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About 10,000 papers for “Advanced Multi-Objective Optimization Algorithms”
Int Conf, Fanjuan Meng, Carl Labergère et al. · HAL (Le Centre pour la Communication Scientifique Directe) · 2011
International audience
Cited by 0
Cha, Young Jin · OakTrust (Texas A&M University Libraries) · 2010
The architectures of the control devices in active control algorithm are an\nimportant fact in civil structural buildings. Traditional research has limitations in finding\nthe optimal architecture of control devices such as using predefined numbers or\nlocations of sensors and dampers within the 2-and 3-dimensional (3-D) model of the\nstructure. Previous research using single-objective optimization only provides limited\ndata for defining the architecture of sensors and control devices. The Linear Quadratic\nGaussian (LQG) control algorithm is used as the active control strategy. The American\nSociety of Civil Engineers (ASCE) control benchmark building definition is used to\ndevelop the building system model. The proposed gene manipulation genetic algorithm\n(GMGA) determines the near-optimal Pareto fronts which consist of varying numbers\nand locations of sensors and control devices for controlling the ASCE benchmark\nbuilding by considering multi-objectives such as interstory drift and minimizing the\nnumber of the control devices.\nThe proposed GMGA reduced the central processing unit (CPU) run time and\nproduced more optimal Pareto fronts for the 2-D and 3-D 20-story building models. Using the GMGA provided several benefits: (1) the possibility to apply any presuggested\nmulti-objective optimization mechanism; (2) the availability to perform a\nobjective optimization problem; (3) the adoptability of the diverse encoding provided by\nthe GA; (4) the possibility of including the engineering judgment in generating the next\ngeneration population by using a gene creation mechanisms; and (5) the flexibility of the\ngene creation mechanism in applying and changing the mechanism dependent on\noptimization problem.\nThe near-optimal Pareto fronts obtained offer the structural engineer a diverse\nchoice in designing control system and installing the control devices. The locations and\nnumbers of the dampers and sensors in each story are highly dependent on the sensor\nlocations. By providing near-Pareto fronts of possible solutions to the engineer that also\nconsider diverse earthquakes, the engineer can get normalized patterns of architectures\nof control devices and sensors about random earthquakes.
Cited by 2
Lorenzo Mas, HIT09, Rita Ponza et al. · 2014
The main rotor hub cap beanie is an aerodynamic component involved in the reduction of helicopter tail shake phenomenon. Recently, an extensive CFD test campaign concerning the AgustaWestland AW101 beanie has been carried out in order to assess its aerodynamic performance at specific operating conditions. These numerical analyses were essential for the beanie aerodynamic multi-objective optimization which is described in the present work. Specifically, the original beanie model was first modified and then, the new geometry was parameterized and the variables describing the main beanie geometrical characteristics were defined. Finally, the aerodynamic optimization was carried out using a specific procedure based on the advanced multi-objective evolutionary algorithm GeDEA developed by the University of Padova. The objectives were the beanie drag reduction and increase of its wake deflection capabilities. Amongst the different optimal solutions belonging to the final Pareto front, three new beanie geometries were finally selected and analyzed via CFD to evaluate the margins of improvement with respect to the baseline configuration.
Cited by 0
Ghanshyam G. Tejani, Sunil Kumar Sharma, Nikunj Mashru et al. · Alexandria Engineering Journal · 2025
This study identifies the Two-Archive Multi-Objective Hippopotamus Optimization Algorithm (MOHO2Arc) as an advanced multi-objective optimization method for optimizing five widely recognized truss structures. The primary objectives are to minimize the structures' mass and maximum nodal displacement. MOHO2Arc improves upon the standard Multi-Objective Hippopotamus Optimization (MOHO) by incorporating a two-archive strategy, significantly boosting solution diversity and optimization efficiency. A thorough comparative analysis was performed to evaluate the performance of the MOHO2Arc against other established multi-objective optimization algorithms. Performance metrics were applied to assess each algorithm's ability to generate diverse, high-quality solutions. The results demonstrate that MOHO2Arc substantially improves solution diversity and quality. Moreover, statistical analysis using Friedman's test further confirms that MOHO2Arc consistently outperforms the other algorithms in optimization tasks. This research highlights MOHO2Arc as an efficient and promising multi-objective truss structure optimization approach, offering notable advancements over current state-of-the-art techniques.
