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Superdefect Photonic Crystal Filter optimization Using Grey Wolf Optimizer

IEEE PHOTONICS TECHNOLOGY LETTERS 2015年第22期27卷 2355-2358页

作者： Chaman-Motlagh, Abolfazl Imam Hossein Comprehens Univ Dept Elect Engn Tehran *** Iran

This letter proposes a new method for designing photonic crystal (PhC) filters. The method is proposed while designing novel superdefect PhC filters with three and five ellipse-shaped rods. The main idea of the method is to formulate the optical filter design problem as an optimization problem and to solve it by single-objective optimization techniques. This method bypasses discovering the complex relationship between the output spectral transmission performance and the radii of rods. The results demonstrate the merits of the method in facilitating the design process of the PhC filters and finding high-performance designs.

关键词： Photonic crystal filters super defect grey wolf optimizer (GWO) single-objective optimization engineering design

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THE MAGIC OF NASH SOCIAL WELFARE IN optimization: DO NOT SUM, JUST MULTIPLY!

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ANZIAM JOURNAL 2022年第2期64卷 119-134页

作者： Charkhgard, Hadi Keshanian, Kimia Esmaeilbeigi, Rasul Charkhgard, Parisa Univ S Florida Dept Ind & Management Syst Engn Tampa FL 33620 USA Univ Tampa Informat & Technol Management Dept Tampa FL 33606 USA Deakin Univ Sch Informat Technol Geelong Vic 3220 Australia Univ Newcastle Sch Math & Phys Sci Callaghan NSW 2308 Australia

We explain some key challenges when dealing with a single- or multi-objective optimization problem in practice. To overcome these challenges, we present a mathematical program that optimizes the Nash social welfare function. We refer to this mathematical program as the Nash social welfare program (NSWP). An interesting property of the NSWP is that it can be constructed for any single- or multi-objective optimization problem. We show that solving the NSWP could result in more desirable solutions in practice than its single- or multi-objective counterpart. We also discuss several promising approaches that could be employed to solve the NSWP in practice.

关键词： Nash social welfare equity single-objective optimization multi-objective optimization multiplicative program

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Evolutionary Time-Use optimization for Improving Children's Health Outcomes 17th

Evolutionary Time-Use Optimization for Improving Children's ...

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17th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Xie, Yue Neumann, Aneta Stanford, Ty Rasmussen, Charlotte Lund Dumuid, Dorothea Neumann, Frank Univ Adelaide Adelaide SA Australia Univ South Australia Allied Hlth & Human Performance Alliance Res Exercise Nutr & Act Adelaide SA Australia Norwegian Univ Sci & Technol Trondheim Norway Univ Limerick Dept Phys Educ & Sport Sci Limerick Ireland

ISBN: (纸本)9783031147210;9783031147203

How someone allocates their time is important to their health and well-being. In this paper, we show how evolutionary algorithms can be used to promote health and well-being by optimizing time usage. Based on data from a large population-based child cohort, we design fitness functions to explain health outcomes and introduce constraints for viable time plans. We then investigate the performance of evolutionary algorithms to optimize time use for four individual health outcomes with hypothetical children with different day structures. As the four health outcomes are competing for time allocations, we study how to optimize multiple health outcomes simultaneously in the form of a multi-objective optimization problem. We optimize one-week time-use plans using evolutionary multi-objective algorithms and point out the trade-offs achievable with respect to different health outcomes.

关键词： Real-world application Time-use optimization single-objective optimization Multi-objective optimization

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DEVELOPMENT OF AN OPEN-SOURCE AUTONOMOUS CFD META-MODELING ENVIRONMENT FOR SMALL-SCALE COMBUSTOR optimization - PART I

DEVELOPMENT OF AN OPEN-SOURCE AUTONOMOUS CFD META-MODELING E...

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67th ASME Turbomachinery Technical Conference and Exposition (Turbo Expo) on Road Mapping the Future of Propulsion and Power

作者： Briones, Alejandro M. Rankin, Brent A. Univ Dayton Res Inst Dayton OH 45469 USA Air Force Res Lab Wright Patterson AFB OH USA

