Relevant bibliographies by topics / Multi-Objective Optimization; Intelligent Optimization Algorithms; Trade off methods

Contents

  1. Journal articles
  2. Book chapters
  3. Conference papers
  4. Reports

Academic literature on the topic 'Multi-Objective Optimization; Intelligent Optimization Algorithms; Trade off methods'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Multi-Objective Optimization; Intelligent Optimization Algorithms; Trade off methods.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Multi-Objective Optimization; Intelligent Optimization Algorithms; Trade off methods"

1

Burak, Kürşat Gül, and Taşpınar Necmi. "Usage of multi-objective genetic and multi-objective differential evolution algorithms on energy and spectral efficiencies in massive MIMO systems." Global Journal of Engineering and Technology Advances 5, no. 1 (2020): 018–24. https://doi.org/10.5281/zenodo.4467654.

Full text
Abstract:
There is a significant increase in the use of wireless communication and it is expected that this increase will continue progressively. In the near future, cellular network technologies are expected to be capable of increasing the area throughput hundreds of times in order to cope with the increase in data traffic. Increasing spectral efficiency (SE) with massive multi-input multi-output (Massive MIMO) systems is one of the main methods used to meet these expectations. SE means the amount of information transmitted successfully with each complex sample. Increasing the transmission power and th
APA, Harvard, Vancouver, ISO, and other styles
2

R., Subramani*1 and C. Vijayalakshmi2. "MULTI-OBJECTIVE OPTIMIZATION ALGORITHMS AND PERFORMANCE TEST FUNCTIONS FOR ENERGY EFFICIENCY : REVIEW." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 10 (2017): 39–52. https://doi.org/10.5281/zenodo.1002626.

Full text
Abstract:
The application of multiobjective optimization is currently receiving growing interest from researchers with various backgrounds. Most research in this area has understandably concentrated on the selection stage, due to the need to integrate vectorial performance measures with the inherently scalar way in which multi objective reward individual performance. In this review, current multiobjective approaches are discussed, ranging from the conventional analytical aggregation of the different objectives into a single function to a number of population-based approaches and the more recent ranking
APA, Harvard, Vancouver, ISO, and other styles
3

Burak Kürşat Gül and Necmi Taşpınar. "Usage of multi-objective genetic and multi-objective differential evolution algorithms on energy and spectral efficiencies in massive MIMO systems." Global Journal of Engineering and Technology Advances 5, no. 1 (2020): 018–24. http://dx.doi.org/10.30574/gjeta.2020.5.1.0079.

Full text
Abstract:
There is a significant increase in the use of wireless communication and it is expected that this increase will continue progressively. In the near future, cellular network technologies are expected to be capable of increasing the area throughput hundreds of times in order to cope with the increase in data traffic. Increasing spectral efficiency (SE) with massive multi-input multi-output (Massive MIMO) systems is one of the main methods used to meet these expectations. SE means the amount of information transmitted successfully with each complex sample. Increasing the transmission power and th
APA, Harvard, Vancouver, ISO, and other styles
4

Belakaria, Syrine, Aryan Deshwal, and Janardhan Rao Doppa. "Output Space Entropy Search Framework for Multi-Objective Bayesian Optimization." Journal of Artificial Intelligence Research 72 (November 2, 2021): 667–715. http://dx.doi.org/10.1613/jair.1.12966.

Full text
Abstract:
We consider the problem of black-box multi-objective optimization (MOO) using expensive function evaluations (also referred to as experiments), where the goal is to approximate the true Pareto set of solutions by minimizing the total resource cost of experiments. For example, in hardware design optimization, we need to find the designs that trade-off performance, energy, and area overhead using expensive computational simulations. The key challenge is to select the sequence of experiments to uncover high-quality solutions using minimal resources. In this paper, we propose a general framework f
APA, Harvard, Vancouver, ISO, and other styles
5

Gao, Yisheng, Jiahe Liu, and Jiaqi Lu. "A Study on Multi-Stage Assembly Production Testing Decision Based on Binary-Coded Genetic Algorithm." Highlights in Science, Engineering and Technology 142 (May 18, 2025): 382–90. https://doi.org/10.54097/3djjjg42.

Full text
Abstract:
In the current manufacturing industry, intelligent optimization algorithms provide new ideas for quality control of multi-stage assembly production of complex products. However, the traditional inspection strategies and coding methods make it difficult to balance the inspection cost and product quality effectively. Aiming at this challenge, this paper proposes a multi-stage inspection decision model based on a binary coding genetic algorithm, which constructs a nonlinear profit maximization objective function by introducing 0-1 variables to describe the discrete decisions of spare parts inspec
APA, Harvard, Vancouver, ISO, and other styles
6

Ajeigbe, Olusayo Adekunle, Abiodun Ladanu Ajala, Omotunde Abiodun Ojo, and Ismail Adewale Salau. "Multi-agent System Optimal Expansion Planning for Management of Renewable Distributed Generations." Journal of Engineering Research and Reports 27, no. 6 (2025): 162–82. https://doi.org/10.9734/jerr/2025/v27i61536.

