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

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

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Published: 3 June 2025

Last updated: 31 July 2025

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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Ramprabhu, T., Vimal Savsani, Sohil Parsana, Nishil Radadia, Mohak Sheth, and Nisarg Sheth. "Multi-Objective Optimization of EDM Process Parameters by Using Passing Vehicle Search (PVS) Algorithm." Defect and Diffusion Forum 382 (January 2018): 138–46. http://dx.doi.org/10.4028/www.scientific.net/ddf.382.138.

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Electro-Discharge Machining (EDM) is very popular for machining high-strength conductive materials for aerospace and automotive application. These machining involve a range of processing parameters. In order to optimize these for the best performance, a trade-off has to be decided for the responses achieved through machining. Conventional algorithms have long been replaced by advanced optimization algorithms. Performance of meta-heuristic algorithms in relation to traditional deterministic approaches for multi-modal, non-linear engineering problems is very promising in recent days. In this pap

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Chen, Haitao, Nishi Chu та Aiqing Kang. "Application of Adaptive ε-IZOA-Based Optimization Algorithm in the Optimal Scheduling of Reservoir Clusters". Water 17, № 9 (2025): 1274. https://doi.org/10.3390/w17091274.

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Increasing environmental variability and operational complexity in reservoir systems necessitate advanced optimization frameworks for flood control. This study proposes the ε-constrained Improved Zebra Optimization Algorithm (ε-IZOA), a novel metaheuristic algorithm integrating an enhanced Zebra Optimization Algorithm (ZOA) with adaptive ε-constraint handling, to address multi-reservoir flood control optimization. Three strategic modifications advance the standard ZOA: (1) Bernoulli chaotic mapping for diversified population initialization; (2) adaptive weight balancing for exploration-exploit

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Gül, Burak Kürşat, and Necmi Taşpınar. "Spectral and energy efficiency trade-off in massive MIMO systems using multi-objective bat algorithm." Journal of Electrical Engineering 73, no. 2 (2022): 132–39. http://dx.doi.org/10.2478/jee-2022-0017.

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Abstract The rise in the usage of wireless communication increases the cellular communication by the same rate. With the continuation of this situation, the density in data traffic has the potential to cause problems in the near future. Coping with spectral efficiency-energy efficiency trade-off using massive MIMO systems is considered to be a reasonable solution to this problem. In this paper, cellular communication simulations were performed in cases with different number of users, number of antennas and transmission power of massive MIMO systems and then non-dominated solutions are determin

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Babaei, Mehdi, and Masoud Mollayi. "MULTI-OBJECTIVE OPTIMIZATION OF REINFORCED CONCRETE FRAMES USING NSGA-II ALGORITHM." Engineering Structures and Technologies 8, no. 4 (2016): 157–64. http://dx.doi.org/10.3846/2029882x.2016.1250230.

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In recent decades, the use of genetic algorithm (GA) for optimization of structures has been highly attractive in the study of concrete and steel structures aiming at weight optimization. However, it has been challenging for multi-objective optimization to determine the trade-off between objective functions and to obtain the Pareto-front for reinforced concrete (RC) and steel structures. Among different methods introduced for multi-objective optimization based on genetic algorithms, Non-Dominated Sorting Genetic Algorithm II (NSGA II) is one of the most popular algorithms. In this paper, multi

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Li, Rong, Zalina Shari, and Mohd Zainal Abidin Ab Kadir. "A conceptual framework for multi-objective optimization of building performance: Integrating intelligent algorithms, simulation tools, and climate adaptation." Multidisciplinary Reviews 8, no. 12 (2025): 2025385. https://doi.org/10.31893/multirev.2025385.

