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A multi-strategy improved particle swarm optimization algorithm and its application to identifying uncorrelated multi-source load in the frequency domain

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NEURAL COMPUTING & APPLICATIONS 2017年第7期28卷 1635-1656页

作者： Gou, Jin Guo, Wang-Ping Wang, Cheng Luo, Wei Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China Xi An Jiao Tong Univ Sch Aerosp State Key Lab Strength & Vibrat Xian 710049 Peoples R China

As a type of indirect method, the traditional frequency approach to load identification establishes a systems frequency response function (FRF) and calculates loads using its inverse and the responses. Based on the established FRF method, a novel identification approach that transforms the inverse problem into a forward single-objective optimization model is proposed. Furthermore, a multi-strategy improved particle swarm optimization algorithm (MsiPSO) for identifying uncorrelated multi-source load in the frequency domain is also proposed. Depending on the specific application, MsiPSO initializes the swarm based on domain knowledge. It applies asymmetric and nonlinear strategies to adaptively set the control parameters and genetic operators to strengthen the diversity of the population and avoid local optima. In the experiments, MsiPSO is compared with the general particle swarm optimization (PSO) algorithm and some well-performing variants. A simulated model is then defined to validate the load recognition accuracy of the proposed approach. The experimental results show that MsiPSO is competitive with other methods in terms of convergence, stability, and precision. It has a higher recognition accuracy and is faster than traditional load identification methods and standard PSO.

关键词： Load identification Uncorrelated multisource load in the frequency domain Matrix inversion single-objective optimization Particle swarm optimization Multi-strategy improvement

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Optimal configuration and operating condition of counter flow cooling towers using particle swarm optimization algorithm

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APPLIED THERMAL ENGINEERING 2019年 151卷 318-327页

作者： Zhang, Yin Zhang, Huan Wang, Yaran You, Shijun Zheng, Wandong Tianjin Univ Sch Environm Sci & Engn Tianjin 300072 Peoples R China Tianjin Univ Minist Educ Key Lab Efficient Utilizat Low & Medium Grade Ene Tianjin 300350 Peoples R China

Cooling towers are broadly utilized in industrial activities and consume substantial energy, resource and expenditure. optimization of cooling tower configurations and operating conditions is imperative for reducing consumptions. In this paper, the counter flow cooling tower optimization problems are solved utilizing the particle swarm optimization (PSO) algorithm, which is able to handle both single-objective and deeper multi-objective optimization problems. Based on heat and mass transfer balance equations, the mathematical model of the counter flow cooling tower is established. Validation of the mathematical model shows satisfactory agreement with the experimental data. The single-objective particle swarm optimization (SOPSO) is explored through the proposed mathematical model. With appropriate population size and iteration step number, six cases pursuing minimum total annual cost are investigated by deciding multiple optimal decision variables. Since SOPSO only considers one objective, which may be restricted under conditions with diverse requirements, the multi-objective particle swarm optimization (MOPSO) is also introduced. The case for MOPSO involves four objectives, namely the range, tower characteristic ratio, effectiveness and water evaporation rate, while flow rates of air and water are the decision variables. The results of SOPSO and MOPSO are both compared with previous literatures and satisfactory performance of PSO algorithm is revealed.

关键词： Particle swarm optimization Mathematical model single-objective optimization Multi-objective optimization

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Structural crack detection without updated baseline model by single and multiobjective optimization

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2009年第3期23卷 752-768页

作者： Perera, Ricardo Fang, Sheng-En Huerta, C. Tech Univ Dept Struct Mech Madrid 28006 Spain

Identification, localization and quantification of structural damage can be performed through a model-updating procedure. Model-updating methods require a baseline finite element (FE) model of the undamaged structure, which imposes a restriction on their applicability and can become very problematic especially for large and complex civil structures. Modeling errors in the baseline model whose effects exceed the modal sensitivity to damage are critical and make an accurate estimation of damage impossible. This paper presents an identification algorithm using modal data for assessing structural damage that is based on FE-updating procedures and takes modeling error into account. To overcome its influence, differences of mode shapes and frequencies before and after damage for both numerical model and experimental measurements are used instead of the mode shapes and frequencies themselves. To formulate the objective function, two different approaches have been considered taking into account how these differences are grouped: a single-objective approach and a multiobjective approach. The effectiveness of both approaches is verified against numerical and experimental results. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Damage detection Model updating Modeling error single-objective optimization Multiobjective optimization Evolutionary algorithms

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Learning-Aided Evolution for optimization

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第6期27卷 1794-1808页

作者： Zhan, Zhi-Hui Li, Jian-Yu Kwong, Sam Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Zhejiang Normal Univ Jinhua 321004 Peoples R China Hanyang Univ Ansan 15588 South Korea

Learning and optimization are the two essential abilities of human beings for problem solving. Similarly, computer scientists have made great efforts to design artificial neural network (ANN) and evolutionary computation (EC) to simulate the learning ability and the optimization ability for solving real-world problems, respectively. These have been two essential branches in artificial intelligence (AI) and computer science. However, in humans, learning and optimization are usually integrated together for problem solving. Therefore, how to efficiently integrate these two abilities together to develop powerful AI remains a significant but challenging issue. Motivated by this, this article proposes a novel learning-aided evolutionary optimization (LEO) framework that plus learning and evolution for solving optimization problems. The LEO is integrated with the evolution knowledge learned by ANN from the evolution process of EC to promote optimization efficiency. The LEO framework is applied to both classical EC algorithms and some state-of-the-art EC algorithms including a champion algorithm, with benchmarking against the IEEE Congress on EC competition data. The experimental results show that the LEO can significantly enhance the existing EC algorithms to better solve both single-objective and multi-/many-objective global optimization problems, suggesting that learning plus evolution is more intelligent for problem solving. Moreover, the experimental results have also validated the time efficiency of the LEO, where the additional time cost for using LEO is greatly deserved. Therefore, the promising LEO can lead to a new and more efficient paradigm for EC algorithms to solve global optimization problems by plus learning and evolution.

关键词： optimization Artificial intelligence Evolution (biology) Artificial neural networks Learning systems Problem-solving Benchmark testing Artificial neural network (ANN) differential evolution (DE) evolutionary computation (EC) learning-aided evolution many-objective optimization multiobjective optimization particle swarm optimization (PSO) single-objective optimization

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Reconciling variability and optimal behaviour using multiple criteria in optimization models

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EVOLUTIONARY ECOLOGY 1998年第1期12卷 73-94页

作者： Schmitz, OJ Cohon, JL Rothley, KD Beckerman, AP Yale Univ Sch Forestry & Environm Studies New Haven CT 06511 USA

A major objective in behavioural and evolutionary ecology is to understand how animals make decisions in complex environments. Examinations of animal behaviour typically use optimization models to predict the choices animals ought to make. The performance of animals under specific conditions is then compared against the predicted optimal strategy. This optimization approach has come into question because model predictions often do not match animal behaviour exactly. This has led to serious scepticism about the ability of animals to exhibit optimal behaviour in complex environments. We show that conventional approaches that compare observed animal behaviour with single optimal values may bias the way we view real-world variation in animal performance. Considerable insight into the abilities of animals to make optimal decisions can be gained by interpreting why variability in performance exists. We introduce a new theoretical framework, called `multi-objective optimization', which allows us to examine decision-making in complex environments and interpret the meaning of variability in animal performance. A multi-objective approach defines the set of efficient choices animals may make in attempting to reach compromises among multiple conflicting demands. In a multi-objective framework, we may see variation in animal choices, but, unlike single-objective optimizations where there is one `best solution', this variation may represent a range of adaptive compromises to conflicting objectives. An important feature of this approach is that, within the set of efficient alternatives, no choice can be considered to yield higher fitness, a priori, than any other choice. Thus, variability and optimal behaviour may be entirely consistent. We illustrate our point using selected examples from foraging theory where there is already an optimization program in place.

关键词： adaptive behaviour environmental complexity multi-objective optimization single-objective optimization trade-offs variability

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optimization approach of balancing life cycle cost and environmental impacts on residential building design

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ENERGY AND BUILDINGS 2015年 87卷 282-292页

作者： Islam, Hamidul Jollands, Margaret Setunge, Sujeeva Bhuiyan, Muhammed A. RMIT Univ Sch Civil Environm & Chem Engn Melbourne Vic 3000 Australia

This paper describes an optimization approach of balancing life cycle cost and environmental impacts for typical Australian houses. It evaluates the effects of various alternative wall, floor and roofing assemblages to select an optimized house design. A Base House was modified with 18 alternative wall, 4 floor and 8 roofing designs. Each design was varied in such a way that it achieved specific star rating based on materials and assemblages. Two sets of objective functions, namely life cycle cost and environmental impacts were evaluated for each design using life cycle costing and life cycle assessment approaches, respectively. The use of Linear Programming helped to identify alternative best designs depending on single-objective (SOO) or multi-objective optimization (MOO). Outcomes from SOO depended on the choice of particular objective function, while outcomes from MOO depended on the decision-makers preferences in terms of weightings. A high star-rating weatherboard house with ceramic tiles and wooden floor, and skillion flat roofing was the best design if the weighting of all five objective functions were equal. A house designed with these optimal wall, floor and roofing had a much higher rating (by 1.3 stars), lower environmental impacts (by 10-20%) for the same life cycle cost. (C) 2014 Elsevier B.V. All rights reserved.

关键词： single-objective optimization Multi-objective optimization Linear Programming Weighted sum approach Life cycle cost Life cycle environmental impacts

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An optimization study of passive flow control mechanism for a seashell-shaped wind turbine

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ENERGY 2024年 289卷

作者： Hamid, Hossam Abd El Maksoud, Rafea Mohamed Helwan Univ Fac Engn Mataria Mech Power Engn Dept Cairo 11718 Egypt

Small wind turbines have a lot of promise in areas that are remote from the power grid. For such small-scale applications, such as off-grid electricity, a spiral wind turbine (SWT), a novel type of horizontal-axis wind tur-bine, may be employed. Placing wind turbines within a duct is one potential method for increasing the efficiency of wind energy harvesting in low-wind urban locations. In this work, a shroud with a flange at its outlet is created using an optimization approach that attempts to maximize the coefficient of power (CP), which improves SWT performance. An evolutionary algorithm over a Kriging interpolative model serves as the optimizer in use. The shroud's shape is determined using a series of straight lines. Using the commercial code program ANSYS-FLUENT, the Reynolds-averaged Navier-Stokes (RANS) equations are solved along with the SST k-omega turbulence model to determine the turbine CP. The computational results are validated and confirmed with previously published results. The optimal shroud design introduced significant improvements in the CP when applied to the SWT, resulting in a maximum CP of 0.967 (at lambda = 3.25), which is 3.6 times the maximum CP of the bare SWT (CP = 0.2668 at lambda = 2.5).

关键词： Aerodynamics Spiral wind turbine Shroud single-objective optimization Optimal LHS Evolutionary algorithm

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An inductor modeling and optimization toolbox for RF circuit design

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INTEGRATION-THE VLSI JOURNAL 2017年 58卷 463-472页

作者： Passos, F. Roca, E. Castro-Lopez, R. Fernandez, F. V. CSIC IMSE CNM Inst Microelect Sevilla Seville Spain Univ Seville Seville Spain

This paper describes the SIDe-O toolbox and the support it can provide to the radio-frequency designer. SIDe-O is a computer-aided design toolbox developed for the design of integrated inductors based on surrogate modeling techniques and the usage of evolutionary optimization algorithms. The models used feature less than 1% error when compared to electromagnetic simulations while reducing the simulation time by several orders of magnitude. Furthermore, the tool allows the creation of S-parameter files that accurately describe the behavior of inductors for a given range of frequencies, which can later be used in SPICE-like simulations for circuit design in commercial environments. This toolbox provides a solution to the problem of accurately and efficiently optimizing inductors, which alleviates the bottleneck that these devices represent in the radio-frequency circuit design process.

关键词： Inductor design single-objective optimization Multi-objective optimization Surrogate modeling Radio-frequency circuit design

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Net Zero Energy in a Residential Building Using Heuristic optimization Solution

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2021年第2期32卷 458-471页

作者： Madathil, Deepthisree Pandi, V. Ravikumar Nair, Manjula G. Jamasb, Tooraj Thakur, Tripta Amrita Vishwa Vidyapeetham Dept Elect & Elect Engn Amritapuri India Copenhagen Business Sch Copenhagen Sch Energy Infrastruct CSEI Dept Econ Frederiksberg Denmark MANIT Natl Inst Technol Dept Elect Engn Bhopal India

In recent times, improvement in building designs has been on a steady upward trend, precipitated by large greenhouse gas emissions of energy draining outmoded construction materials. Global energy crisis exacerbated by climate change has motivated the design and construction of energy-efficient Net Zero Energy Buildings (NZEB). This paper proposes an energy management strategy for grid-connected Net Zero Energy Buildings that help to achieve a net zero balance of energy in Electrical Grid Photovoltaic-connected NZEB homes. The significance of NZEB was evaluated by providing insights into cost-savings for the consumer's net energy consumption and reduced carbon footprint. The proffered energy management strategy uses a single-objective Differential Evolution (DE) optimization algorithm that prunes the demand for electrical energy through an efficacious appliance scheduling routine. A novel mathematical energy index is introduced that enables consumers to monitor the net energy imported from the electrical grid and maintain a zero energy index. The proposed net zero energy concept is validated using DE and Genetic Algorithm (GA)-based optimization techniques.

关键词： Net zero energy buildings Differential evolution Energy management single-objective optimization Renewable energy Genetic algorithm

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Research on optimization of CCUS Injection Production Parameters in High-Temperature Reservoirs Based on Intelligent optimization Algorithms

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第6期49卷 8919-8937页

作者： Wang, Guodong Hou, Zhiwei Shi, Li PetroChina Liaohe Oilfield Explorat & Dev Res Inst Panjin Liaoning Peoples R China

The paper takes the Jidong Nanbu high temperature oil reservoir as the research object and establishes the comprehensive numerical model of CO2 storage and displacement based on the CO2 displacement mechanism, CO2 heat recovery mechanism, and CO2 geological storage mechanism. On this basis, the injection and production optimization model is established by combining the theory of multi-objective and single-objective optimization algorithm respectively. The objective function of the multi-objective injection and production optimization model is NPV and CO2 heat recovery. The model is solved using the NSGAII and MOPSO algorithm. The results show that the objective function value obtained by the MOPSO algorithm is better than the NSGAII algorithm. The optimal values of NPV and CO2 heat recovery were 4.781 x 108 CNY and 1.078 x 1016 J respectively. The objective function of the single objective injection and production optimization model is the NPV. GA and DE optimization algorithms are used to solve the model. The results show that the DE algorithm gets the better objective function value than the GA algorithm. The optimal value of NPV is 3.69 x 1010 CNY.

关键词： High temperature reservoir CCUS Injection-production parameters Intelligent optimization algorithm Multi-objective optimization single-objective optimization

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