Full text
Abstract:
Nigeria's centralized electrical infrastructure has regularly failed to satisfy the country's rising energy consumption, therefore impeding technical advancement and economic growth. Renewable Distributed Generations (RDGs) including solar PV, wind, and small hydro provide a sustainable route toward distributed energy access and enhanced grid dependability when combined. On operational stability, coordination, and growth planning, however, the variability and spatial dispersion of RDGs present difficult problems. Leveraging intelligent, autonomous agents representing smart microgrids, generati
APA, Harvard, Vancouver, ISO, and other styles
7

Tan, K. C., E. F. Khor, T. H. Lee, and R. Sathikannan. "An Evolutionary Algorithm with Advanced Goal and Priority Specification for Multi-objective Optimization." Journal of Artificial Intelligence Research 18 (February 1, 2003): 183–215. http://dx.doi.org/10.1613/jair.842.

Full text
Abstract:
This paper presents an evolutionary algorithm with a new goal-sequence domination scheme for better decision support in multi-objective optimization. The approach allows the inclusion of advanced hard/soft priority and constraint information on each objective component, and is capable of incorporating multiple specifications with overlapping or non-overlapping objective functions via logical 'OR' and 'AND' connectives to drive the search towards multiple regions of trade-off. In addition, we propose a dynamic sharing scheme that is simple and adaptively estimated according to the on-line popul
APA, Harvard, Vancouver, ISO, and other styles
8

Lisowski, Józef. "Comparison of Multi-Object Control Methods Using Multi-Objective Optimization." Electronics 12, no. 20 (2023): 4198. http://dx.doi.org/10.3390/electronics12204198.

Full text
Abstract:
The aim of this work is to obtain multi-objective linear programming algorithms that can be used to solve the global problem of multi-object safety control processes in order to minimize the risk of collisions. In multi-objective linear optimization models, satisfactory trade-off assesses and resolves the conflict between different control objectives. A comparison of single-, bi-, and tri-objective linear programming algorithms allows us to adapt the appropriate optimization method to the conditions of the control process. An important outcome of the present research is the demonstration of th
APA, Harvard, Vancouver, ISO, and other styles
9

Karambeigi, Mohammad Saber, Ali Haghighi Asl, and Masoud Nasiri. "Multi-Objective Optimization of Microemulsion Flooding for Chemical Enhanced Oil Recovery." Oil & Gas Sciences and Technology – Revue d’IFP Energies nouvelles 73 (2018): 4. http://dx.doi.org/10.2516/ogst/2017032.

Full text
Abstract:
Microemulsion flooding is one of the most effective methods of Chemical Enhanced Oil Recovery (CEOR), particularly for the production of residual oil trapped in unconventional reservoirs. A critical step for successful application of this technique is to achieve a suitable formulation. Previous studies have almost focused on the technical aspects while considering both practical and economic matters as conflicting objectives has been neglected. In the present paper, the formulation of microemulsion is optimized based on the trade-off between scientific and financial responses using a hybrid wo
APA, Harvard, Vancouver, ISO, and other styles
10

Zhang, Fan, and Zhongsheng Chen. "A Novel Reinforcement Learning-Based Particle Swarm Optimization Algorithm for Better Symmetry between Convergence Speed and Diversity." Symmetry 16, no. 10 (2024): 1290. http://dx.doi.org/10.3390/sym16101290.

Full text
Abstract:
This paper introduces a novel Particle Swarm Optimization (RLPSO) algorithm based on reinforcement learning, embodying a fundamental symmetry between global and local search processes. This symmetry aims at addressing the trade-off issue between convergence speed and diversity in traditional algorithms. Traditional Particle Swarm Optimization (PSO) algorithms often struggle to maintain good convergence speed and particle diversity when solving multi-modal function problems. To tackle this challenge, we propose a new algorithm that incorporates the principles of reinforcement learning, enabling
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Multi-Objective Optimization; Intelligent Optimization Algorithms; Trade off methods"

1

Dixit, Gaurav, and Kagan Tumer. "Objective-Informed Diversity for Multi-Objective Multiagent Coordination." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240923.