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This study systematically examined recent research trends in multi-objective optimization (MOO) for building performance from 2020 to 2024 and proposed a conceptual framework integrating intelligent algorithms, simulation tools, and climate adaptation strategies. A thematic analysis of 40 peer-reviewed articles was conducted using ATLAS.ti, revealing three dominant research themes: intelligent algorithms, building performance simulation techniques, and adaptive design for climate change. Quantitative analysis highlights China’s prominent contributions to the field, while the thematic analysis

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Gorzałczany, M. B., and F. Rudziński. "Handling fuzzy systems’ accuracy-interpretability trade-off by means of multi-objective evolutionary optimization methods – selected problems." Bulletin of the Polish Academy of Sciences Technical Sciences 63, no. 3 (2015): 791–98. http://dx.doi.org/10.1515/bpasts-2015-0090.

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Abstract The paper addresses several open problems regarding the automatic design of fuzzy rule-based systems (FRBSs) from data using multi-objective evolutionary optimization algorithms (MOEOAs). In particular, we propose: a) new complexity-related interpretability measure, b) efficient strong-fuzzy-partition implementation for improving semantics-related interpretability, c) special-coding-free implementation of rule base and original genetic operators for its processing, and d) implementation of our ideas in the context of well-known MOEOAs such as SPEA2 and NSGA-II. The experiments demonst

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Wang, Chun, Zhicheng Ji, and Yan Wang. "Many-objective flexible job shop scheduling using NSGA-III combined with multi-attribute decision making." Modern Physics Letters B 32, no. 34n36 (2018): 1840110. http://dx.doi.org/10.1142/s0217984918401103.

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This paper considers many-objective flexible job shop scheduling problem (MaOFJSP) in which the number of optimization problems is larger than three. An integrated multi-objective optimization method is proposed which contains both optimization and decision making. The non-dominated sorting genetic algorithm III (NSGA-III) is utilized to find a trade-off solution set by simultaneously optimizing six objectives including makespan, workload balance, mean of earliness and tardiness, cost, quality, and energy consumption. Then, an integrated multi-attribute decision-making method is introduced to

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Dwivedi, Praveen Kumar, and Surya Prakash Tripathi. "A Review of Multi-Objective Evolutionary Based Fuzzy Classifiers." Recent Advances in Computer Science and Communications 13, no. 1 (2020): 77–85. http://dx.doi.org/10.2174/2213275912666190410142052.

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Background: Fuzzy systems are employed in several fields like data processing, regression, pattern recognition, classification and management as a result of their characteristic of handling uncertainty and explaining the feature of the advanced system while not involving a particular mathematical model. Fuzzy rule-based systems (FRBS) or fuzzy rule-based classifiers (mainly designed for classification purpose) are primarily the fuzzy systems that consist of a group of fuzzy logical rules and these FRBS are unit annexes of ancient rule-based systems, containing the "If-then" rules. During the d

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Wadood, Abdul, Al-Fahad Yousaf, and Aadel Mohammed Alatwi. "An Enhanced Multiple Unmanned Aerial Vehicle Swarm Formation Control Using a Novel Fractional Swarming Strategy Approach." Fractal and Fractional 8, no. 6 (2024): 334. http://dx.doi.org/10.3390/fractalfract8060334.

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This paper addresses the enhancement of multiple Unmanned Aerial Vehicle (UAV) swarm formation control in challenging terrains through the novel fractional memetic computing approach known as fractional-order velocity-pausing particle swarm optimization (FO-VPPSO). Existing particle swarm optimization (PSO) algorithms often suffer from premature convergence and an imbalanced exploration–exploitation trade-off, which limits their effectiveness in complex optimization problems such as UAV swarm control in rugged terrains. To overcome these limitations, FO-VPPSO introduces an adaptive fractional

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Jalilzadehazhari, Vadiee, and Johansson. "Achieving a Trade-Off Construction Solution Using BIM, an Optimization Algorithm, and a Multi-Criteria Decision-Making Method." Buildings 9, no. 4 (2019): 81. http://dx.doi.org/10.3390/buildings9040081.

