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Modern Optimization Methods for Science, Engineering and Technology

Editor G R Sinha Published November 2019

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Achieving a better solution or improving the performance of existing system design is an ongoing a process for which scientists, engineers, mathematicians and researchers have been striving for many years. Ever increasingly practical and robust methods have been developed, and every new generation of computers with their increased power and speed allows for the development and wider application of new types of solutions. This book defines the fundamentals, background and theoretical concepts of optimization principles in a comprehensive manner along with their potential applications and implementation strategies. It encompasses linear programming, multivariable methods for risk assessment, nonlinear methods, ant colony optimization, particle swarm optimization, multi-criterion and topology optimization, learning classifier, case studies on six sigma, performance measures and evaluation, multi-objective optimization problems, machine learning approaches, genetic algorithms and quality of service optimizations. The book will be very useful for wide spectrum of target readers including students and researchers in academia and industry.

Copyright © IOP Publishing Ltd 2020 Online ISBN: 978-0-7503-2404-5 • Print ISBN: 978-0-7503-2402-1

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Front matter

Introduction and background to optimization theory, linear programming.

K A Venkatesh

Multivariable optimization methods for risk assessment of the business processes of manufacturing enterprises

Vladimir Gorbunov

Nonlinear optimization methods—overview and future scope

Somesh Kumar Dewangan , Siddharth Choubey , Jyotiprakash Patra and Abha Choubey

Implementing the traveling salesman problem using a modified ant colony optimization algorithm

Zar Chi Su Su Hlaing , G R Sinha and Myo Khaing

Application of a particle swarm optimization technique in a motor imagery classification problem

Rahul Kumar and Mridu Sahu

Multi-criterion and topology optimization using Lie symmetries for differential equations

Sailesh Kumar Gupta

Learning classifier system

Kapil Kumar Nagwanshi

A case study on the implementation of six sigma tools for process improvement

Bonya Mukherjee , Rajesh Chamorshikar and Subrahmanian Ramani

Performance evaluation and measures

K A Venkatesh and N Pushkala

Evolutionary techniques in the design of PID controllers

Santosh Desai , Rajendra Prasad and Shankru Guggari

A variational approach to substantial efficiency for linear multi-objective optimization problems with implications for market problems

Glenn Harris and Sien Deng

A machine learning approach for engineering optimization tasks

Arpana Rawal , Mamta Singh and Jyothi Pillai

Simulation of the formation process of spatial fine structures in environmental safety management systems and optimization of the parameters of dispersive devices

Sergij Vambol , Viola Vambol , Nadeem Ahmad Khan , Kostiantyn Tkachuk , Oksana Tverda and Sirajuddin Ahmed

Future directions: IoT, robotics and AI based applications

K C Raveendranathan

Efficacy of genetic algorithms for computationally intractable problems

Ajay Kulkarni and Sachin Puntambekar

A novel approach for QoS optimization in 4G cellular networks

Vandana Khare and G R Sinha

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https://doi.org/10.1088/978-0-7503-2404-5

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Published November 2019

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Myanmar Institute of Information Technology Mandalay, Myanmar

About the editor

G R Sinha is working as Adjunct Professor at International Institute of Information Technology, Bangalore, currently deputed as Professor at Myanmar Institute of Information Technology, Mandalay. He obtained his B.E. and M.Tech. with Gold Medal from National Institute of Technology, Raipur and his Ph.D. in Electronics & Telecommunication Engineering from Chhattisgarh Swami Vivekanand Technical University, Bhilai. He has published over 200 research papers in various international and national journals and conferences, is an active reviewer and editorial member of numerous international journals and has authored or edited six books.

Particle Swarm Optimization: A Comprehensive Survey

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Scalable optimization and decision-making in operations research

• Published: 08 August 2022
• Volume 316 , pages 1–4, ( 2022 )

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• Jun Pei 1 &
• Panos M. Pardalos 2  

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Acknowledgements

We gratefully acknowledge Editor-in-Chief Professor Endre Boros, and the Publications Managers Katie d’Agosta and Ann Pulido for supporting us in organizing this special issue. We sincerely thank all the authors who made excellent contributions and the reviewers who provided their helpful suggestions. Jun Pei would like to acknowledge the support by the National Natural Science Foundation of China (Nos. 71922009, 72071057, 71871080, 72188101).

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School of Management, Hefei University of Technology, Hefei, China

Department of Industrial and Systems Engineering, University of Florida, Gainesville, USA

Panos M. Pardalos

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Correspondence to Jun Pei .