Full text
Abstract:
To coordinate in multiagent settings characterized by multiple objectives, asymmetric agents (agents with distinct capabilities and preferences) must learn diverse behaviors to balance trade-offs between agent-specific and team objectives. Hierarchical methods partially address this by leveraging a combination of Quality-Diversity methods that illuminate the behavior space and evolutionary algorithms that use non-dominated sorting over the explored behaviors to improve coverage in the objective space. However, optimizing diverse behaviors and trade-offs in isolation is susceptible to producing
APA, Harvard, Vancouver, ISO, and other styles
2

Bhimavarapu, Usharani. "Exploring CMA-ES for Energy and Carbon Emission Optimization in Green Technologies." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-8034-5.ch011.

Full text
Abstract:
Reducing the carbon footprint has become a critical objective in the context of global sustainability efforts. One promising avenue to achieve this goal is through the optimization of energy consumption and resource allocation in various industries. In this study, the authors propose the use of covariance matrix adaptation evolution strategy (CMA-ES), a robust optimization algorithm, to address complex, non-linear problems in reducing carbon emissions. CMA-ES is particularly suited for continuous optimization tasks where traditional gradient-based methods may fail. By adapting the covariance m
APA, Harvard, Vancouver, ISO, and other styles
3

Man-Im, Anongpun, Weerakorn Ongsakul, and Nimal Madhu M. "Heuristic Optimization Algorithms for Power System Scheduling Applications." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1192-3.ch012.

Full text
Abstract:
Power system scheduling is one of the most complex multi-objective scheduling problems, and a heuristic optimization method is designed for finding the OPF solution. Stochastic weight trade-off chaotic mutation-based non-dominated sorting particle swarm optimization algorithm can improve solution-search-capability by balancing between global best exploration and local best utilization through the stochastic weight and dynamic coefficient trade-off methods. This algorithm with chaotic mutation enhances diversity and search-capability, preventing premature convergence. Non-dominated sorting and
APA, Harvard, Vancouver, ISO, and other styles
4

Ahmed, Abdelrahman A. Mohamed, and Mohamed Hassan Gadallah. "Improved Strength Pareto Global Adaptive Multi-Objective Optimization Search and Selection Algorithm (ISPGAEA)." In Practice, Progress, and Proficiency in Sustainability. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-7117-6.ch009.

Full text
Abstract:
Meta heuristic hybrid search optimization methods combine advantages of several methods in hybrid one. randomly generated solutions are archived, assessing diversity and convergence. However, after several cycles, random generated numbers tend to cluster rather than fill the search space. Uniform random number generators for initializing meta heuristic algorithms have drawbacks like uneven distribution, high disparities, and ineffective search space coverage. Control parameters in Genetic Algorithms, such as crossover and mutation, can impact the trade-off solutions of search space. The resear
APA, Harvard, Vancouver, ISO, and other styles
5

Jiaxin, Hu, Zhao Demin, and Zhu Yanwei. "A Dynamic Mission Planning Method of Multi-Satellite Cooperative Observation for Highly Time-Sensitive Targets." In Advances in Transdisciplinary Engineering. IOS Press, 2025. https://doi.org/10.3233/atde250060.

Full text
Abstract:
To address the challenges posed by highly time-sensitive targets with uncertainty and unpredictability in multi-satellite cooperative observation, conventional intelligent optimization algorithms often suffer from time-consuming and unstable issues. This paper presents a novel dynamic mission planning method that cleverly incorporates waiting time for tracking and tracked time as heuristics to guide task assignment. Furthermore, plannable and robustness indicators are introduced to facilitate global trade-offs in resource conflicts among multiple targets. Initially, the constraints specificall
APA, Harvard, Vancouver, ISO, and other styles
6

Elhosni, Soumaya, and Sami Faiz. "GIS-Based Evolutionary Approaches Using Multiple-Criteria Decision Analysis for Spatial Issues." In Interdisciplinary Approaches to Spatial Optimization Issues. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-1954-7.ch003.

Full text
Abstract:
Geographic information systems (GIS) have been considered as good decision support tools to provide the decision maker (DM). However, their spatial data functionalities fail to provide any report about the potentials of the information and cannot make rational choice between conflicting alternatives. Literature review shows that the integration of GIS with multiple-criteria decision analysis (MCDA) makes GIS more robust in decision making process. While MCDA are used to support DMs to deal and solve spatial multi-objective optimisation problems (SMOPs), the use of their methods are suited for
APA, Harvard, Vancouver, ISO, and other styles
7

Kalia, Harihar, Satchidananda Dehuri, and Ashish Ghosh. "Multi-Objective Genetic and Fuzzy Approaches in Rule Mining Problem of Knowledge Discovery in Databases." In Global Trends in Intelligent Computing Research and Development. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4936-1.ch007.