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The Energy Performance of Building Directive obligated all European countries to reduce the energy requirements of buildings while simultaneously improving indoor environment quality. Any such improvements not only enhance the health of the occupants and their productivity, but also provide further economic benefits at the national level. Accomplishing this task requires a method that allows building professionals to resolve conflicts between visual and thermal comfort, energy demands, and life-cycle costs. To overcome these conflicts, this study exploits the incorporation of building informat

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He, Yifan, and Claus Aranha. "Solving Portfolio Optimization Problems Using MOEA/D and Lévy Flight." Advances in Data Science and Adaptive Analysis 12, no. 03n04 (2020): 2050005. http://dx.doi.org/10.1142/s2424922x20500059.

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Portfolio optimization is a financial task which requires the allocation of capital on a set of financial assets to achieve a better trade-off between return and risk. To solve this problem, recent studies applied multi-objective evolutionary algorithms (MOEAs) for its natural bi-objective structure. This paper presents a method injecting a distribution-based mutation method named Lévy Flight into a decomposition based MOEA named MOEA/D. The proposed algorithm is compared with three MOEA/D-like algorithms, NSGA-II, and other distribution-based mutation methods on five portfolio optimization be

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Tran, Duc-Hoc, Jui-Sheng Chou, and Duc-Long Luong. "MULTI-OBJECTIVE SYMBIOTIC ORGANISMS OPTIMIZATION FOR MAKING TIME-COST TRADEOFFS IN REPETITIVE PROJECT SCHEDULING PROBLEM." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 25, no. 4 (2019): 322–39. http://dx.doi.org/10.3846/jcem.2019.9681.

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Time-cost problems that arise in repetitive construction projects are commonly encountered in project scheduling. Numerous time-cost trade-off approaches, such as mathematical, metaheuristic, and evolutionary methods, have been extensively studied in the construction community. Currently, the scheduling of a repetitive project is conducted using the traditional precedence diagramming method (PDM), which has two fundamental limitations: (1) progress is assumed to be linear from start to finish; and (2) activities in the schedule are connected each other only at the end points. This paper propos

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Farid, Mazen, Heng Siong Lim, Chin Poo Lee, and Rohaya Latip. "Scheduling Scientific Workflow in Multi-Cloud: A Multi-Objective Minimum Weight Optimization Decision-Making Approach." Symmetry 15, no. 11 (2023): 2047. http://dx.doi.org/10.3390/sym15112047.

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One of the most difficult aspects of scheduling operations on virtual machines in a multi-cloud environment is determining a near-optimal permutation. This task requires assigning various computing jobs with competing objectives to a collection of virtual machines. A significant number of NP-hard problem optimization methods employ multi-objective algorithms. As a result, one of the most successful criteria for discovering the best Pareto solutions is Pareto dominance. In this study, the Pareto front is calculated using a novel multi-objective minimum weight approach. In particular, we use par

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Elmadina, Nahla Nur, Rashid A. Saeed, Elsadig Saeid, Ala Eldin Awouda, Hana M. Mujlid, and Hashim Elshafie. "Adaptive Trajectory Optimization for UAV-IRS Systems in 6G Thz Networks Using Multi Agent-DRL." Transport and Telecommunication Journal 26, no. 2 (2025): 133–48. https://doi.org/10.2478/ttj-2025-0011.

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Abstract Future 6th Generation (6G) networks will rely on Terahertz (THz) wireless communication as their main enabler for delivering both ultra-high data speed and minimal delay. THz wireless systems become crucial for upcoming communications by using Unmanned Aerial Vehicles (UAVs) together with Intelligent Reflecting Surfaces (IRS) while improving reliability and efficiency. In UAV-IRS-assisted networks, minimizing mission completion time and energy consumption is critical. However, achieving rapid mission execution often requires UAVs to operate at higher speeds, increasing energy usage an

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Yao, Peng, Xingfeng Duan, and Jiale Tang. "An improved gray wolf optimization to solve the multi-objective tugboat scheduling problem." PLOS ONE 19, no. 2 (2024): e0296966. http://dx.doi.org/10.1371/journal.pone.0296966.