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Pei, J., Pardalos, P.M. Scalable optimization and decision-making in operations research. Ann Oper Res 316 , 1–4 (2022). https://doi.org/10.1007/s10479-022-04895-x

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Published : 08 August 2022

Issue Date : September 2022

DOI : https://doi.org/10.1007/s10479-022-04895-x

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Working Paper 24-04: Measuring Price Effects from Disasters Using Public Data: A Case Study of Hurricane Ian

​Justin C. Contat, William M. Doerner, Robert N. Renner, and Malcolm J. Rogers

​​Ab​stract:

Natural disasters can disrupt housing markets, causing destruction to communities and distress to economic activity. To estimate the effects of disasters on home prices, publicly-available data on property damages are often used to classify “treated” properties. However, by design these data lack precise geospatial information, leading to measurement error in the treatment variable as aggregate measures must be used. We leverage leading difference-in-differences and synthetic control methodologies across various treatments and levels of geography to measure price effects with such data following Hurricane Ian’s unexpected landfall in southwest Florida during September 2022, coinciding with the state’s initial recovery from the COVID-19 pandemic. Empirical results suggest positive, time-varying price effects, though we place caveats on these results as there may be many mechanisms underway; our results should be interpreted as descriptive correlations and not causal effects for various reasons. Our main contribution is methodological, highlighting the importance of robustness checks, functional form, statistical techniques, and testing across different samples. Additionally, quicker access to high quality public data could enhance quantitatively-informed conversations on natural disaster effects.​

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• Published: 14 May 2024

2023 summer warmth unparalleled over the past 2,000 years

• Jan Esper   ORCID: orcid.org/0000-0003-3919-014X 1 , 2 ,
• Max Torbenson   ORCID: orcid.org/0000-0003-2720-2238 1 &
• Ulf Büntgen 2 , 3 , 4  

Nature ( 2024 ) Cite this article

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• Climate change
• Palaeoclimate

Including an exceptionally warm Northern Hemisphere (NH) summer 1 ,2 , 2023 has been reported as the hottest year on record 3-5 . Contextualizing recent anthropogenic warming against past natural variability is nontrivial, however, because the sparse 19 th century meteorological records tend to be too warm 6 . Here, we combine observed and reconstructed June-August (JJA) surface air temperatures to show that 2023 was the warmest NH extra-tropical summer over the past 2000 years exceeding the 95% confidence range of natural climate variability by more than half a degree Celsius. Comparison of the 2023 JJA warming against the coldest reconstructed summer in 536 CE reveals a maximum range of pre-Anthropocene-to-2023 temperatures of 3.93°C. Although 2023 is consistent with a greenhouse gases-induced warming trend 7 that is amplified by an unfolding El Niño event 8 , this extreme emphasizes the urgency to implement international agreements for carbon emission reduction.

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Department of Geography, Johannes Gutenberg University, Mainz, Germany

Jan Esper & Max Torbenson

Global Change Research Institute of the Czech Academy of Sciences, Brno, Czech Republic

Jan Esper & Ulf Büntgen

Department of Geography, University of Cambridge, Cambridge, United Kingdom

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Department of Geography, Masaryk University, Brno, Czech Republic

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Esper, J., Torbenson, M. & Büntgen, U. 2023 summer warmth unparalleled over the past 2,000 years. Nature (2024). https://doi.org/10.1038/s41586-024-07512-y

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Received : 16 January 2024

Accepted : 02 May 2024

Published : 14 May 2024

DOI : https://doi.org/10.1038/s41586-024-07512-y

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Computer Science > Computation and Language

Title: a survey on rag meets llms: towards retrieval-augmented large language models.

Abstract: As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) techniques can offer reliable and up-to-date external knowledge, providing huge convenience for numerous tasks. Particularly in the era of AI-generated content (AIGC), the powerful capacity of retrieval in RAG in providing additional knowledge enables retrieval-augmented generation to assist existing generative AI in producing high-quality outputs. Recently, large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation, while still facing inherent limitations, such as hallucinations and out-of-date internal knowledge. Given the powerful abilities of RAG in providing the latest and helpful auxiliary information, retrieval-augmented large language models have emerged to harness external and authoritative knowledge bases, rather than solely relying on the model's internal knowledge, to augment the generation quality of LLMs. In this survey, we comprehensively review existing research studies in retrieval-augmented large language models (RA-LLMs), covering three primary technical perspectives: architectures, training strategies, and applications. As the preliminary knowledge, we briefly introduce the foundations and recent advances of LLMs. Then, to illustrate the practical significance of RAG for LLMs, we categorize mainstream relevant work by application areas, detailing specifically the challenges of each and the corresponding capabilities of RA-LLMs. Finally, to deliver deeper insights, we discuss current limitations and several promising directions for future research.

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Revenue Slumps and Fiscal Capacity: Evidence from Brazil

This paper investigates how non-tax revenues impact tax collection in Brazilian municipalities, focusing on shifts in intergovernmental transfers due to population updates. Our analysis reveals asymmetric effects of shocks: revenue gains lead to increased spending without tax reductions, while losses in transfers prompt investments in fiscal capacity and boost tax revenues. Enhancing fiscal capacity entails adjusting tax bureaucrat payments, improving property registries, and cracking down on delinquency, with heterogeneous responses based on political competition and the educational levels of local leaders and the bureaucracy. These findings emphasize the importance of rules that reduce the reliance on non-tax revenues and promote effective tax collection.

We are grateful to Juliano Assunão, Bruno Ferman, Fred Finan, François Gerard, Gustavo Gonzaga, Rudi Rocha, David Schonholzer, Jonathan Weigel, and participants at various seminars and conferences for comments and suggestions. We thank financial support for this project from the Spanish Ministry of Education (grant RTI2018-097271-B-I00). The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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