Full text
Abstract:
Knowledge Discovery in Databases (KDD) is the process of automatically searching patterns from large volumes of data by using specific data mining techniques. Classification, association, and associative classification (integration of classification and association) rule mining are popularly used rule mining techniques in KDD for harvesting knowledge in the form of rule. The classical rule mining techniques based on crisp sets have bad experience of “sharp boundary problems” while mining rule from numerical data. Fuzzy rule mining approaches eliminate these problems and generate more human und
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Multi-Objective Optimization; Intelligent Optimization Algorithms; Trade off methods"

1

Watanabe, Shuhei, Noor Awad, Masaki Onishi, and Frank Hutter. "Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen Estimator." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/487.

Full text
Abstract:
Hyperparameter optimization (HPO) is a vital step in improving performance in deep learning (DL). Practitioners are often faced with the trade-off between multiple criteria, such as accuracy and latency. Given the high computational needs of DL and the growing demand for efficient HPO, the acceleration of multi-objective (MO) optimization becomes ever more important. Despite the significant body of work on meta-learning for HPO, existing methods are inapplicable to MO tree-structured Parzen estimator (MO-TPE), a simple yet powerful MO-HPO algorithm. In this paper, we extend TPE’s acquisition f
APA, Harvard, Vancouver, ISO, and other styles
2

Osika, Zuzanna, Jazmin Zatarain Salazar, Diederik M. Roijers, Frans A. Oliehoek, and Pradeep K. Murukannaiah. "What Lies beyond the Pareto Front? A Survey on Decision-Support Methods for Multi-Objective Optimization." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/755.

Full text
Abstract:
We present a review that unifies decision-support methods for exploring the solutions produced by multi-objective optimization (MOO) algorithms. As MOO is applied to solve diverse problems, approaches for analyzing the trade-offs offered by these algorithms are scattered across fields. We provide an overview of the current advances on this topic, including methods for visualization, mining the solution set, and uncertainty exploration as well as emerging research directions, including interactivity, explainability, and support on ethical aspects. We synthesize these methods drawing from differ
APA, Harvard, Vancouver, ISO, and other styles
3

Zhang, Yulun, He Jiang, Varun Bhatt, Stefanos Nikolaidis, and Jiaoyang Li. "Guidance Graph Optimization for Lifelong Multi-Agent Path Finding." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/35.

Full text
Abstract:
We study how to use guidance to improve the throughput of lifelong Multi-Agent Path Finding (MAPF). Previous studies have demonstrated that, while incorporating guidance, such as highways, can accelerate MAPF algorithms, this often results in a trade-off with solution quality. In addition, how to generate good guidance automatically remains largely unexplored, with current methods falling short of surpassing manually designed ones. In this work, we introduce the guidance graph as a versatile representation of guidance for lifelong MAPF, framing Guidance Graph Optimization as the task of optimi
APA, Harvard, Vancouver, ISO, and other styles
4

Vabø, Jon Gustav, Evan Thomas Delaney, Tom Savel, and Norbert Dolle. "Novel Application of Artificial Intelligence with Potential to Transform Well Planning Workflows on the Norwegian Continental Shelf." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206339-ms.

Full text
Abstract:
Abstract This paper describes the transformational application of Artificial Intelligence (AI) in Equinor's annual well planning and maturation process. Well planning is a complex decision-making process, like many other processes in the industry. There are thousands of choices, conflicting business drivers, lots of uncertainty, and hidden bias. These complexities all add up, which makes good decision making very hard. In this application, AI has been used for automated and unbiased evaluation of the full solution space, with the objective to optimize the selection of drilling campaigns while
APA, Harvard, Vancouver, ISO, and other styles
5

Arroyos, Marina Roche, Javier Arturo Corea Araujo, Didac Sabria, Vinayak Padmaji, Pablo Cano, and Patrice Garmier. "Model based component co-optimization and scalability of virtual testing for electric drivetrain vehicle." In FISITA World Congress 2021. FISITA, 2021. http://dx.doi.org/10.46720/f2021-dgt-045.

Full text
Abstract:
"Within the automotive product development cycle virtual and heterogeneous testing is becoming increasingly established through component, module and vehicle-level simulation. Though a number of standards in this field have been established, models are still mostly created in a fragmented manner: using domain-specific tools to create, manage and execute simulations without standardization of the content of the functional interfaces (FMI does only standardize the format) and limited scalability. This fragmentation leads to a lot of redundant effort as models of the same component or system are
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Multi-Objective Optimization; Intelligent Optimization Algorithms; Trade off methods"

1

Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

Full text
Abstract:
Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines traject
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Multi-Objective Optimization; Intelligent Optimization Algorithms; Trade off methods' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography