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A Novel Particle Swarm optimization Algorithm for Meta-heuristic Analysis Mechanism Based on Population Learning Strategies and Adaptive Selection of Leadership Particles 11

A Novel Particle Swarm Optimization Algorithm for Meta-heuri...

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IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA)

作者： Wu, Yuheng Zhu, Xingyu Zhao, Wenxu Xia, Xiaona Qufu Normal Univ Sch Comp Sci Rizhao Peoples R China Qufu Normal Univ Fac Educ Chinese Acad Educ Big Data Qufu Shandong Peoples R China

ISBN: (纸本)9798350364941;9798350364958

To improve the particle swarm optimization algorithm's population diversity and global search ability, this study proposes a novel particle swarm optimization algorithm for metaheuristic analysis mechanism based on population learning strategies and adaptive selection of leadership particles (ASLPSO) to achieve the fusion of population learning strategy and adaptive selection method of leadership particles. The particle swarm is adaptively separated into several populations through density peak clustering. Meanwhile, a new learning strategy is designed for analyzing the local optimal particles of each subgroup, that might enable the ordinary particles to learn effectively. The global optimum is relatively ensured by comparing the fitness value of each local optimal particle, which might obtain the best performances of multi-peak reference functions. After comparing with the approximate algorithms on multiple benchmark functions, it is found that the standard deviation and error mean value are improved. Therefore, ASLPSO has enhanced the population diversity and global search ability, prevented the algorithm from falling into premature too early.

关键词： Meta-heuristic algorithm single objective optimization Particle swarm optimization Inertia coefficient Clustering

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Multi-attribute decision making using fuzzy ranking and Time Varying DE for optimal dispatch of MicroGrid 3

Multi-attribute decision making using fuzzy ranking and Time...

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3rd International Conference on Eco-Friendly Computing and Communication Systems (ICECCS)

作者： Pathak, Archana Pandit, Manjaree Kumar, Y. MANIT Bhopal India MITS Dept Elect Engn Gwalior India MANIT Dept Elect Engn Bhopal India

ISBN: (纸本)9781479970025

With depleting fossil reserves, increasing cost and global focus on reducing emissions, efforts for increasing participation of renewable energy sources have gained momentum. The smart micro grid technology is therefore growing very fast with the aim of improving efficiency and reliability and facilitating integration of distributed energy resources (DER). The economical operation of smart grid is a very big challenge for operators in the deregulated power sector. Decision making with multiple conflicting objectives is a complex task and traditional methods do not work well in practical uncertain environment. This paper presents a technique based on time-varying differential evolution (TVDE) for optimal scheduling of DERs in a micro-grid. A fuzzy decision making is carried out to rank the different solutions in order of their merit, considering both, cost and emission reduction objectives. Pareto optimal solution set is generated to cover the full operating range of DERs. The proposed approach is validated on a 4-Distributed Energy Resources and 14-Bus radial micro network and the results are found to be promising.

关键词： Time Varying Differential Evolution Multi-objective optimization single objective optimization Multi-attributem decision making Fuzzy ranking Pareto optimal solutions

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A Differential Evolution Algorithm with Success-based Parameter Adaptation for CEC2015 Leaming-based optimization

A Differential Evolution Algorithm with Success-based Parame...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Awad, Noor Ali, Mostafa Z. Reynolds, Robert G. Jordan Univ Sci & Technol Irbid 22110 Jordan Wayne State Univ Detroit MI 48202 USA

ISBN: (纸本)9781479974924

Developing efficient evolutionary algorithms for solving learning-based real-parameter single objective optimization is a very challenging and essential task in many real applications. This task involves finding the best optimal solution with least computational cost, avoiding premature convergence. This paper proposes a new efficient Differential Evolution algorithm with success-based parameter adaptation with resizing population space. We introduce a new technique to adapt the control parameters which uses a memory-based structure of previous successful settings. Moreover, the population size is adapted linearly to find the most suitable size which helps to guide the search in each optimization loop. The proposed algorithm is tested on the benchmarks of the CEC2015 real parameter single objective competition. The results affirm the efficiency and robustness of our approach to reach good results.

关键词： evolutionary algorithms differential evolution premature convergence single objective optimization

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MULTI-CRITERIA SUPPLY CHAIN INVENTORY MODELS WITH TRANSPORTATION COSTS

MULTI-CRITERIA SUPPLY CHAIN INVENTORY MODELS WITH TRANSPORTA...

