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Optimal Route Search Based on multi-objective genetic algorithm for Maritime Navigation Vessels

Optimal Route Search Based on Multi-objective Genetic Algori...

Human Interface and the Management of Information (HIMI) part of International Conference on Human-Computer Interaction (HCII)

作者： Saga, Ryosuke Liang, Zhipeng Hara, Naoyuki Nihei, Yasunori Osaka Prefecture Univ 1-1 Gakuen Cho Sakai Osaka 5998531 Japan

ISBN: (纸本)9783030500160;9783030500177

Ocean research requires regular collection of ocean data, wherein an autonomous robotic ship is usually used. However, in contrast to collecting land-based data, collecting sea level data face the following problems. First, robot ships are affected by sea surface winds, waves, and tides, with constantly changing strength and direction. Second, hull collisions must be prevented when multiple ships are working simultaneously. Third, given the limitation of the electric power of the autonomous sailing ship, the electric power consumption of the robot ship must be considered when collecting over a wide sea. Fourth, fixed obstacles, such as an island on the sea surface, must be avoided. Given such issues, no effective navigation route search system is currently available. In this work, a navigation route system for complex situations on the sea surface was designed on the basis of the actual situation. Clustering method was used to classify collection points according to distance based on the number of robot ships, and a multi-objective genetic algorithm was used to determine the optimal path for each classification.

关键词： Ocean research multi-objective genetic algorithm Optimal route search

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Design of a Data-Driven Control System using a multi-objective genetic algorithm 13th

Design of a Data-Driven Control System using a Multi-Objecti...

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13th International-Federation-of-Automatic-Control (IFAC) Workshop on Adaptive and Learning Control Systems (ALCOS)

作者： Kinoshita, Takuya Yamamoto, Toru Hiroshima Univ 1-4-1 Kagamiyama Higashihiroshima Hiroshima Japan

In recent years, control design schemes for directly calculating control parameters from operational data have been realized and include the virtual reference feedback tuning (VRFT) method and the fictitious reference iterative tuning (FRIT) method. They were designed for objects that have a linear system. However, many objects in industry are nonlinear;hence, it is challenging to obtain good control performance by only applying fixed PID controllers. In this study, multiple linear systems as objects using multiple linear controllers are investigated. Specifically, it is necessary to solve two optimization problems of (i) the number of controllers (ii) the control parameters of each controller, and it is solving by using multi-objective genetic algorithm (MOGA) in this research. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： multi-objective genetic algorithm data-driven control VRFT

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Trading Strategy Optimisation with a multi-objective genetic algorithm 11

Trading Strategy Optimisation with a Multi-objective Genetic...

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11th International Conference on Information Technology - IoT and Smart City (ITIoTSC)

作者： Liu, Yu Univ Western Australia Dept Comp Sci & Software Engn Perth WA 6009 Australia

ISBN: (纸本)9798350328356;9798350328349

algorithmic trading, a significant area of research in artificial intelligence, faces the challenges of interpreting and processing extensive and dynamic data. This environment is ideal for implementing adaptive algorithms, such as the evolutionary algorithm NSGA-II used in this paper, notable for its effectiveness in multi-objective optimisation problems. We apply this algorithm, in conjunction with technical indicators, to explore and exploit the search space provided by BTC/AUD market data for a specific trading strategy. Our objective is to demonstrate how NSGA-II, with an optimal parameter configuration, can balance net profit and risk to optimise trading outcomes, as evidenced in the Pareto-front results. Our findings show that by optimizing the parameter values, our NSGA-II driven strategy significantly outperformed a buy-and-hold strategy over the same period.

关键词： BTC/AUD Trading multi-objective genetic algorithm Optimization Risk and Return Evaluation

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Optimization of intermittent oil recovery mechanism based on multi-objective genetic algorithm 35

Optimization of intermittent oil recovery mechanism based on...

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35th Chinese Control and Decision Conference (CCDC)

作者： Wu, Jingsong Jiang, Mingzheng Dong, Kangxing Yang, Hukun Northeast Petroleum Univ Sch Mech Sci & Engn Daqing Peoples R China

ISBN: (纸本)9798350334722

With the continuous exploitation of oil fields, the problem of long-term inefficient operation of some pumping units is common in major oil fields in China. The intermittent oil recovery mechanism can effectively avoid the wear and tear of empty pumping while reducing electrical energy consumption. The Pareto multi-objective genetic algorithm is used to optimize the optimal downtime of the pumping units from the perspective of energy saving in the oil recovery system, to maximize the efficiency of oil recovery while minimizing power consumption. A comparison of the experimental results showed that the intermittent oil recovery mechanism was optimized to save 21.45% of energy consumption and improve the system efficiency by 38.85%. This method solves the problems of empty pumping and inefficiency of pumping units and achieves the purpose of reducing the mechanical wear and tear of oil recovery machines, saving electrical energy, and improving the overall development benefit of the oil field.

