Ziyuan Peng, Haibo Yuan, Heng Shen et al. · Journal of Electrotechnology Electrical Engineering and Management · 2024
With the extensive grid connection of various new energy sources, the traditional centralized economic dispatching mode is facing challenges in the new power system. This paper presents an optimal scheduling technology based on distributed power cluster model and advanced algorithm. By constructing edge clustering models of some new energy sources and analyzing their fluctuation characteristics, an optimization method for distributed economic scheduling is proposed based on the traditional consistency algorithm. In addition, a two-layer optimal planning model of AC-DC hybrid microgrid cluster is established. In the upper layer model, node coupling and power balance are considered to realize the cluster division of regional nodes. The bottom optimization configuration model aims to minimize the total cost, and uses optimization software to analyze and obtain optimal configuration results. Simulation results show that the proposed optimization scheduling technology has significant advantages in the field energy consumption of distributed power supply, reducing system cost, improving scheduling efficiency, etc. It provides a new theory and technical support for the optimization scheduling of distributed power supply system.
Cited by 0Open access
Anjali Basnyat Bista, Kim Aleksander Haukeland Paus, Isabel Seifert-Dähnn · Environmental Modelling & Software · 2025
Multi-objective optimization (MOO) methods are increasingly used to optimize the multifunctional benefits and costs of Nature-based solutions (NbS) in urban water management. However, comprehensive reviews of current research in this area remain limited. This scoping review analyzed 110 optimization studies focused on NbS in urban water management. It examined key components of the simulation-optimization (S/O) framework, including simulation models, optimization algorithms, objectives, spatial and temporal configurations. Twenty-three algorithms were assessed for their applications, strengths and limitations. NSGA-II was the most widely used, while advanced algorithms were applied in a few studies for complex, high-dimensional problems. Water quantity (87%) and costs (93%) were the most studied objectives, whereas only 11% of studies addressed other socio-environmental objectives. Various challenges in the S/O framework, such as integrating socio-environmental benefits, model complexity, uncertainties and optimal solution selection, are discussed. Future research should prioritize proper objectives selection, socio-environmental objectives integration and advanced dynamic NbS planning. • Analyzes the impact of components of simulation-optimization framework on model performance and computational complexity. • Reviews the strengths and weaknesses of optimization algorithms. • Identifies challenges encountered in the simulation-optimization framework and provides insights to resolve them. • Recommends future research on finding the appropriate objectives and on methods to integrate them into the optimization algorithm.
Cited by 3Open access
Saravanan G, Yuvaraj V, Ramasubramanian B et al. · 2025
This research focuses on creating and applying advanced meta heuristic algorithms for Optimization Problems. The primary goals are to improve the effectiveness and efficiency of nature-inspired algorithms and to solve complex optimization problems in multiple areas. The research describes a number of algorithms that utilize principles of swarm intelligence, such as Particle Swarm Optimization, to address problems in machine learning, robotics, and data analysis. The goal is to enable researchers and practitioners to use these algorithms on various real-life challenges to enhance the processes and outcomes of optimization. This study not only serves scholarly interests but also has real-world application value, catalyzing the use of advanced methods for solving optimization problems. It is shown that all algorithms are implemented in Python, which ensures that a large audience is able to use and access them easily. Each algorithm is accompanied by comprehensive documentation, which offers detailed explanations and illustrative examples, thus enabling users to easily understand and incorporate the algorithms into their projects. Application of these meta-heuristic algorithms is anticipated to improve not just the optimization processes but also the resultant performance across a wide spectrum of tasks. Such findings motivate practitioners and specialists to actively participate in fostering collaboration and further development within the research community, thereby further advancing methodologies in swarm intelligence. This study's findings are of considerable significance with respect to artificial intelligence and optimization, offering a basis for subsequent advancements. The ongoing refinement of these algorithms may provide, at some point, solutions for addressing more sophisticated issues, which will, in turn, promote breakthroughs in technology and engineering.
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Joshy, Anugrah Jo · Zenodo (CERN European Organization for Nuclear Research) · 2025
modOpt is a modular development environment and library for optimization algorithms, written in Python. It is primarily designed to support research and education in the field of numerical optimization. modOpt facilitates rapid and efficient development of optimization algorithms by enabling a modular approach to building algorithms and providing built-in capabilities for testing and benchmarking.modOpt as a library includes several gradient-based and gradient-free optimization algorithms. It provides interfaces to more than a dozen general-purpose optimizers, along with fully transparent implementations of several educational optimization algorithms. Additionally, modOpt offers various features to support students, optimization practitioners, and advanced developers. For instance, it includes built-in visualization and recording capabilities, interfaces to modeling frameworks such as JAX, CasADi, OpenMDAO and CSDL, and an interface to the CUTEst test problem set.
Cited by 0Open access
Jun Tang, Gang Liu, Qingtao Pan · IEEE/CAA Journal of Automatica Sinica · 2021
Swarm intelligence algorithms are a subset of the artificial intelligence (AI) field, which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications. In the past decades, numerous swarm intelligence algorithms have been developed, including ant colony optimization (ACO), particle swarm optimization (PSO), artificial fish swarm (AFS), bacterial foraging optimization (BFO), and artificial bee colony (ABC). This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures. It provides an overview of the various swarm intelligence algorithms and their advanced developments, and briefly provides the description of their successful applications in optimization problems of engineering fields. Finally, opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.
Cited by 881Open access
Amir Parnianifard · Preprints.org · 2024
This research study focuses on addressing practical optimization challenges in audio-visual content using advanced computational intelligence methods, with a specific emphasis on privacy-preserving issues. The research will be presented as a proposal and can be conducted based on the provided ideas. Computational intelligence encompasses various approaches, including evolutionary algorithms, learning methods, fuzzy systems, and neural networks, which have become essential tools for tackling complex engineering optimization problems due to computational expenses, dimensional complexities, and interdisciplinary integration. The research objectives include a thorough investigation of optimization challenges in audio-visual content, the delineation of problem characteristics, and the development of relevant methodologies based on these specifications. The main goals of the study, titled "Advanced Computational Intelligence Assisted Less-Expensive Privacy-Preserving Optimization," encompass the development of a metamodel-based learning algorithm integrated with evolutionary computation for privacy-preserving optimization, less-expensive signal watermarking optimization using hybrid metamodeling and evolutionary algorithms, and a closed-loop robust intelligent PID control system applied in real-time dynamic signal watermarking. Additional objectives may be defined as the research progresses and further background review is conducted.
Cited by 0Open access
Sergiy Butenko, Pãnos M. Pardalos · 2014
For students in industrial and systems engineering (ISE) and operations research (OR) to understand optimization at an advanced level, they must first grasp the analysis of algorithms, computational complexity, and other concepts and modern developments in numerical methods. Satisfying this prerequisite, Numerical Methods and Optimization: An Intro
Cited by 37
Yunfan Guan, Wei‐Bin Zhang, Wei Wu et al. · 2025
Research on traffic signal timing optimization has evolved over the years, from traditional methods like Webster’s single-point timing calculation and MAXBAND mainline coordination, to advanced techniques using combined algorithms such as genetic algorithms and particle swarm optimization. Typically, traffic simulation software has only been used to verify these optimization algorithms. In this study, we propose a signal timing optimization method that integrates genetic algorithms with simulation. This approach leverages the simulation environment in an iterative validate-optimize cycle, enhancing the role of traffic simulation in the optimization process. The key advantage is that the final optimized solution is tightly linked to the reliability of the simulation software and its real-world applicability. Moreover, the simulated vehicle movement data can be utilized for developing deep learning-based signal timing optimization techniques.
Cited by 0
El‐Ghazali Talbi · ACM Computing Surveys · 2021
In recent years, research in applying optimization approaches in the automatic design of deep neural networks has become increasingly popular. Although various approaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this hot research topic. In this article, we propose a unified way to describe the various optimization algorithms that focus on common and important search components of optimization algorithms: representation, objective function, constraints, initial solution(s), and variation operators. In addition to large-scale search space, the problem is characterized by its variable mixed design space, it is very expensive, and it has multiple blackbox objective functions. Hence, this unified methodology has been extended to advanced optimization approaches, such as surrogate-based, multi-objective, and parallel optimization.
Cited by 42
El‐Ghazali Talbi · LillOA (Université de Lille (University Of Lille)) · 2020
During the last years, research in applying optimization approaches in the automatic design of deep neural networks (DNNs) becomes increasingly popular. Although various appproaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this hot research topic. In this paper, we propose a unified way to describe the various optimization algorithms which focus on common and important search components of optimization algorithms: representation, objective function, constraints, initial solution(s) and variation operators. In addition to large scale search space, the problem is characterized by its variable mixed design space, very expensive and multiple blackbox objective functions. Hence, this unified methodology has been extended to advanced optimization approaches such as surrogate-based, multi-objective and parallel optimization.
Cited by 12Open access
Rakesh Kumar M · International Journal of Artificial Intelligence Data Science and Machine Learning · 2021
High-dimensional data is increasingly prevalent in scientific computing, driven by advancements in data collection technologies and the growing complexity of scientific models. Traditional optimization algorithms often struggle with the curse of dimensionality, leading to inefficiencies and suboptimal solutions. This paper explores advanced optimization algorithms tailored for high-dimensional data, focusing on their applications in scientific computing. We discuss the challenges posed by high-dimensional data, review state-of-the-art optimization techniques, and present case studies from various scientific domains. The paper also includes algorithmic details, performance evaluations, and future research directions
Cited by 0Open access
AmirHossein Mokabberi, Mehdi Golsorkhtabaramiri, Ramzan Abbasnezhad Varzi · 2024
optimization algorithms play a crucial role in solving complex problems in various domains. Single-objective optimization algorithms aim to discover the most optimal solution for a particular objective function, commonly distinguished by a single criterion or goal. Grey Wolf optimizer (GWO) is a swarm-based algorithm that has gained attention due to its simplicity and efficiency in solving optimization problems. In this article, we propose an advanced version of GWO, which is referred to as the Advanced Trending-based Grey Wolf optimizer (ATGWO), specifically tailored for single-objective optimization problems. The motivation behind this modification stems from the need to improve the performance metrics of the original GWO algorithm and avoid local optimum. By altering the algorithm’s coefficients, we aim to enhance its convergence rate, exploration, and exploitation abilities. To evaluate the proposed ATGWO algorithm, we conduct simulations using 7 multimodal single-objective benchmark functions. The results suggest that although the ATGWO excels in accuracy, it has more delay in comparison with GWO. This study paves the way for future research about optimization algorithms.
Cited by 2
Andile Ngcobo, Bongumsa Mendu, Bessie Baakanyang Monchusi · Future Sustainability · 2025
Smart home electric energy management systems are designed to optimize energy efficiency, reduce waste, and promote sustainability. This paper aims to conduct a literature review on optimization and learning algorithms for energy management in smart homes. The research methodology involves retrieving data from the Scopus database and visualizing the information through VOSviewer. The results of the literature reviewed encompass a wide range of strategies and algorithms aimed at optimizing energy management in smart homes. A key noticeable aspect is the diverse application of advanced optimization techniques and machine learning algorithms to enhance energy efficiency, reduce costs, and promote sustainability. The study contributes by providing an in-depth analysis of optimization algorithms for energy cost reduction, evaluating and categorizing learning algorithms, and identifying best practices for optimizing energy consumption in smart homes.
Cited by 3Open access
Mr. Shashanka S · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · 2024
This study presents a comprehensive comparative analysis of strategies for optimizing the strength-to-weight ratio of driver shafts in automobile applications. The research focuses on four key areas: material selection, manufacturing methods, generative design, and topology optimization. Firstly, a range of materials commonly used in driver shaft manufacturing is evaluated, considering factors such as strength, weight, durability.. Various manufacturing methods, including traditional techniques and advanced processes like additive manufacturing, are assessed for their impact on shaft performance. The study also delves into the application of generative design and topology optimization techniques. Generative design algorithms are employed to explore innovative shaft geometries that enhance structural integrity while minimizing weight. Topology optimization algorithms are utilized to optimize material distribution within the shaft, further improving strength-to-weight characteristics. The findings highlight the potential benefits of integrating advanced design methodologies with appropriate material selection and manufacturing processes. This research contributes valuable insights for optimizing driver shafts, enhancing automobile performance, and achieving efficient utilization of materials in automotive engineering. Key Words: Driver Shaft design, Strength and weight optimization, Generative Design, Topology Optimization, Material and manufacturing Consideration.