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作者： Natarajan, Ajay PennState University Libraries

学位级别：Doctor of Philosophy

This thesis deals with developing and solving tactical inventory planning models for a supply chain that will enable the individual companies to determine their ordering policies efficiently under different conditions. The supply chain is modeled as a single warehouse supplying a product to several retailers which in turn satisfies the end consumer demand. The supply chain operates under a decentralized control, i.e., each location is managed by an independent decision maker (DM).In the first model, using the conventional single cost objective framework we propose a new coordination scheme that enables the warehouse to better manage its inventory and at the same time meet the retailers’ demands without deviating too much from their requirements. To avoid the problem of estimating marginal cost information and to incorporate the DM’s preference information, a more realistic multiple criteria model is then developed. To account for discounts in shipping, actual freight rate functions are used to model transportation costs between the stages. The conflicting criteria considered are: 1) capital invested in Inventory 2) annual number of orders 3) annual transportation costs. While the first two models deal with deterministic demand and constant lead time, the third model deals with stochastic demand and random lead time. In addition to the above three criteria, fill rate is used as a fourth criterion to measure customer satisfaction. The multiple criteria models are solved to generate several efficient solutions. The value path method, a visual tool, is used to display tradeoffs associated with the efficient solutions to the DM of each location in the supply *** models are tested with real world data obtained from a Fortune 500 consumer products company. Additional problems faced while extending the theoretical models to the real world data are addressed and solved. The decision making process is simulated by using an executive from the company to be the DM for the

关键词： Multi-Criteria optimization single objective optimization Inventory policies Transportation Inventory Supply Chain Stochastic modelling Supply chain Case Study

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Applications of Thyristor Controlled Series Compensator in Power System: An Overview

Applications of Thyristor Controlled Series Compensator in P...

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International Conference on Power Signals Control and Computations (EPSCICON)

作者： Jamhoria, Sangeeta Srivastava, Laxmi Madhav Inst Sci & Technol Dept Elect Engn Gwalior India

ISBN: (纸本)9781479936120

In recent years, power demand has increased day by day while the expansion of power generation and transmission has been limited due to limited resources and environmental restrictions. Flexible Alternating Current Transmission Systems (FACTS) devices play an important role in improving the power system performance. Flexible AC transmission systems devices have been mainly used for solving various steady states, dynamic and transient control problems of power system. TCSC is an important member of FACTS family. In a power system, TCSC device has high potential in applications because it may improve power system performance including power flow control, transfer capability enhancement of the transmission system, transient stability enhancement and sub-synchronous resonance (SSR) mitigation etc. As other FACTS devices, TCSC is also a costly device. Therefore this is utmost essential to find its optimal size and location in a power system to achieve maximum benefit of this device. This complicated problem of finding optimal location and sizing of TCSC has been solved by researchers in different ways by considering different objectives. This paper presents a brief review of the work carried out by researchers in this field.

关键词： FACTS Devices Multi objective optimization Performance Parameters of System single objective optimization TCSC

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Rotor Saliency optimization for High-Power Density Wound-Field Flux-Switching Machines

Rotor Saliency Optimization for High-Power Density Wound-Fie...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Fereydoonian, Mostafa Lee, Kangbeen Choi, Gilsu Lee, Woongkul Michigan State University Department of Electrical and Computer Engineering East LansingMI United States Inha University Department of Electrical Engineering Incheon Korea Republic of

ISBN: (纸本)9798350316445

This paper conducts a rotor saliency optimization to enhance the electromagnetic performance of a wound-field flux-switching machine (WFFSM). The WFFSM comprises two sets of windings, namely field and armature windings, on the stator. Having both windings on the stator creates inefficient magnetic flux paths that do not directly contribute the electromagnetic torque generation. Therefore, the main objective of this paper is to design a rotor structure that facilitates an optimal magnetic flux path, leading to high and smooth torque generation. The study extensively analyzes how the rotor geometry affects the terminal voltage and torque characteristics. As a result of the optimization, the terminal voltage total harmonic distortion (THD) decreases significantly from 0.46 to 0.11, and there is an impressive 89% reduction in self-inductance spikes. By incorporating an advanced cooling system and employing higher current density levels, the optimized WFFSM achieves a torque level comparable to 97% of the baseline flux-switching PM machine (FSPMM). While the FSPMM exhibits superior efficiency in the constant torque region, the WFFSM's unique field regulation capability allows it to maintain better efficiency in the constant power region. © 2023 IEEE.

关键词： Circumferential winding flux linkage frozen permeability permanent magnet rotor optimization rotor tooth eccentricity rotor tooth width single objective optimization toroidal winding torque segregation

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Sensitivity-based guided automatic calibration of hydrological models

Sensitivity-based guided automatic calibration of hydrologic...

