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multi-objective optimal operation of integrated thermal-natural gas-electrical energy distribution systems

APPLIED THERMAL ENGINEERING 2020年 181卷

作者： Asl, Dariush Keihan Seifi, Ali Reza Rastegar, Mohammad Mohammadi, Mohammad Shiraz Univ Sch Elect & Comp Engn Shiraz Iran

This paper proposes a multi-objective optimal energy flow problem in the integrated energy distribution systems including electrical, thermal, and natural gas distribution networks. From the mathematical perspective, the electrical network is considered unbalanced and thermal units such as combined heat and power plants and boilers are modeled by their part-load performance. The objectives are minimizing the total operational energy cost of supplying electrical and thermal demands, electrical losses, and unbalances in the power flow in three phases of the grid. A modified teaching-learning based optimization algorithm is applied to solve the optimization problem, considering all constraints of the integrated energy distribution systems. The Pareto optimal front is employed to show the results. The best compromise solution is selected from the Pareto optimal solutions using a fuzzy decision making-based mechanism. The performance of obtained solutions is validated in a typical integrated energy distribution system. In this regard, a practical 19-bus unbalanced electrical network, a practical thermal network with 30-node, and a typical 20-node natural gas network are combined together. The results demonstrate the effectiveness of the proposed multi-objective methodology in finding better operating points for integrated energy distribution systems in comparison with single- and bi-objective problems. In addition, the impacts of the deployment of energy storage systems on the operation of the energy systems are studied in the designated cases.

关键词： Integrated energy distribution system multi-objective problem Unbalanced distribution system Pareto optimal front Energy storage system

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Inverse design of an indoor environment using a filter-based topology method with experimental verification

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INDOOR AIR 2020年第5期30卷 1039-1051页

作者： Zhao, Xingwang Shi, Zhu Chen, Qingyan Tianjin Univ Sch Environm Sci & Engn Tianjin Key Lab Indoor Air Environm Qual Control Tianjin Peoples R China Purdue Univ Sch Mech Engn W Lafayette IN 47907 USA

In order to create a healthy, comfortable, productive, and energy-efficient indoor environment, the computational fluid dynamics (CFD)-based adjoint method with an area-constrained topology method can be used to inversely design the optimal number, size, location, and shape of air supply inlets and air supply parameters. However, this method is not very mature, and the distribution of retained inlets is always scattered. To solve that problem, this investigation introduced a filter method that smooths the intermediate results during the inverse design process. Using a three-dimensional, non-isothermal, asymmetrical office with pre-set air supply inlets as an example, this study verified the performance of the proposed filter-based topology method. The verified method was then used to solve a multi-objective design problem and design an optimal indoor environment for a room. The results indicate that the proposed method was able to find the optimal number, location, and shape of air supply inlets and the optimal air supply temperature, velocity, and angle that led to a thermally comfortable, healthy, productive, and energy-efficient indoor environment. Finally, this investigation installed the optimal inlets in an environmental chamber to mimic the room. The measured air temperature, velocity, and mean age of air in several typical locations in the environmental chamber matched the CFD simulation results very closely.

关键词： CFD-based adjoint method filter-based topology method Indoor environment inverse design multi-objective problem

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RFID Network Planning Optimization Using a Genetic-Simulated Annealing Combined Algorithm

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China Communications 2023年第8期20卷 234-253页

作者： Ali Sanagooy Aghdam Abbas Toloie Eshlaghy Mohammad Ali Afshar Kazemi Amir Danehsvar Department of Information Technology Management Central Tehran BranchIslamic Azad UniversityTehranIran Department of Industrial Management Science and Research BranchIslamic Azad UniversityTehranIran Department of Industrial Management Central Tehran BranchIslamic Azad UniversityTehranIran

The main purpose of this paper is to present and apply a genetic and simulated annealing combined algorithm to solve an optimization problem of Radio Frequency Identification(RFID)network planning in an emergency department of a ***,though genetic algorithm(GA)and simulated annealing(SA)have advantages and disadvantages,but they are also ***,the combined algorithm not only takes advantages of the two methods,but also avoids their *** simulation results in an emergency department of a hospital present that the proposed method provides minimum total cost and maximum RFID network coverage in a simultaneous way with the efficient use of multi-antenna RFID ***,the results of comparison of two scenarios of the model with the results of other existing models in the relevant literature show that the proposed model has better outcomes.

关键词： multi-objective problem multi-antenna readers healthcare simulation

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multi-objective Hole-Making Sequence Optimization by Genetic Algorithm Based on Q-Learning

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第6期8卷 3793-3806页

作者： Zhang, Desong Chen, Yanjie Zhu, Guangyu Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350116 Peoples R China

As one of the most fundamental operations in mechanical production, hole-making plays a crucial role. However, existing hole-making sequence optimization models are not suitable for workshops with variable production parameters. To address this issue, a new model, named multi-objective multi-tool hole-making sequence optimization with precedence constraints (MO-MTpcHSO), is proposed in this paper. The model has two objectives: spindle travel distance and tool switching time. To solve MO-MTpcHSO, a customized Q-learning based genetic algorithm (QLGA) is proposed. The adaptive encoding method allows chromosomes to express feasible solutions, the population is considered as the agent, and the states are intervals of the diversity coefficient. Different insertion methods in the crossover operator are set as actions, and the reward is related to the diversity and values of objective functions of the population. The effectiveness of QLGA is validated by comparing it with other algorithms in practical workpieces. Moreover, the reasonability of actions and the necessity of the Q-learning framework in QLGA are validated.