ISBN: (纸本)9780791885994

This work presents an open-source autonomous CFD meta-modeling environment (OpenACME) for small-scale combustor design optimization in a deterministic and continuous design space. OpenACME couples several object-oriented programming open-source codes for conjugate-heat transfer, steady-state, multiphase incompressible RANS CFD-assisted engineering design meta-modeling. There are fifteen design variables. Non-parametric rank regression (NPRR), global sensitivity analyses (GSA), and single-objective (SOO) optimization strategies are evaluated. The Euclidean distance (single-objective criterion) between a design point and the utopic point is based on the multi-objective criteria: combustion efficiency (eta) maximization and pattern factor (PF), critical liner area factor (A(critical)), and total pressure loss (TPL) minimization. The SOO approach conducts Latin Hypercube Sampling for reacting flow CFD for subsequent local constraint optimization by linear interpolation. The local optimization successfully improves the initial design condition. The SOO approach is useful for guiding the design and development of future gas turbine combustors. NPRR and GSA indicate that there are no leading-order design variables controlling eta, PF, A(critical), and TPL Therefore, interactions between design variables control these output metrics because the output design space is inherently non-smooth and nonlinear. In summary, OpenACME is developed and demonstrated to be a viable tool for combustor design meta-modeling and optimization studies.

关键词： automated optimization flamelet/progress variable conjugate heat transfer single-objective optimization

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AOAB - Automated optimization Algorithm Benchmarking 10

AOAB - Automated Optimization Algorithm Benchmarking

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12th Annual Genetic and Evolutionary Computation Conference (GECCO)

作者： Weise, Thomas Niu, Li Tang, Ke Univ Sci & Technol China Nat Inspired Computat & Applicat Lab Hefei 230027 Anhui Peoples R China

ISBN: (纸本)9781450300735

In this paper we present AOAB, the Automated optimization Algorithm Benchmarking system. AOAB can be used to automatically conduct experiments with numerical optimization algorithms by applying them to different benchmarks with different parameter settings. Based on the results, AOAB can automatically perform comparisons between different algorithms and settings. It can aid the researcher to identify trends for good parameter settings and to find which algorithms are suitable for which type of problem. We introduce the system structure of AOAB (the server and the graphical client interface), define the way in which optimizers and benchmark functions can be implemented for the use in AOAB, and conduct an illustrative example experiment with our system: a comparison between Random Search and two Hill Climbers.

关键词： AOAB Automated Benchmarking optimization Algorithm Distribution single-objective optimization Numerical optimization

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Simpler and Enhanced Multifactorial Evolutionary Algorithms for Continuous optimization Tasks

Simpler and Enhanced Multifactorial Evolutionary Algorithms ...

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IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)

作者： Chang, Yangyang Sobelman, Gerald E. Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

ISBN: (纸本)9781665420358

Multifactorial optimization has become one of the most promising paradigms for evolutionary multitasking within the field of computational intelligence. This methodology can improve the performance results for multiple, simultaneous optimization problems by exploiting the transfer of genetic information between them. In this paper, we present an in-depth analysis of this approach by considering several variations of the standard multifactorial evolutionary algorithm (MFEA). By using a simpler structure together with some enhanced operators, two new multifactorial evolutionary algorithms are proposed. We demonstrate that, compared with the traditional MFEA, our approach produces better results on a set of continuous optimization benchmark problems.

关键词： single-objective optimization evolutionary computation multifactorial optimization continuous optimization problems multifactorial evolutionary algorithm

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A Winner-Take-All Methodology: Finding the Best Evolutionary Algorithm for the Global optimization of Functions 12

A Winner-Take-All Methodology: Finding the Best Evolutionary...

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14th International Conference on Genetic and Evolutionary Computation Conference (GECCO)

作者： Zhang, Tiantian Borrero, Giselle Georgiopoulos, Michael Univ Cent Florida Dept EECS Orlando FL 32816 USA

ISBN: (纸本)9781450311786

The problem of effectively and efficiently finding the global optimum of a function by using evolutionary algorithms is current and pertinent, and two of the evolutionary techniques that have received significant attention in the literature are Particle Swarm optimization (PSO), and Differential Evolution (DE), as well as their numerous variants. One way of taking advantage of the many good PSO and DE variant algorithms that have appeared in the literature is to run them all for a particular optimization problem and choose the best answer provided. This approach, referred to as the Naive Approach (NA) is time consuming. In this paper, we are using the naive approach with a suite of algorithms for each function minimization problem and we run the algorithms, in the suite, for a specific number of function evaluations (typically much smaller than the number of function evaluations needed for each specific algorithm to converge to a solution) and decide, using appropriate performance measures of merit, which one of these algorithms will continue running until convergence;the rest of the algorithms, deemed as algorithms that will eventually produce inferior solutions, are aborted. We refer to our methodology, for obvious reasons, as the Winner-Take-All (WTA) methodology. Using this methodology we introduce WTA algorithmic variants that are efficient and effective solvers of global optimization problems. In this paper, we report results on one of these variants, called WTA1, and show that it is very competitive compared to the constituent algorithms in the suite used for its design, and efficient compared to NA.