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With the continuous prosperity of maritime transportation on a global scale and the resulting escalation in port trade volume, tugboats assume a pivotal role as essential auxiliary tools influencing the ingress and egress of vessels into and out of ports. As a result, the optimization of port tug scheduling becomes of paramount importance, as it contributes to the heightened efficiency of ship movements, cost savings in port operations, and the promotion of sustainable development within the realm of maritime transportation. However, a majority of current tugboat scheduling models tend to focu

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Mehta, Sumet, Fei Han, Muhammad Sohail, et al. "Gene selection based on adaptive neighborhood-preserving multi-objective particle swarm optimization." PeerJ Computer Science 11 (May 28, 2025): e2872. https://doi.org/10.7717/peerj-cs.2872.

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The analysis of high-dimensional microarray gene expression data presents critical challenges, including excessive dimensionality, increased computational burden, and sensitivity to random initialization. Traditional optimization algorithms often produce inconsistent and suboptimal results, while failing to preserve local data structures limiting both predictive accuracy and biological interpretability. To address these limitations, this study proposes an adaptive neighborhood-preserving multi-objective particle swarm optimization (ANPMOPSO) framework for gene selection. ANPMOPSO introduces fo

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Li, Qin, Zhou, Cheng, Zhang, and Ai. "Intelligent Rapid Adaptive Offloading Algorithm for Computational Services in Dynamic Internet of Things System." Sensors 19, no. 15 (2019): 3423. http://dx.doi.org/10.3390/s19153423.

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As restricted resources have seriously limited the computational performance of massive Internet of things (IoT) devices, better processing capability is urgently required. As an innovative technology, multi-access edge computing can provide cloudlet capabilities by offloading computation-intensive services from devices to a nearby edge server. This paper proposes an intelligent rapid adaptive offloading (IRAO) algorithm for a dynamic IoT system to increase overall computational performance and simultaneously keep the fairness of multiple participants, which can achieve agile centralized contr

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Chauhan Priyank Hasmukhbhai and Ritu Khanna. "Multi-objective optimization of lithography alignment using fractional dynamics and fuzzy meta-goal programming." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 2179–208. https://doi.org/10.30574/wjaets.2025.15.3.1150.

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Lithography alignment in semiconductor manufacturing demands nanometer-scale precision amidst inherent challenges such as mechanical vibrations, thermal drift, and actuator nonlinearities. Traditional integer-order control strategies often fail to optimally balance competing objectives like positional accuracy, settling time, and energy efficiency. This paper introduces a Fuzzy Meta Goal Programming (FMGP) framework integrated with fractional calculus to address these limitations. The alignment process is modeled using a fractional-order differential equation (FDE) governed by the Caputo deriv

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Alruwaili, Mohammed. "A Multi-Objective Optimization Framework for Robust and Accurate Photovoltaic Model Parameter Identification Using a Novel Parameterless Algorithm." Processes 13, no. 7 (2025): 2111. https://doi.org/10.3390/pr13072111.

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Photovoltaic (PV) models are hard to optimize due to their intrinsic complexity and changing operation conditions. Root mean square error (RMSE) is often given precedence in classic single-objective optimization methods, limiting them to address the intricate nature of PV model calibration. To bypass these limitations, this research proposes a novel multi-objective optimization framework balancing accuracy and robustness by considering both maximum error and the L2 norm as significant objective functions. Along with that, we introduce the Random Search Around Bests (RSAB) algorithm, which is a

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Dong, Yuxuan, Helai Huang, Gongquan Zhang, and Jieling Jin. "Adaptive Transit Signal Priority Control for Traffic Safety and Efficiency Optimization: A Multi-Objective Deep Reinforcement Learning Framework." Mathematics 12, no. 24 (2024): 3994. https://doi.org/10.3390/math12243994.