Cited by 14Open access
Stanislav Gulay, Nikolay Korovkin, Dmitry Verkhovtsev et al. · 2023
Multi-objective evolutionary algorithms (MOEAs) are broadly accepted methods in multi-criteria decision-making. Numerous advanced genetic optimization algorithms compute the complicated sets of Pareto-optimal solution. However, with hard-to-calculate objectives, the computation becomes intractable. This paper presents the approach to accelerate the solution of a multi-objective optimization problem, by the example of hydro-generator parameters. Using clustering and regression analysis allow us to reduce the sample size and optimization time, which is especially relevant for problems where each individual requires a long computation time. This paper will describe the underlying genetic algorithm, clustering analysis, regression analysis, and some initial results.
Cited by 1
Xingyi Zhang, Ran Cheng, Ye Tian et al. · 2024
Tackle the most challenging problems in science and engineering with these cutting-edge algorithms Multi-objective optimization problems (MOPs) are those in which more than one objective needs to be optimized simultaneously. As a ubiquitous component of research and engineering projects, these problems are notoriously challenging. In recent years, evolutionary algorithms (EAs) have shown significant promise in their ability to solve MOPs, but challenges remain at the level of large-scale multi-objective optimization problems (LSMOPs), where the number of variables increases and the optimized solution is correspondingly harder to reach. Evolutionary Large-Scale Multi-Objective Optimization and Applications constitutes a systematic overview of EAs and their capacity to tackle LSMOPs. It offers an introduction to both the problem class and the algorithms before delving into some of the cutting-edge algorithms which have been specifically adapted to solving LSMOPs. Deeply engaged with specific applications and alert to the latest developments in the field, it’s a must-read for students and researchers facing these famously complex but crucial optimization problems. The book’s readers will also find: Analysis of multi-optimization problems in fields such as machine learning, network science, vehicle routing, and more Discussion of benchmark problems and performance indicators for LSMOPs Presentation of a new taxonomy of algorithms in the field Evolutionary Large-Scale Multi-Objective Optimization and Applications is ideal for advanced students, researchers, and scientists and engineers facing complex optimization problems.
Cited by 2
Hiroyuki Sano, Shogo Asahino, Kenta Kato et al. · 2025
Optimizing advanced motor designs with Multi-Objective Genetic Algorithms (MOGA) often requires extensive Finite Element Analysis (FEA), leading to computationally expensive problems. This paper aims to accelerate optimization by reducing FEA computation through the integration of machine learning-based surrogate models. We adopt an online optimization approach, where the surrogate model is built during the process, known for its higher learning efficiency. While surrogate models have been applied to optimization, their effectiveness in complex multi-objective, multi-constraint problems with numerous design variables and challenging physical quantities like torque ripple is less explored. We validate the acceleration effect of this online, surrogate-model-enhanced optimization for a multi-objective, multi-constraint Permanent Magnet Synchronous Motor (PMSM) design problem.