Cited by 0Open access
2024
META-HEURISTIC ALGORITHMS FOR ADVANCED DISTRIBUTED SYSTEMS Discover a collection of meta-heuristic algorithms for distributed systems in different application domains Meta-heuristic techniques are increasingly gaining favor as tools for optimizing distributed systems—generally, to enhance the utility and precision of database searches. Carefully applied, they can increase system effectiveness, streamline operations, and reduce cost. Since many of these techniques are derived from nature, they offer considerable scope for research and development, with the result that this field is growing rapidly. Meta-Heuristic Algorithms for Advanced Distributed Systems offers an overview of these techniques and their applications in various distributed systems. With strategies based on both global and local searching, it covers a wide range of key topics related to meta-heuristic algorithms. Those interested in the latest developments in distributed systems will find this book indispensable. Meta-Heuristic Algorithms for Advanced Distributed Systems readers will also find: Analysis of security issues, distributed system design, stochastic optimization techniques, and more Detailed discussion of meta-heuristic techniques such as the genetic algorithm, particle swam optimization, and many others Applications of optimized distribution systems in healthcare and other key??industries Meta-Heuristic Algorithms for Advanced Distributed Systems is ideal for academics and researchers studying distributed systems, their design, and their applications.
Cited by 2
Tiantian Niu, Bingyin Lei, Li Guo et al. · Axioms · 2023
In the face of an increasingly high-demand environment for outpatients, achieving a balance between allocation of limited medical resources and patient satisfaction has considerable social and economic benefits. Therefore, appointment scheduling (AS) system operation is used in clinics and hospitals, and its operation optimization research is of great significance. This study reviews the research progress on appointment scheduling system optimization. Firstly, we classify and conclude the existing appointment scheduling system structures and decision-making frameworks. Subsequently, we summarize the system reliability optimization framework from three aspects: appointment scheduling system optimization objectives, decision variables and constraints. Following that, we methodically review the most applied system optimization algorithms in different appointment scheduling systems. Lastly, a literature bibliometric analysis is provided. During our review of the literature, we observe that (1) optimization methods in ASs predominantly involve the application of genetic algorithms and simulation optimization algorithms; (2) neural networks and deep learning methods are core technologies in health management optimization; (3) a bibliometric analysis reveals a heightened interest in the optimization technology of ASs within China compared to other nations; and (4) further advancements are essential in the comprehensive optimization of the system, exploration of practical usage scenarios, and the application of advanced simulation and modeling techniques in this research.
Cited by 14Open access
Wei-Chiang Hong (Ed.) · Directory of Open access Books (OAPEN Foundation) · 2018
More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers.This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy.
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David Padua · 2016
Summary form only given. High-performing irregular algorithms are typically implemented using simple operations and conventional control structures. In addition, due to today's compilers inability to manipulate these implementations, program tuning must usually be done by hand. Better notations and automatic optimization would help improve programmer productivity, portability, and maintainability. This talk will review high-level notation proposals for the description of irregular algorithms, as well as compiler and autotuning techniques for the optimization of these algorithms. A short discussion of open research problems and necessary conditions for adoption of these more advanced notations and strategies will conclude the presentation.
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Yogini N. Bhosale, Ramchandra P. Hasabe, Chandrakant L. Bhattar · 2025
This Directional Overcurrent Relays (DOCRs) are essential components in active distribution power systems, safeguarding the grid by identifying faults and isolating affected sections. Ensuring reliable DOCR coordination is critical for achieving selective and effective protection during fault events. This review systematically examines a variety of optimization techniques employed for DOCR coordination as documented in existing literature. The study spans from conventional methods to advanced algorithms, showcasing diverse strategies to attain optimal coordination. Additionally, it explores the application of optimization approaches such as genetic algorithms, particle swarm optimization, and invasive weed optimization. By outlining current advancements and identifying potential research directions, this review provides valuable insights into the evolving field of DOCR coordination
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Changliang Zhu, Emmanuel Anuoluwa Bamidele, Xiangying Shen et al. · Chemical Reviews · 2024
Artificial Intelligence (AI) has advanced material research that were previously intractable, for example, the machine learning (ML) has been able to predict some unprecedented thermal properties. In this review, we first elucidate the methodologies underpinning discriminative and generative models, as well as the paradigm of optimization approaches. Then, we present a series of case studies showcasing the application of machine learning in thermal metamaterial design. Finally, we give a brief discussion on the challenges and opportunities in this fast developing field. In particular, this review provides: (1) Optimization of thermal metamaterials using optimization algorithms to achieve specific target properties. (2) Integration of discriminative models with optimization algorithms to enhance computational efficiency. (3) Generative models for the structural design and optimization of thermal metamaterials.
Cited by 129Open access