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This study introduces a multi-objective deep reinforcement learning (DRL)-based adaptive transit signal priority control framework designed to enhance safety and efficiency in mixed-autonomy traffic environments. The framework utilizes real-time data from connected and automated vehicles (CAVs) to define states, actions, and rewards, with traffic conflicts serving as the safety reward and vehicle waiting times as the efficiency reward. Transit signal priority strategies are incorporated, assigning weights based on vehicle type and passenger capacity to balance these competing objectives. Simul

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Gorzałczany, Marian B., and Filip Rudziński. "Energy Consumption Prediction in Residential Buildings—An Accurate and Interpretable Machine Learning Approach Combining Fuzzy Systems with Evolutionary Optimization." Energies 17, no. 13 (2024): 3242. http://dx.doi.org/10.3390/en17133242.

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This paper addresses the problem of accurate and interpretable prediction of energy consumption in residential buildings. The solution that we propose in this work employs the knowledge discovery machine learning approach combining fuzzy systems with evolutionary optimization. The contribution of this work is twofold, including both methodology and experimental investigations. As far as methodological contribution is concerned, in this paper, we present an original designing procedure of fuzzy rule-based prediction systems (FRBPSs) for accurate and transparent energy consumption prediction in

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Gheouany, Saad, Hamid Ouadi, and Saida El Bakali. "Hybrid-integer algorithm for a multi-objective optimal home energy management system." Clean Energy 7, no. 2 (2023): 375–88. http://dx.doi.org/10.1093/ce/zkac082.

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Abstract Most of the energy produced in the world is consumed by commercial and residential buildings. With the growth in the global economy and world demographics, this energy demand has become increasingly important. This has led to higher unit electricity prices, frequent stresses on the main electricity grid and carbon emissions due to inefficient energy management. This paper presents an energy-consumption management system based on time-shifting of loads according to the dynamic day-ahead electricity pricing. This simultaneously reduces the electricity bill and the peaks, while maintaini

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Jan, Tony, Pegah Azami, Saeid Iranmanesh, Omid Ameri Sianaki, and Shiva Hajiebrahimi. "Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City." Sensors 20, no. 8 (2020): 2276. http://dx.doi.org/10.3390/s20082276.

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Traffic control is one of the most challenging issues in metropolitan cities with growing populations and increased travel demands. Poor traffic control can result in traffic congestion and air pollution that can lead to health issues such as respiratory problems, asthma, allergies, anxiety, and stress. The traffic congestion can also result in travel delays and potential obstruction of emergency services. One of the most well-known traffic control methods is to restrict and control the access of private vehicles in predetermined regions of the city. The aim is to control the traffic load in o

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Fareh, Raouf, Mohammed Baziyad, Tamer Rabie, Ibrahim Kamel, and Maamar Bettayeb. "Sobel Potential Field: Addressing Responsive Demands for UAV Path Planning Techniques." Drones 6, no. 7 (2022): 163. http://dx.doi.org/10.3390/drones6070163.

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Dealing with the trade-off challenge between computation speed and path quality has been a high-priority research area in the robotic path planning field during the last few years. Obtaining a shorter optimized path requires additional processing since iterative algorithms are adopted to keep enhancing the final optimized path. Therefore, it is a challenging problem to obtain an optimized path in a real-time manner. However, this trade-off problem becomes more challenging when planning a path for an Unmanned Aerial Vehicle (UAV) system since they operate in 3D environments. A 3D map will natur

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Moshref, Mahmoud, Rizik Al-Sayyed, and Saleh Al-Sharaeh. "Improving the quality of service in wireless sensor networks using an enhanced routing genetic protocol for four objectives." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (2022): 1182. http://dx.doi.org/10.11591/ijeecs.v26.i2.pp1182-1196.