关键词： Particle Swarm optimization Differential Evolution single-objective optimization

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Resource Dispatch optimization for Firefighting Using a Differential Evolution Algorithm 3rd

Resource Dispatch Optimization for Firefighting Using a Diff...

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3rd International Conference on optimization, Learning Algorithms and Applications (OL2A)

作者： Matos, Marina A. Goncalves, Rui Rocha, Ana Maria A. C. Costa, Lino A. Alvelos, Filipe Univ Minho ALGORITMI Res Ctr LASI Campus Gualtar P-4710057 Braga Portugal

ISBN: (纸本)9783031530241;9783031530258

The incidence of forest fires has shown an upward trend in recent years. This increase can be attributed to rising ambient temperatures and population growth, which act as the primary catalysts for these disasters. The application of optimization techniques has significantly contributed to addressing forest firefighting challenges, enabling improvements in the efficiency and promptness of firefighting operations. This study focuses on a specific resource dispatch problem to combat forest fires, which involves assigning 7 resources to extinguish 20 ignitions. The main objective is to minimize the total area burned by these ignitions in a minimum period of time. To solve this problem, the differential evolution algorithm adapted to this context was applied. Furthermore, a statistical analysis was performed to evaluate the performance of differential evolution when different selection and crossover operators are tested. The preliminary results show that the current-to-best selection and exponential crossover operators are the most suitable to solve the resource dispatch problem for forest firefighting.

关键词： Forest Fires single-objective optimization Resource Dispatch Problem Differential Evolution

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optimization Methods with Power Quality Issues for Reactive Power Control of Distribution Networks with Dispersed Generation

Optimization Methods with Power Quality Issues for Reactive ...

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Bucharest PowerTech Conference

作者： Battistelli, C. Carpinelli, G. De Martinis, U. Mottola, F. Proto, D. Univ Naples Federico II Dept Elect Engn Naples Italy

ISBN: (纸本)9781424422340

The paper deals with the reactive power control in a MV distribution system in presence of Dispersed Generation (DG). The control variables are the reactive powers of DG units and capacitors, and the tap position of the on-load tap changer of the HV/MV transformer. single-objective and multi-objective optimization problems are formulated and solved in order to achieve the optimal control, even containing the impact of the waveform distortions. Numerical applications performed on a 109-bus test distribution system give evidence of the effectiveness of the proposed procedure.

关键词： Reactive power Power Quality Dispersed Generation Distribution systems single-objective optimization Multi-objective optimization

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Weighting Particle Swarm, Simulation Annealing and Local Search optimization for S/MIMO MC-CDMA Systems

Weighting Particle Swarm, Simulation Annealing and Local Sea...

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IEEE Swarm Intelligence Symposium

作者： Abrao, Taufik Ciriaco, Fernando Oliveira, Leonardo D. Angelico, Bruno A. Jeszensky, Paul Jean E. Casadevall, Fernando Department of Electrical Engineering State University of Londrina Paraná 86051-990 Brazil Department of Telecommunications and Control Engineering Escola Politécnica University of São Paulo 05508-900 Brazil Department of Signal Theory and Communication Universitat Politécnica de Catalunya Edificio D4 08034 Barcelona C. Jordi Girona 1-3 Spain

ISBN: (纸本)9781424427048

This paper analyzes the complexity-performance trade-off of three heuristic approaches applied to synchronous multicarrier multiuser detection (MuD) of single/multiple transmit antennas and multiple receive antennas code division multiple access (S/MIMO MC-CDMA) systems. Weighting particle swarm optimization (woPSO) and unitary Hamming distance search-based strategies, specifically 1-opt local search (1-LS) and simulation annealing (SA) multiuser detection algorithms, were analyzed in details using a single-objective antenna-diversity-aided optimization approach. Monte-Carlo simulations show that, after convergence, the performances reached by the three heuristic MuD (HEUR-MuD) S/MIMO MC-CDMA algorithms are identical, with computational complexities remarkably smaller than the optimum multiuser detector (OMuD). However, the computational complexities could differ substantially depending on the operation system conditions. The complexities of the HEUR-MuDs were carefully analyzed in order to demonstrate that 1-LS scheme provides the best trade-off between implementation complexity aspects and bit error rate (BER) performance when applied to multiuser detection of S/MIMO MC-CDMA systems with low order modulation.

关键词： MC-CDMA multiuser detection particle swarm optimization simulating annealing single-objective optimization unitary Hamming distance search

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