关键词： pumping units intermittent pumping energy saving multi-objective genetic algorithm oil extraction efficiency

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Cognitive Radio Decision Engine Based on multi-objective genetic algorithm

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3rd International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2011)

作者： Wu Di Yang Shengyao Liu, J. C. Dalian Univ Technol Sch Comp Sci Dalian Peoples R China Simon Fraser Univ Sch Comp Sci Vancouver BC Canada

ISBN: (纸本)9783037850190

The performance optimization of cognitive radio is a multi-objective optimization problem. Existing genetic algorithms are difficult to assign the weight of each objective when the linear weighting method is used to simplify the multi-objective optimization problem into a single objective optimization problem. In this paper, we propose a new cognitive decision engine algorithm using multi-objective genetic algorithm with population adaptation. A multicarrier system is used for simulation analysis, and experimental results show that the proposed algorithm is effective and meets the real-time requirement.

关键词： Cognitive radio multi-objective genetic algorithm Decision engine

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A Cluster-Based Orthogonal multi-objective genetic algorithm

A Cluster-Based Orthogonal Multi-Objective Genetic Algorithm

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4th International Symposium on Intelligence Computation and Applications

作者： Zhu, Jiankai Dai, Guangming Mo, Li China Univ Geosci Wuhan 430074 Peoples R China

ISBN: (纸本)9783642049613

multi-objective genetic algorithm is proved to be suitable for solving multi-objective optimization problems. However, it is usually very hard to balance the convergence and diversity of a multi-objective genetic algorithm. This paper introduces a new algorithm, with both good convergence and diversity based on clustering method and multi-parent crossover operator. Meanwhile, an initial population is generated by orthogonal design to enhance the search effort of the algorithm. The experimental results on a number of test problems indicate the good performance of the Cluster-Based Orthogonal multi-objective genetic algorithm.

关键词： multi-objective Optimization Problem multi-objective genetic algorithm Orthogonal design Cluster multi-parent Crossover

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Scheduling of a flexible job-shop using a multi-objective genetic algorithm

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JOURNAL OF ADVANCES IN MANAGEMENT RESEARCH 2012年第2期9卷 178-188页

作者： Agrawal, Rajeev Pattanaik, L. N. Kumar, S. Birla Inst Technol Dept Prod Engn Mesra India GLA Univ Dept Mech Engn Mathura India

Purpose - The purpose of this paper is to solve a flexible job shop scheduling problem where alternate machines are available to process the same job. The study considers the Flexible Job Shop Problem (FJSP) having n jobs and more than three machines for scheduling. Design/methodology/approach - FJSP for n jobs and more than three machines is non polynomial (NP) hard in nature and hence a multi-objective genetic algorithm (GA) based approach is presented for solving the scheduling problem. The two objective functions formulated are minimizations of the make-span time and total machining time. The algorithm uses a unique method of generating initial populations and application of genetic operators. Findings - The application of GA to the multi- objective scheduling problem has given optimum solutions for allocation of jobs to the machines to achieve nearly equal utilisation of machine resources. Further, the make span as well as total machining time is also minimized. Research limitations/implications - The model can be extended to include more machines and constraints such as machine breakdown, inspection etc., to make it more realistic. Originality/value - The paper presents a successful implementation of a meta-heuristic approach to solve a NP-hard problem of FJSP scheduling and can be useful to researchers and practitioners in the domain of production planning.