Cited by 0
Mohammad Moein Fazeli, Yaghoub Farjami, Amir Jalaly Bidgoly · International Journal of Engineering · 2023
Cloud manufacturing (CMfg) is a new advanced manucatring model developed with the help of enterprise information technologies under the support of cloud computing, Internet of Things and service-based technologies. CMfg compose multiple manufacturing resources to provide efficient and valuable services. CMfg has a highly dynamic environment. In this environment, many disruptions or events may occur that lead the system to unplanned situations. In CMfg, a series of service providers are scheduled for production. During the production operation, some of them may be damaged, stopped, and out of service. Therefore, rescheduling is necessary for the continuation of the production process according to the concluded contracts and initial schedule. When any disruptions or other events occurred, the rescheduling techniques used to updating the inital schedule. In this paper, the dynamic rescheduling problem in CMfg is analyzed. Then the multi-objective rescheduling in CMfg is modeled and defined as a multi-objective optimization problem. Defining this problem as a multi-objective optimization problem provides the possibility of applying, checking and comparing different algorithms. For solving this problem, previous optimization methods have improved and a multi-objective and elitist algorithm based on the Jaya algorithm, called advanced multi-objective elitist Jaya algorithm (AMEJ) is proposed. Several experiments have been conducted to verify the performance of the proposed algorithm. Computational results showed that the proposed algorithm performs better compared to other multi-objective optimization algorithms.
Cited by 2Open access
Ghanshyam G. Tejani, Nikunj Mashru, Pinank Patel et al. · Scientific Reports · 2024
The study suggests a better multi-objective optimization method called 2-Archive Multi-Objective Cuckoo Search (MOCS2arc). It is then used to improve eight classical truss structures and six ZDT test functions. The optimization aims to minimize both mass and compliance simultaneously. MOCS2arc is an advanced version of the traditional Multi-Objective Cuckoo Search (MOCS) algorithm, enhanced through a dual archive strategy that significantly improves solution diversity and optimization performance. To evaluate the effectiveness of MOCS2arc, we conducted extensive comparisons with several established multi-objective optimization algorithms: MOSCA, MODA, MOWHO, MOMFO, MOMPA, NSGA-II, DEMO, and MOCS. Such a comparison has been made with various performance metrics to compare and benchmark the efficacy of the proposed algorithm. These metrics comprehensively assess the algorithms' abilities to generate diverse and optimal solutions. The statistical results demonstrate the superior performance of MOCS2arc, evidenced by enhanced diversity and optimal solutions. Additionally, Friedman's test & Wilcoxon's test corroborate the finding that MOCS2arc consistently delivers superior optimization results compared to others. The results show that MOCS2arc is a highly effective improved algorithm for multi-objective truss structure optimization, offering significant and promising improvements over existing methods.
Cited by 30Open access
Bing Yan, Y. Yang, Zhao Jing et al. · 2022
Constrained multi-objective optimization problems (CMOPs) involves both conflicting objective function and various constraints. Numerous constrained multi-objective evolutionary algorithm (CMOEAs) have been proposed to deal with CMOPs. Accordingly, this paper adopts a co-evolutionary framework for constrained multi-objective optimization. The main population is responsible for the original constrained multi-objective optimization. The help population search the unconstrained derived from the original CMOP. Dual population coevolution is helpful to promote the convergence of the algorithm. To further improve the performance of the algorithm, this article proposes dual environment selection strategies. The two populations adopt different environment selection strategies to improve the diversity of the individuals. The proposed algorithm is compared with four advanced constrained multi-objective evolutionary algorithms on 24 bench-mark CMOPs. The experimental results demonstrate the competitiveness of the proposed algorithm.
Cited by 0
R. Venkata Rao, Hameer Singh Keesari · Journal of Computational Design and Engineering · 2020
Abstract This work proposes a metaphor-less and algorithm-specific parameter-less algorithm, named as self-adaptive population Rao algorithm, for solving the single-, multi-, and many-objective optimization problems. The proposed algorithm adapts the population size based on the improvement in the fitness value during the search process. The population is randomly divided into four sub-population groups. For each sub-population, a unique perturbation equation is randomly allocated. Each perturbation equation guides the solutions toward different regions of the search space. The performance of the proposed algorithm is examined using standard optimization benchmark problems having different characteristics in the single- and multi-objective optimization scenarios. The results of the application of the proposed algorithm are compared with those obtained by the latest advanced optimization algorithms. It is observed that the results obtained by the proposed method are superior. Furthermore, the proposed algorithm is used to identify optimum design parameters through multi-objective optimization of a fertilizer-assisted microalgae cultivation process and many-objective optimization of a compression ignition biodiesel engine system. From the results of the computational tests, it is observed that the performance of the self-adaptive population Rao algorithm is superior or competitive to the other advanced optimization algorithms. The performances of the considered bio-energy systems are improved by the application of the proposed optimization algorithm. The proposed optimization algorithm is more robust and may be easily extended to solve single-, multi-, and many-objective optimization problems of different science and engineering disciplines.
