design problem. It was also found that the body length and diameter ratio were key factors in deciding the maximum altitude. Furthermore, during the downrange maximization, the characteristic geometries observed in the obtained nondominated solutions revealed the design required to fulfill the constraint related to the target altitude. (C) 2017 American Society of Civil Engineers.

关键词： Hybrid rocket Design optimization multiobjective evolutionary algorithm

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GADPO: Genetic algorithm based on Dominance for Primer Optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartD期238卷

作者： Rodriguez-Bejarano, Fernando M. Vega-Rodriguez, Miguel A. Santander-Jimenez, Sergio Univ Extremadura Escuela Politecn Campus Univ S-N Caceres 10003 Spain

The amplification of the 16S ribosomal RNA gene through the polymerase chain reaction (PCR) is the main approach to profile bacterial communities. This gene is a widely used marker with a composition that allows the identification of microorganisms at the genus or species levels. The correct performance of a PCR assay depends on the properties of the chosen set of primers that match with the target 16S sequences, allowing the amplification by a DNA polymerase. For this reason, optimizing the design of primers attending to such multiple properties is crucial to ensure the specificity and robustness of this process. However, only one multiobjective proposal exists that addresses the optimization of primer design targeted to the 16S gene amplification. Herein, we propose a novel approach for multiobjective primer optimization based on a mutation-based genetic algorithm with three new problem-aware mutation operators, each aimed at covering one of the three objectives that compose the problem (efficiency, coverage, and variability). The proposed algorithm has been tested on 5 real datasets of bacterial 16S gene sequences. The results have been evaluated using 4 quality metrics, showing that our approach achieves statistically significant improvements with regard to the reference multiobjective approach in the field.

关键词： multiobjective evolutionary algorithm Problem-aware mutations Genetic algorithm based on dominance Bioinformatics PCR primer optimization

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