关键词： Q-learning genetic algorithm multi-objective problem hole-making sequence optimization

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Design of Digital IIR Filter with Conflicting objectives Using Hybrid Predator-Prey Optimization

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2018年第5期37卷 2117-2141页

作者： Sidhu, D. S. Dhillon, J. S. Govt Polytech Coll Bathinda 151001 India St Longowal Inst Engn & Technol Dept Elect & Instrumentat Engn Longowal 148106 India

The design of digital IIR filter as a single-objective optimization problem using evolutionary algorithms has gained much attention in the previous years. In this paper, the design of filter is treated as a multi-objective problem by simultaneously minimizing the magnitude response error, linear phase response error and optimal order within the stability constraints. The global search technique, predator-prey optimization (PPO), has been applied to design the digital IIR filter. The global search technique has been hybridized with binary successive approximation (BSA)-based evolutionary search method for exploring the search space locally. The relative performance of PPO and hybrid PPO has been evaluated by applying these techniques to standard mathematical test functions. The above-proposed hybrid search technique has been applied to achieve the solution for multi-parameter and multi-objective optimization problem of low-pass (LP), high-pass (HP), band-pass (BP) and band-stop (BS) digital IIR filter design. The results obtained from the proposed technique are compared with the results of other algorithms applied by other researchers for the design of digital IIR filter.

关键词： Predator-prey optimization (PPO) Binary successive approximation (BSA) Digital IIR filter multi-objective problem

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Ship hull-propeller system optimization based on the multi-objective evolutionary algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2017年第1期231卷 175-192页

作者： Ghassemi, Hassan Zakerdoost, Hassan Amirkabir Univ Technol Dept Maritime Engn Hafez Ave Tehran 158754413 Iran

The optimization of the hull-propeller system of a ship has always been one of the most important aspects of design in order to reduce the costs, mechanical losses and increase the life of system components. The proposed design methodology represents a comprehensive approach to optimize the hull-propeller system simultaneously. In this study, two objective functions are considered, i. e. lifetime fuel consumption (LFC) and lifetime cost function (Cost). The mission profile of the vessel is adopted to minimize the LFC and Cost over their operational life. The well-known evolutionary algorithm based on NSGA-II is employed to handle the multi-objective problems, where the main propeller and hull coefficients are the unknown and are considered as design variables. The results are presented for a commercial container ship driven by B-series propeller. Three different engines with the same mission profile were taken and the results revealed that the proposed method is an appropriate and effective approach for finding Pareto optimal solutions distributed uniformly and is able to improve both of the objective functions significantly and other performances of the system.

关键词： multi-objective problem hull-propeller system lifetime fuel consumption NSGA-II

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Two Geoscience Applications by Optimal Neural Network Architecture

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PURE AND APPLIED GEOPHYSICS 2020年第6期177卷 2663-2683页

作者： Anochi, Juliana Aparecida Hernandez Torres, Reynier de Campos Velho, Haroldo Fraga Natl Inst Space Res INPE Ctr Weather Forecasting & Climate Studies CPTEC Rodovia Presidente DutraKm 39 BR-12630000 Cachoeira Paulista SP Brazil Natl Inst Space Res INPE Associated Lab Comp & Appl Math LABAC Ave Astronautas 1758 BR-12227010 Sao Jose Dos Campos SP Brazil

Nowadays, artificial neural networks have been successfully applied on several research and application fields. An appropriate configuration for a neural network is a complex task, and it often requires the knowledge of an expert on the application. A technique for automatic configuration for a neural network is formulated as an optimization problem. Two strategies are considered: a mono-objective minimization problem, using multi-particle collision algorithm (MPCA);and a multi-objective minimization problem addressed by the non-dominated sorting genetic algorithm (NSGA-II). The proposed optimization approaches were tested for two application in geosciences: data assimilation for wave evolution equation, and the mesoscale seasonal climate prediction for precipitation. Better results with automatic configuration were obtained for data assimilation than those obtained by network defined by an expert. For climate seasonal precipitation, automatic configuration presented better predictions were presented than ones carried out by an expert. For the worked examples, the NSGA-II presented a superior result for the worked experiments.

关键词： Metaheuristics optimization problem neural network data assimilation climate precipitation prediction mono-objective problem multi-objective problem

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Parameter optimization of electromagnetic suspension-type maglev train control system based on multi-objective grey wolf non-dominated sorting hybrid algorithm-II hybrid algorithm

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2024年第2期43卷 956-978页

作者： Wang, Meiqi Zeng, Siheng Liu, Pengfei He, Yixin Chen, Enli Shijiazhuang Tiedao Univ State Key Lab Mech Behav & Syst Safety Traff Engn Shijiazhuang Peoples R China Shijiazhuang Tiedao Univ Sch Mech Engn Shijiazhuang Peoples R China Shijiazhuang Tiedao Univ Hebei Prov Key Lab Engn Machinery Power & Transmis Shijiazhuang Peoples R China Shijiazhuang Tiedao Univ State Key Lab Mech Behav & Syst Safety Traff Engn 17 NortheastInner Ring 2 Shijiazhuang 050043 Hebei Peoples R China

This paper presents a novel hybrid algorithm based on CMOGWO-ADNSGA-II to solve the vibration stability problem during the operation of a EMS-type maglev train dynamics model subjected to strong non-linear magnetic buoyancy. The proposed algorithm optimizes the control system parameters of EMS-type maglev train suspensions by combining an improved multi-objective chaotic grey wolf algorithm (CMOGWO) with an improved non-dominated Sorting genetic algorithm-II (ADNSGA-II) to enhance the search capability of the algorithm and ensure population diversity. The efficacy of the algorithm is demonstrated by applying it to the EMS-type maglev train suspension frame control system to find the optimal control parameters. Experimental results show that the system with the optimal parameters applied significantly reduces the suspension gap amplitude and the corresponding standard deviation, as well as the vertical acceleration amplitude and the corresponding standard deviation during operation. The proposed algorithm provides a good solution for EMS-type maglev train suspension vibration control, which can improve its performance and safety.

关键词： electromagnetic suspension-type maglev trains hybrid optimization algorithm vibration control non-linear magnetic buoyancy multi-objective problem

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multi-objective cluster analysis using a gradient evolution algorithm

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SOFT COMPUTING 2020年第15期24卷 11545-11559页

作者： Kuo, R. J. Zulvia, Ferani E. Natl Taiwan Univ Sci & Technol Dept Ind Management 43Sect 4Kee Lung Rd Taipei 106 Taiwan Univ Pertamina Dept Logist Engn Teuku Nyak Arief Rd Jakarta 12220 Indonesia

Data analysis becomes more important since rapid technology developments. In data analysis, data clustering is one of the very useful approaches. It can reveal important information hiding inside the dataset by organizing the instances based on their similarity. The objectives of data clustering are maximizing dissimilarity between clusters and minimizing dissimilarity within clusters. In order to construct a good clustering results, many clustering algorithms have been proposed, including the metaheuristic-based clustering algorithms. Recently, a new metaheuristic algorithm named gradient evolution has been proposed. This algorithm shows a good performance on solving the optimization problems. Therefore, this paper employs this GE algorithm for solving the clustering problem. In order to obtain a better clustering result, this paper considers multi-objective clustering instead of single-objective clustering. In this paper, the original GE algorithm is improved so then it is suitable for the multi-objective problem. The proposed modification includes the procedure for vector updating and jumping which involves Pareto rank assignment. In addition, it also employs K-means algorithm to provide the final clustering result. The proposed algorithm is verified using some benchmark datasets. It is also compared with some other multi-objective metaheuristic-based clustering algorithms. The experimental results show that the proposed algorithm can obtain better results than other metaheuristic-based algorithms.

关键词： Cluster analysis multi-objective problem Metaheuristics Gradient evolution

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Portfolio optimization using novel co-variance guided Artificial Bee Colony algorithm

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SWARM AND EVOLUTIONARY COMPUTATION 2017年 33卷 119-130页

作者： Kumar, Divya Mishra, K. K. Motilal Nehru Natl Inst Technol Allahabad Comp Sci & Engn Dept Allahabad 211004 Uttar Pradesh India

Although the use of evolutionary algorithms and fuzzy logic for portfolio optimization is an established research area, this field remains fascinating because of its important financial aspects. The field is brisk and it trances as there always remain research issues which are yet to explore. The problem of portfolio optimization comprises of finding an optimal distribution of funds among various available securities so as to maximize the return and minimize the risk. Artificial Bee Colony (ABC) is one of the effectual and widely used optimization technique based on swarm intelligence. Mixing co-variance principles with ABC algorithm assists in quick convergence with more precision. This paper presents a novel co-variance guided Artificial Bee Colony algorithm for portfolio optimization. As portfolio optimization consists of simultaneous optimization of multiple conflicting objectives, this algorithm is named as multi-objective Co-variance based ABC (M-CABC). The efficacy of the proposed algorithm is tested on benchmark problems of portfolio optimization from the OR-library. The results validate the adept performance of the proposed algorithm in finding various optimal trade-off solutions simultaneously handling realistic constraints. The article concludes with exhaustive post-result analysis and observatory remarks to bring out some of the crucial properties of optimal portfolios.

关键词： Optimization Artificial Bee Colony Co-variance Portfolio Risk Return multi-objective problem Non-dominatedsorting Pareto-front

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