Cited by 30Open access
R. Venkata Rao, R. B. Pawar · Journal of Computational Design and Engineering · 2020
Abstract In this paper, an endeavor is made to enhance the convergence speed of the recently proposed Rao algorithms. The new upgraded versions of Rao algorithms named as “quasi-oppositional-based Rao algorithms” are proposed in this paper. The quasi-oppositional-based learning is incorporated in the basic Rao algorithms to diversify the searching process of the algorithms. The performance of the proposed algorithms is tested on 51 unconstrained benchmark functions. Also, three multi-objective optimization case studies of different heat sinks such as a single-layered microchannel heat sink (SL-MCHS), a double-layered microchannel heat sink (DL-MCHS), and a plate-fin heat sink (PFHS) are attempted to investigate the effectiveness of the proposed algorithms in solving real-world complex engineering optimization problems. The results obtained using the proposed algorithms are compared with the results obtained using the well-known advanced optimization algorithms such as genetic algorithm (GA), artificial bee colony (ABC), differential evolution (DE), particle swarm optimization (PSO), teaching-learning-based algorithm (TLBO), Jaya algorithm, multi-objective genetic algorithm (MOGA), non-dominated sorting genetic algorithm (NSGA-II), real-coded GA (RCGA), direction-based GA, self-adaptive multi-population (SAMP) Rao algorithms, and basic Rao algorithms. The proposed quasi-oppositional-based Rao algorithms are found superior or competitive to the other optimization algorithms considered.
Cited by 36Open access
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
Xu Yang, Rui Wang, Kaiwen Li et al. · ArXiv.org · 2025
The landscape of optimization problems has become increasingly complex, necessitating the development of advanced optimization techniques. Meta-Black-Box Optimization (MetaBBO), which involves refining the optimization algorithms themselves via meta-learning, has emerged as a promising approach. Recognizing the limitations in existing platforms, we presents PlatMetaX, a novel MATLAB platform for MetaBBO with reinforcement learning. PlatMetaX integrates the strengths of MetaBox and PlatEMO, offering a comprehensive framework for developing, evaluating, and comparing optimization algorithms. The platform is designed to handle a wide range of optimization problems, from single-objective to multi-objective, and is equipped with a rich set of baseline algorithms and evaluation metrics. We demonstrate the utility of PlatMetaX through extensive experiments and provide insights into its design and implementation. PlatMetaX is available at: \href{https://github.com/Yxxx616/PlatMetaX}{https://github.com/Yxxx616/PlatMetaX}.
Cited by 0Open access
Sanhueza, Claudio, Jiménez, Francia, Berretta, Regina et al. · arXiv (Cornell University) · 2018
Algorithms for data visualizations are essential tools for transforming data into useful narratives. Unfortunately, very few visualization algorithms can handle the large datasets of many real-world scenarios. In this study, we address the visualization of these datasets as a Multi-Objective Optimization Problem. We propose mQAPViz, a divide-and-conquer multi-objective optimization algorithm to compute large-scale data visualizations. Our method employs the Multi-Objective Quadratic Assignment Problem (mQAP) as the mathematical foundation to solve the visualization task at hand. The algorithm applies advanced sampling techniques originating from the field of machine learning and efficient data structures to scale to millions of data objects. The algorithm allocates objects onto a 2D grid layout. Experimental results on real-world and large datasets demonstrate that mQAPViz is a competitive alternative to existing techniques.