关键词： Programming and algorithm theory Production scheduling Job sequence loading Flexible job shop problem Make-span Scheduling multi-objective genetic algorithm

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A Three-Stage Surrogate Model Assisted multi-objective genetic algorithm for Computationally Expensive Problems

A Three-Stage Surrogate Model Assisted Multi-Objective Genet...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Jiang, Puyu Zhou, Qi Liu, Jun Cheng, Yuansheng Huazhong Univ Sci & Technol Sch Naval Architecture & Ocean Engn Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Sch Aerosp Engn Wuhan Hubei Peoples R China

ISBN: (纸本)9781728121536

multi-objective optimization problems (MOPs) are commonly encountered in practical engineering. multi-objective evolutionary algorithms (MOEAs) are one of the powerful methods to solve MOPs. However, MOEAs require a large number of fitness evaluations, which limits the practical application of MOEAs. Surrogate model assisted evolutionary algorithm (SAEA) can effectively alleviate the computation burden of MOEAs by replacing time-consuming simulation with the surrogate model. In this paper, a three-stage adaptive multi-fidelity surrogate (MFS) model assisted multi-objective genetic algorithm(MOGA) are proposed. In the first stage, a cheap low-fidelity (LF) model is adopted to obtain a preliminary Pareto frontier (PF). In the second stage, some of the individuals are selected and sent to high-fidelity (HF) model to construct MFS models, which are used to evaluate the fitness functions and sequentially updated according to the model management strategy. During this stage, in order to obtain a better PF, a fidelity control strategy is developed to subjectively determine when transforming is conducted to the third stage, in which all the individuals are evaluated by the HF model. Three benchmark tests are used to test the performance of the proposed method. Results show that the proposed method performs better than online MFS model assisted MOGA( OLMFM-MOGA) and NSGA-II with HF model, especially when the correlation between the LF and HF models is very poor.

关键词： multi-fidelity surrogate model Fidelity control strategy Surrogate model assisted evolutionary algorithm Model management multi-objective genetic algorithm

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Performance Optimization of a STATCOM based on Cascaded multi-Level Converter Topology Using multi-objective genetic algorithm 23

Performance Optimization of a STATCOM based on Cascaded Mult...

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23rd Iranian Conference on Electrical Engineering

作者： Tousi, S. M. Reza Aznavi, Sima Bu Ali Sina Univ Fac Engn Dept Elect Engn Hamadan Iran

ISBN: (纸本)9781479919710

In this paper, a comprehensive study in component sizing of a cascaded multi-level inverter based static synchronous compensator (STATCOM) is presented. multi-objective genetic algorithm (MOGA) is utilized to optimize the performance of STATCOM. "Equivalent dynamic capacitance" concept is considered in order to simplify the complex structure of the multi-level inverter based STATCOM. Cascaded multi-level inverter parameters such as DC capacitance, initial capacitor charge, switching angles and also power system parameters including buffer inductance, transformer ratio and transformer inductance are to be found using optimization algorithm. In contrast with previous researches which have tried to optimize the objectives independently, in this paper the aim of using MOGA is optimizing losses, total harmonic distortion (THD) and maximum DC voltage ripple simultaneously, providing designing constraints with respect to the "equivalent dynamic capacitance" concept. The results show that better performance of STATCOM can be achieved via a thorough analysis by considering all parameters of STATCOM and components of power system as decision variables of a multi-objective optimization problem.

关键词： Static Synchronous Compensator (STATCOM) Cascaded multi-Level Converter Performance Optimization multi-objective genetic algorithm Equivalent Dynamic Capacitance

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Motor velocity based multi-objective genetic algorithm controlled navigation method for two-wheeled pioneer P3-DX robot in V-REP scenario

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International Journal of Information Technology (Singapore) 2021年第5期13卷 2101-2108页

作者： Panwar, Vikas Singh Pandey, Anish Hasan, Md. E. School of Mechanical Engineering KIIT Deemed To Be University Patia Bhubaneswar PIN - 751024 India

This paper proposes the right and left motor velocity based multi-objective genetic algorithm controlled navigation method for Two-Wheeled Pioneer P3-DX Robot (TWPR) in Virtual Robot Experimentation Platform (V-REP) software scenarios. The first objective function is made by taking front, left, and right ultrasonic sensors data and right motor velocity. Similarly, the second objective function is designed using front, left, and right ultrasonic sensors data and left motor velocity. Next, the sensor data are considered independent variables or inputs. The velocities of the motors are chosen as dependent variables or outputs for making multi-objective fitness functions for genetic algorithm (GA). This multi-objective GA makes a sensor-actuator control architecture and helps the TWPR to avoid the obstacles in the simulated scenarios. Further, the successful simulation test results show that the multi-objective GA provided a collision-free smooth, and shortest path for TWPR compared to the previously developed benchmark single objective GA. © 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： multi-objective genetic algorithm Navigation method Sensor Single objective genetic algorithm Two-wheeled pioneer P3-DX robot Virtual robot experimentation platform

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