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2010 IEEE World Congress on Computational Intelligence

作者： Miranda, Gara de Armas, Jesica Segura, Carlos Leon, Coromoto Univ La Laguna Dpto Estadist IO & Computac San Cristobal la Laguna 38271 Santa Cruz Ten Spain

ISBN: (纸本)9781424481262

Most research on Strip Packing Problems is focused on the single-objective formulation of the problem. However, in this work we deal with a more general and practical variant of the problem, which not only seeks to optimize the usage of the raw material, but also the production process. For the problem solution, we have applied some of the most-known multi-objective evolutionary algorithms, since they have shown a promising behavior when affording multi-objective real-world problems. For an initial implementation, we proposed a solution codification which is based on a complete representation of the pattern layouts. Such an approach was promising but wasn't suitable to afford large instances. For this reason, we have focused on the design of a codification which can be much more competitive when compared to some tailor-made methods. In this sense, we present a hyperheuristic-based codification as an alternative to combine heuristics in such a way that a heuristic's strengths make up for the drawbacks of another. Results demonstrate the advantage of using multi-objective approaches, hyperheuristic-based representations, and of course, the importance on the selection of appropriate solution codifications.

关键词： bin packing codification design cutting evolutionary computation hyperheuristic codification multiobjective 2D guillotine strip packing problem multiobjective evolutionary algorithm multiobjective real-world problem pattern layout production process raw material raw materials

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A Fast Method of Constructing the Non-Dominated Set: Arena's Principle

A Fast Method of Constructing the Non-Dominated Set: Arena's...

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4th International Conference on Natural Computation (ICNC 2008)

作者： Tang, Suqin Cai, Zixing Zheng, Jinhua Cent South Univ Sch Informat Sci & Engn Changsha 410083 Peoples R China Guangxi Normal Univ Sch Comp Sci & Informat Engn Guiyang 541004 Peoples R China Xiangtan Univ Inst Informat Technol Xiangtan 411105 Peoples R China

ISBN: (纸本)9780769533049

The objectives in multiobjective optimization problem (MOP) conflict with each others in usually, and no single solution can optimize all the objectives at the same time. An important task in multiobjective optimization is to identify the set of Pareto-optimal solutions. The Pareto front is the set of all the optimal tradeoffs in the objective space. Constructing a non-dominated set is an important process in the multiobjective evolutionary algorithm (MOEAs). In this paper we introduce a fast method for constructing the non-dominated set, called the arena's principle (AP). The method with O(m (n) over barm) computational complexity is presented Experimental results have demonstrated that the computational complexity of the arena's principle is better than the fast non-dominated sorting approach in NSGA-II

关键词： multiobjective evolutionary algorithm Pareto-Optimal front non-dominated set

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Circuit-Simulation-Based Multi-objective evolutionary algorithm with Multi-Level Clock Driving Technique for a-Si:H TFTs Gate Driver Circuit Design Optimization

Circuit-Simulation-Based Multi-objective Evolutionary Algori...

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Workshop on Computer Architecture, Embedded Systems, SoC, and VLSI/EDA / International Computer Symposium (ICS)

作者： Hung, Sheng-Chin Chen, Chieh-Yang Chiang, Chien-Hsueh Li, Yiming Natl Chiao Tung Univ Parallel & Sci Comp Lab 1001 Ta Hsueh Rd Hsinchu 300 Taiwan Natl Chiao Tung Univ Inst Biomed Engn Hsinchu 300 Taiwan Natl Chiao Tung Univ Inst Commun Engn Hsinchu 300 Taiwan Natl Chiao Tung Univ Dept Elect & Comp Engn Hsinchu 300 Taiwan

ISBN: (纸本)9781614994848;9781614994831

In this work, dynamic characteristic of a new amorphous silicon gate (ASG) driver circuit is optimized by using multi-objective evolutionary algorithm (MOEA) and hydrogenated amorphous silicon (a-Si:H) TFT circuit simulator on the unified optimization framework (UOF). The ASG driver circuit consisting of 14 a-Si:H TFTs is optimized for the given specifications of the fall time < 3 mu s and the ripple voltage < -9 V with simultaneously minimizing the total layout area. More than 50% reductions on the falling time of the ASG driver circuit have been achieved by using the proposed optimization methodology together with a novel 3-level clock driving technique.

关键词： a-Si:H TFTs amorphous silicon gate driver circuits display panel design optimization multiobjective evolutionary algorithm unified optimization framework

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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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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

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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Preference-based Pareto optimization in certain and noisy environments

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ENGINEERING OPTIMIZATION 2009年第1期41卷 23-38页

作者： Trautmann, Heike Mehnen, Joern TU Dortmund Univ Stat Fac D-44221 Dortmund Germany Cranfield Univ Decis Engn Ctr Cranfield MK43 0AL Beds England

In this article a method for including a priori preferences of decision makers into multicriteria optimization problems is presented. A set of Pareto-optimal solutions is determined via desirability functions of the objectives which reveal experts' preferences regarding different objective regions. An application to noisy objective functions is not straightforward but very relevant for practical applications. Two approaches are introduced in order to handle the respective uncertainties by means of the proposed preference-based Pareto optimization. By applying the methods to the original and uncertain Binh problem and a noisy single cut turning cost optimization problem, these approaches prove to be very effective in focusing on different parts of the Pareto front of the ori-ginal problem in both certain and noisy environments.

关键词： multiobjective evolutionary optimization multiobjective evolutionary algorithm noisy environment desirability function single cut turning problem

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GADPO: Genetic algorithm based on Dominance for Primer Optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 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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Use of Domain Knowledge to Increase the Convergence Rate of evolutionary algorithms for Optimizing the Cost and Resilience of Water Distribution Systems

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2016年第9期142卷

作者： Bi, Weiwei Dandy, Graeme C. Maier, Holger R. Univ Adelaide Sch Civil Environm & Min Adelaide SA 5005 Australia Univ Adelaide Environm & Min Engn Adelaide SA 5005 Australia

evolutionary algorithms (EAs) have been used extensively for the optimization of water distribution systems (WDSs) over the last two decades. However, computational efficiency can be a problem, especially when EAs are applied to complex problems that have multiple competing objectives. In order to address this issue, there has been a move toward developing EAs that identify near-optimal solutions within acceptable computational budgets, rather than necessarily identifying globally optimal solutions. This paper contributes to this work by developing and testing a method for identifying high-quality initial populations for multiobjective EAs (MOEAs) for WDS design problems aimed at minimizing cost and maximizing network resilience. This is achieved by considering the relationship between pipe size and distance to the source(s) of water, as well as the relationship between flow velocities and network resilience. The benefit of using the proposed approach compared with randomly generating initial populations in relation to finding near-optimal solutions more efficiently is tested on five WDS optimization case studies of varying complexity with two different MOEAs. The results indicate that there are considerable benefits in using the proposed initialization method in terms of being able to identify near-optimal solutions more quickly. These benefits are independent of MOEA type and are more pronounced for larger problems and smaller computational budgets.

关键词： multiobjective evolutionary algorithm Optimization Initialization method Water distribution system Near-optimal fronts

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Ensemble multi-objective optimization approach for heterogeneous drone delivery problem

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 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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