Cited by 0Open access
Xichao Zhou, Bing Wang, Zishang Xu et al. · 2025
Facing the requirement of efficient balanced operation of multiple distributed energy sources and loads, the multi-objective optimal scheduling task for microbalanced area grids relies on more advanced algorithms day by day. Some metaheuristic intelligent self-trending optimization methods such as particle swarm algorithms, dung beetle optimization algorithms, etc., have been used to improve energy scheduling efficiency. Along these works, this paper further extends and generalizes some recently proposed metaheuristic algorithms, e.g., Walrus optimization, Optical microscope optimization, and Crayfish optimization, to investigate which type of methods are more effective in solving the energy scheduling optimization problem in microbalancing zones. Simulation of the algorithm shows that the Walrus optimization method performs better for optimal energy scheduling of microbalanced area in terms of convergence speed and optimization capability level.
Cited by 0
Shengxue Qin, Shengnan Zhang, Wenjun Zhong et al. · Processes · 2025
Facing global pressures such as population growth, shrinking arable land, and climate change, intelligent agriculture has emerged as a critical pathway toward sustainable and efficient agricultural production. Control algorithms serve as the core enabler of this transition, finding applications in crop production, pest management, agricultural machinery, and resource optimization. This review systematically examines the performance and applications of both traditional (e.g., PID, fuzzy logic) and advanced control algorithms (e.g., neural networks, model predictive control, adaptive control, active disturbance rejection control, and sliding mode control) in agriculture. While traditional methods are valued for simplicity and robustness, advanced algorithms better handle nonlinearity, uncertainty, and multi-objective optimization, enhancing both precision and resource efficiency. However, challenges such as environmental heterogeneity, hardware limitations, data scarcity, real-time requirements, and multi-objective conflicts hinder widespread adoption. This review contributes a structured, critical synthesis of these algorithms, highlighting their comparative strengths and limitations, and identifies key research gaps that distinguish it from prior reviews. Future directions include lightweight algorithms, digital twins, multi-sensor integration, and edge computing, which together promise to enhance the scalability and sustainability of intelligent agricultural systems.
Cited by 5Open access
Weijian Sun · 2025
As the autonomous driving technology continues to advance, the multi-objective optimization problem of path planning is increasingly receiving attention. Inspired by the biological principle of neural-based navigation in animals, a multi-objective optimization model based on the width neural network and Nash equilibrium strategy (N-WNN) is proposed to improve the reachability and flexibility of path planning. Specifically, the width neural network (WNN) is expanded to intensify feature extraction within hidden layers, without increasing network depth, thereby capturing crucial path information. Nash equilibrium strategy helps realize multi-objective optimization of path planning by making each objective in path planning reach an equilibrium state under the condition of mutual influence and restriction, so that each objective cannot be further optimized by unilaterally changing the strategy given by other objective strategies, thus helping to realize multi-objective optimization of path planning. Through experimental verification of path planning problems in different scenarios, the results show that the proposed method has significant advantages in multi-objective optimization, which can effectively balance the reachability and flexibility of path planning, and has been compared in detail with the two most advanced multi-objective optimization algorithms currently available, proving the superiority of the proposed algorithm.
Cited by 0
Adrian Gambier · 2020
The advanced control system design is turning more and more complex and, sophisticated optimization techniques become essential in order to satisfy such high requirements. In particular, multi-objective optimal (MOO) control is nowadays a way to reach a control system that conciliates at the same time several design objectives that sometimes could be contradictories. In the past ten years, numerous advances can be observed in the field of multi-objective optimization. Many optimization algorithms with high complexity are now available in order to solve different kind of optimization problems. The use of such approaches is not simple. Thus, the main contribution of this work is study of old and new MOO algorithms from the control point of view providing practical experience and design hints. A numerical study by using the pitch control system design of a wind turbine is included.
Cited by 3