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<span>Multi-objective algorithms are used to achieve high performance for quality of service (QoS) in wireless sensor networks (WSNs) is an important field for researchers because these algorithms improve two or more conflicting objectives and present the best trade-off between the conflicting objectives to solve multi-objective problems (MOPs). Previous research proposed an algorithm that relies on non-dominated sorting genetic algorithm 3 (NSGA-III), namely enhanced non-dominated sorting genetic routing algorithm (ENSGRA). This algorithm is used to optimise three objectives, which incl

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Shahin, Mirbakhsh, and Azizi Mahdi. "Adaptive Traffic Signal's Safety and Efficiency Improvement by Multi-Objective Deep Reinforcement Learning Approach." International Journal of Innovative Research in Multidisciplinary Education 03, no. 07 (2024): 1245–57. https://doi.org/10.5281/zenodo.12903568.

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This research introduces an innovative method for adaptive traffic signal control (ATSC) through the utilization of multi-objective deep reinforcement learning (DRL) techniques. The proposed approach aims to enhance control strategies at intersections while simultaneously addressing safety, efficiency, and decarbonization objectives. Traditional ATSC methods typically prioritize traffic efficiency and often struggle to adapt to real-time dynamic traffic conditions. To address these challenges, the study suggests a DRL-based ATSC algorithm that incorporates the Dueling Double Deep Q Network (D3

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Szala, Mirosław, Leszek Łatka, Michał Awtoniuk, Marcin Winnicki, and Monika Michalak. "Neural Modelling of APS Thermal Spray Process Parameters for Optimizing the Hardness, Porosity and Cavitation Erosion Resistance of Al2O3-13 wt% TiO2 Coatings." Processes 8, no. 12 (2020): 1544. http://dx.doi.org/10.3390/pr8121544.

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The study aims to elaborate a neural model and algorithm for optimizing hardness and porosity of coatings and thus ensure that they have superior cavitation erosion resistance. Al2O3-13 wt% TiO2 ceramic coatings were deposited onto 316L stainless steel by atmospheric plasma spray (ASP). The coatings were prepared with different values of two spray process parameters: the stand-off distance and torch velocity. Microstructure, porosity and microhardness of the coatings were examined. Cavitation erosion tests were conducted in compliance with the ASTM G32 standard. Artificial neural networks (ANN

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Haitham Murtadha, Fatima, and Suhad Faisal Behadili. "A Review of Algorithms and Platforms for Offloading Decisions in Mobile Cloud Computing." Iraqi Journal for Electrical and Electronic Engineering 21, no. 1 (2024): 97–106. http://dx.doi.org/10.37917/ijeee.21.1.9.

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With the substantial growth of mobile applications and the emergence of cloud computing concepts, therefore mobile Cloud Computing (MCC) has been introduced as a potential mobile service technology. Mobile has limited resources, battery life, network bandwidth, storage, and processor, avoid mobile limitations by sending heavy computation to the cloud to get better performance in a short time, the operation of sending data, and get the result of computation call offloading. In this paper, a survey about offloading types is discussed that takes care of many issues such as offloading algorithms,

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Olajiga, Benson O., and Paul K. Olulope. "Development of an Intelligent Model for Voltage Stability Assessment and Enhancement of Nigeria 330 kV System with High Penetration of Solar-Wind System." Journal of Engineering Research and Reports 27, no. 4 (2025): 81–93. https://doi.org/10.9734/jerr/2025/v27i41457.

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In recent decades, voltage instability has become an important contributor to large-scale power outages globally. Periodic voltage security assessments are key for the secure operation of power systems. There are various indicators which have been implemented in different types and complexity of systems to provide a measure of closeness to voltage collapse, but some of these algorithms may require advanced computational resources or may not perform well, depending on the prevailing conditions. Expanding interconnected power systems is needed like never before due to the increasing world demand

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Elgammal, Adel. "Integrated Path Planning and Speed Control for Electric Vehicles Using MOPSO-Based Optimization." International Journal of Innovative Science and Research Technology, May 29, 2025, 2188–95. https://doi.org/10.38124/ijisrt/25may1547.