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作者： Semnani, Mohammad University of Manitoba

学位级别：硕士

A new method for efficient calibration of complex hydrological models that combines Dynamically Dimensioned Search (DDS) global optimization algorithm with Global Sensitivity Analysis (GSA) methods is introduced. This approach, which is called sensitivity-informed DDS, utilizes sensitivity indices to increase the probability of perturbation for the most sensitive parameters, while giving low chance to least sensitive ones. This feature improves the efficiency and effectiveness of optimization by finding good quality solutions in a shorter time. Three different implementations of sensitivity-informed DDS are considered. The first approach is named as GSA↔DDS, in which GSA toolboxes (Morris or Sobol) are performed initially and throughout the optimization process to constantly update the sensitivity information. The second approach is called GSA→DDS. In this method, the GSA methods are only performed initially to include the results of GSA within optimization process. The final implementation is called VARS→DDS. In this method, to enhance the efficiency of sensitivity analysis and optimization, VARS toolbox is performed outside the optimization to provide the sensitivity information. The performances of GSA↔DDS, GSA→DDS and VARS→DDS are compared with original DDS by solving various optimization problems (test functions and model calibration case studies). According to the results, when calibrating complex hydrological models with enough computational budget, VARS→DDS is significantly more efficient and effective than original DDS. However, the results also show that GSA→DDS and GSA↔DDS methods do not substantially improve the convergence rate and the final best solution compared to DDS. Thus, VARS→DDS is the recommended approach for sensitivity-informed DDS in calibration of distributed and semi-distributed models, when enough computational resources are available.

关键词： Sensitivity Analysis single objective optimization Model Calibration

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Self-Organizing Migrating Algorithm Pareto

Self-Organizing Migrating Algorithm Pareto

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作者： Diep, Quoc Bao Brno University of Technology

In this paper, we propose a new method named Pareto-based self-organizing migrating algorithm (SOMA Pareto), in which the algorithm is divided into the Organization, Migration, and Update processes. The important key in the Organization process is the application of the Pareto Principle to select the Migrant and the Leader, increasing the performance of the algorithm. The adaptive PRT, Step, and PRTVector parameters are applied to enhance the ability to search for promising subspaces and then to focus on exploiting that subspaces. Based on the testing results on the well-known benchmark suites such as CEC’13, CEC’15, and CEC’17, the superior performance of the proposed algorithm compared to the SOMA family and the state-of-the-art algorithms such as variant DE and PSO are confirmed. These results demonstrate that SOMA Pareto is an effective, promising algorithm.

关键词： self-organizing migrating algorithm SOMA single objective optimization swarm intelligence

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optimization design of muffler based on acoustic transfer matrix and genetic algorithm

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JOURNAL OF VIBROENGINEERING 2014年第5期16卷 2216-2223页

作者： Chen, Feng Zhejiang Inst Mech & Elect Engn Dept Mech Engn Hangzhou 310053 Zhejiang Peoples R China

So as to improve the noise reduction performance and the optimization efficiency of the muffler, an optimization design method based on acoustic transfer matrix and genetic algorithm was proposed in this paper, wherein the transmission loss at certain frequency was treated as the optimization objective, and each structural dimension of the muffler as the design variable, the structural optimization design was conducted through genetic algorithm, thus the optimal solutions were eventually obtained;Moreover, single objective optimization and multiple objective optimization were successively performed, with the results compared with each other, through which it could be demonstrated that the result of the latter was superior to that of the former, concretely, the noise was reduced by 21.32 dB.

关键词： muffler acoustic transfer matrix and genetic algorithm optimization design transmission loss single objective optimization multiple objective optimization

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Optimal Scheduling of Automatic Flushing Devices in Water Distribution System

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2015年第6期141卷 04014081-04014081页

作者： Xie, Xiongfei Nachabe, Mahmoud Zeng, Bo Pinellas Cty Dept Environm & Infrastruct Clearwater FL 33756 USA Univ S Florida Dept Civil & Environm Engn Tampa FL 33620 USA Univ S Florida Dept Ind & Management Syst Engn Tampa FL 33620 USA

Maintaining sufficient chlorine residual in a water distribution system is important for water quality and system integrity. Discharging stagnant water from the pipeline through automatic flushing device (AFD) is a feasible method to maintain water quality. This paper presents a simulation-based optimization method to minimize total AFD discharge volume during a 24-h horizon. EPANET 2.0 is used as a hydraulics and water quality simulator. This is formulated as a single-objective optimization problem. The decision variables are the AFD operation patterns. The methodology has three phases. In the first phase, AFD discharge capacities are calculated to evaluate whether the existing AFDs are able to maintain chlorine residuals in the water network. In the second phase, the decision variables are converted to AFD discharge rates. A reduced gradient algorithm is used to quickly explore and narrow down the solution space. At the end of this phase, decision variables are switched back to the AFD operation patterns. In the third phase, simulated annealing is used to search intensively to exploit the global minimum. The method is demonstrated on the water system located at the south end of Pinellas County, Florida, where AFD optimal operation patterns are achieved.

关键词： Water quality simulation EPANET single objective optimization Simulated annealing

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