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With the increasing penetration level of electric vehicles (EVs), intelligent control strategies have drawn more and more attentions to make the life of batteries last longer while keeping driving performance. Conventional path planning and speed control operations in EVs are usually independently considered, resulting in performance with respect to energy consumption, riding time and vehicle maneuver. In this work, to tackle the trade-off relations between energy consumption, travel time, and ride comfort, we present an integrated approach by introducing the MOPSO as an integrated optimizatio

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Pham, Vu Hong Son, and Luu Ngoc Quynh Khoi. "Artificial intelligence models to predict optimal trade-off on construction management." Engineering, Construction and Architectural Management, November 1, 2024. http://dx.doi.org/10.1108/ecam-06-2024-0698.

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PurposeThis research aims to introduce a novel algorithm, the Chaotic Giant Pacific Octopus Optimizer (CGPOO) and demonstrate framework includes four key aspects: time, cost, quality and safety trade-off (TCQST).Design/methodology/approachArtificial intelligence is causing a big disruption in the construction management. It is being used to building projects to enhance efficiency, safety and decision-making. This research compared the CGPOO method to those of other algorithms, such as the Chaotic Slime Mold Algorithm (CSMA), the Chaotic Salps Swarm Algorithm (CSSA) and the Chaotic Whale Optimi

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Petelin, Gašper, Margarita Antoniou, and Gregor Papa. "Multi-objective approaches to ground station scheduling for optimization of communication with satellites." Optimization and Engineering, March 28, 2021. http://dx.doi.org/10.1007/s11081-021-09617-z.

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AbstractThe ground station scheduling problem is a complex scheduling problem involving multiple objectives. Evolutionary techniques for multi-objective optimization are becoming popular among different fields, due to their effectiveness in obtaining a set of trade-off solutions. In contrast to some conventional methods, that aggregate the objectives into one weighted-sum objective function, multi-objective evolutionary algorithms manage to find a set of solutions in the Pareto-optimal front. Selecting one algorithm, however, for a specific problem adds additional challenge. In this paper the

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Pham, Vu Hong Son, and Luu Ngoc Quynh Khoi. "Biologically inspired optimization for multi-objective construction management: the MOGPOO model." Engineering, Construction and Architectural Management, June 6, 2025. https://doi.org/10.1108/ecam-05-2024-0619.

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PurposeFor construction projects to be managed successfully and to provide the best results, it is essential to assess the trade-offs between time, cost, and quality. By creating and implementing a unique strategy, this study aims to advance solutions to the time-cost-quality trade-off (TCQT) problem. The main goal is to present the multi-objective Giant Pacific Octopus Optimizer (MOGPOO), a state-of-the-art optimization system. This method is intended to transform multi-objective optimization (MO) in construction project management, drawing inspiration from the Giant Pacific Octopus’s strateg

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Zhang, Wenqiang, Guanwei Xiao, Mitsuo Gen, et al. "Enhancing multi-objective evolutionary algorithms with machine learning for scheduling problems: recent advances and survey." Frontiers in Industrial Engineering 2 (February 28, 2024). http://dx.doi.org/10.3389/fieng.2024.1337174.

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Multi-objective scheduling problems in workshops are commonly encountered challenges in the increasingly competitive market economy. These scheduling problems require a trade-off among multiple objectives such as time, energy consumption, and product quality. The importance of each optimization objective typically varies in different time periods or contexts, necessitating decision-makers to devise optimal scheduling plans accordingly. In actual production, decision-makers confront intricate multi-objective scheduling problems that demand balancing clients’ requirements and corporate interests

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"Combined economic and emission dispatch using Whale Optimization Algorithm." ARPN Journal of Engineering and Applied Sciences, February 29, 2024, 2692–707. http://dx.doi.org/10.59018/1223321.

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Power plants give the most to environmental pollution, another important factor nowadays. Power stations must hold carbon credits and follow tight carbon emission restrictions. This is crucial for minimizing global warming and sustaining life. Electric power system planning and operation must meet load demand reliably, cost-effectively, and environmentally. Planners and operators use optimisation tools to attain these goals. In this study, the performance of two new optimisation methods, like the Whale Optimisation Algorithm (WOA), is compared to the performance of two older optimisation metho

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Garrido‐Merchán, Eduardo C., Sol Mora‐Figueroa, and María Coronado‐Vaca. "Multi‐Objective Bayesian Optimization of Deep Reinforcement Learning for Environmental, Social, and Governance (ESG) Financial Portfolio Management." Intelligent Systems in Accounting, Finance and Management 32, no. 2 (2025). https://doi.org/10.1002/isaf.70008.

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ABSTRACTFinancial portfolio management focuses on the maximization of several objectives in a trading period related not only to the risk and performance of the portfolio but also to other objectives such as the environment, social, and governance (ESG) score of the portfolio. Regrettably, classic methods such as the Markowitz model do not take into account ESG scores but only the risk and performance of the portfolio. Moreover, the assumptions made by this model about the financial returns make it unfeasible to be applicable to markets with high volatility such as the technological sector. Th

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Hoitsma, Fabian, Gonzalo Nápoles, Çiçek Güven, and Yamisleydi Salgueiro. "Mitigating implicit and explicit bias in structured data without sacrificing accuracy in pattern classification." AI & SOCIETY, July 10, 2024. http://dx.doi.org/10.1007/s00146-024-02003-0.

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AbstractUsing biased data to train Artificial Intelligence (AI) algorithms will lead to biased decisions, discriminating against certain groups or individuals. Bias can be explicit (one or several protected features directly influence the decisions) or implicit (one or several protected features indirectly influence the decisions). Unsurprisingly, biased patterns are difficult to detect and mitigate. This paper investigates the extent to which explicit and implicit against one or more protected features in structured classification data sets can be mitigated simultaneously while retaining the

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Jamal, Toutouh, Gabriel Rossit Diego, and Nesmachnow Sergio. "Soft computing methods for multiobjective location of garbage accumulation points in smart cities." June 20, 2019. https://doi.org/10.1007/s10472-019-09647-5.

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This article describes the application of soft computing methods for solving the problem of locating garbage accumulation points in urban scenarios. This is a relevant problem in modern smart cities, in order to reduce negative environmental and social impacts in the waste management process, and also to optimize the available budget from the city administration to install waste bins. A specific problem model is presented, which accounts for reducing the investment costs, enhance the number of citizens served by the installed bins, and the accessibility to the system. A family of single- and m

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Mirbakhsh, Shahin, and Mahdi Azizi. "Adaptive Traffic Signal’s Safety and Efficiency Improvement by Multi-Objective Deep Reinforcement Learning Approach." International Journal of Innovative Research in Multidisciplinary Education 03, no. 07 (2024). http://dx.doi.org/10.58806/ijirme.2024.v3i7n10.

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This research introduces an innovative method for adaptive traffic signal control (ATSC) through the utilization of multi-objective deep reinforcement learning (DRL) techniques. The proposed approach aims to enhance control strategies at intersections while simultaneously addressing safety, efficiency, and decarbonization objectives. Traditional ATSC methods typically prioritize traffic efficiency and often struggle to adapt to real-time dynamic traffic conditions. To address these challenges, the study suggests a DRL-based ATSC algorithm that incorporates the Dueling Double Deep Q Network (D3

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Simaiya, Sarita, Umesh Kumar Lilhore, Yogesh Kumar Sharma, et al. "A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques." Scientific Reports 14, no. 1 (2024). http://dx.doi.org/10.1038/s41598-024-51466-0.

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AbstractVirtual machine (VM) integration methods have effectively proven an optimized load balancing in cloud data centers. The main challenge with VM integration methods is the trade-off among cost effectiveness, quality of service, performance, optimal resource utilization and compliance with service level agreement violations. Deep Learning methods are widely used in existing research on cloud load balancing. However, there is still a problem with acquiring noisy multilayered fluctuations in workload due to the limited resource-level provisioning. The long short-term memory (LSTM) model pla

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