Mohammed Jameel, Hana Merah, Alaa M Abd El-Latif et al. · Scientific Reports · 2026
This paper presents a robust multi-objective optimization approach—the multi-objective starfish optimization algorithm (MOSFOA)—designed to address complex challenges in engineering design and optimal power flow analysis. As an advanced extension of the starfish optimization algorithm (SFOA), MOSFOA leverages biological inspiration from starfish behaviors such as exploration, predation, and regeneration to balance global exploration and local exploitation. The proposed MOSFOA employs elitist non-dominated sorting (NDS) and crowding distance (CD) mechanisms to preserve solution diversity and guide convergence toward the Pareto-optimal front. The effectiveness of MOSFOA is validated on standard ZDT and DTLZ benchmark suites and further demonstrated on real-world applications, including engineering design tasks and the IEEE 30-bus power system. Performance comparisons with ten state-of-the-art multi-objective algorithms, using metrics such as inverted generational distance (IGD) and hypervolume (HV), confirm the strength of MOSFOA in achieving a well-balanced trade-off between convergence and diversity. Additionally, the KKT proximity metric (KKTPM) is employed to assess convergence. The results demonstrate that MOSFOA significantly outperforms its counterparts in terms of both IGD and HV, achieving superior convergence and diversity performance. These findings underscore MOSFOA’s robustness, scalability, and stability across runs. Moreover, its strong performance in handling constrained engineering problems highlights its practical potential for real-world decision-making and optimization tasks in power systems and complex design optimization, making MOSFOA a promising tool for both theoretical research and industrial applications. Source code of MOSFOA are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/183090-mosfoa-multi-objective-starfish-optimization-algorithm.
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J.S. Park, Chang‐Ha Lee, Seog‐Chan Oh et al. · International Journal of Precision Engineering and Manufacturing-Green Technology · 2025
Abstract The automotive industry is experiencing rapid changes due to the rise of the Industry 4.0 manufacturing paradigm, which requires strategic implementation of advanced manufacturing systems to meet diverse customer needs. The Matrix Manufacturing System, characterized by modular facilities and autonomous mobile robots, offers greater flexibility compared to traditional dedicated production systems. This paper conducts a multi-objective optimization of facility layout planning within the matrix manufacturing system to enhance efficiency and responsiveness to market volatility. To solve the optimization problem, three heuristic algorithms—Simulated Annealing, Particle Swarm Optimization, and Non-dominated Sorting Genetic Algorithm-II are employed and their performance is compared. For the comparative analysis, frequency maps are used, visualizing the optimization processes and outcomes between metaheuristic algorithms. The framework with methodologies presented in this report is expected to improve productivity and flexibility of a matrix manufacturing system in the automotive industry.
Cited by 4Open access
Hull, Patrick V., SanSoucie, Michael P., Tinker, Michael L. · NASA Technical Reports Server (NASA)
Long-duration surface missions to the Moon and Mars will require habitats for the astronauts. The materials chosen for the habitat walls play a direct role in the protection against the harsh environments found on the surface. Choosing the best materials, their configuration, and the amount required is extremely difficult due to the immense size of the design region. Advanced optimization techniques are necessary for habitat wall design. Standard optimization techniques are not suitable for problems with such large search spaces; therefore, a habitat design optimization tool utilizing genetic algorithms has been developed. Genetic algorithms use a "survival of the fittest" philosophy, where the most fit individuals are more likely to survive and reproduce. This habitat design optimization tool is a multi-objective formulation of structural analysis, heat loss, radiation protection, and meteoroid protection. This paper presents the research and development of this tool.
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Gongxing Wu, Hongyang Li, Weimin Mo · Journal of Marine Science and Engineering · 2024
In recent years, global trade volume has been increasing, and marine transportation plays a significant role here. In marine transportation, the choice of transportation route has been widely discussed. Minimizing fuel consumption, minimizing voyage time, and maximizing voyage security are concerns of the International Maritime Organization (IMO) regarding Maritime Autonomous Surface Ships (MASS). These goals are contradictory and have not yet been effectively resolved. This paper describes the ship path-planning problem as a multi-objective optimization problem that considers fuel consumption, voyage time, and voyage security. The model considers wind and waves as marine environmental factors. Furthermore, this paper uses an improved Whale Optimization Algorithm to solve multi-objective problems. At the same time, it is compared to three advanced algorithms. Through seven three-objective test functions, the performance of the algorithm is tested and applied in path planning. The results indicate that the algorithm can effectively balance the fuel consumption, voyage time, and voyage security of the ship, offering reasonable paths.
Cited by 6Open access
Huanyi Guo · Procedia Computer Science · 2025
This research presents a novel virtual multi-mission search algorithm that synergizes an enhanced Grey Wolf Optimization (GWO) protocol with a Sine-Cosine Approach (SCA). As optimization algorithms gain traction in addressing complex, multifaceted problems, it becomes crucial to enhance their efficacy and adaptability. The classic GWO algorithm is inspired by the social behaviors and hunting strategies of grey wolves. Although the traditional GWO method has shown remarkable results in many optimization problems, its limitations in handling global search and avoiding local minima should not be ignored. With this in mind, we propose an innovative strategy, SCA, that leverage the periodic properties of sine and cosine functions to enhance global exploration and local development. With SCA, a single optimizer can dynamically adjust search parameters, thereby avoiding local optimizations more stably and enhancing overall optimization performance. This improved GWO strategy addresses the virtual multi-objective optimization problem with a task delegation strategy to ensure that each task receives balanced attention. The cooperative search mechanism among different Wolf groups promotes information sharing and efficient coordination among multiple tasks, which reflects the cooperative optimization ability of the optimization process. In summary, this integrated strategy not only improves the efficiency of optimization, but also demonstrates the application possibility of advanced algorithms in dealing with complex practical problems.
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Begüm İpek · Next generation. · 2025
Next-generation mobility systems—ranging from electric vertical takeoff and landing (eVTOL) vehicles to hypersonic aircraft and high-efficiency autonomous drones—require advanced aerothermal and structural optimization techniques to meet demands for safety, performance, and sustainability. Traditional computational approaches, such as finite element analysis, computational fluid dynamics (CFD), and multi-physics simulation, face increasing computational burdens due to the nonlinear interactions among thermal loads, aerodynamic behavior, material deformation, and structural integrity. This study investigates the role of quantum computing in accelerating and improving aerothermal and structural optimization workflows. Leveraging quantum algorithms, including quantum annealing, variational quantum eigensolvers (VQE), quantum approximate optimization algorithms (QAOA), and quantum-inspired multi-objective solvers, the proposed framework enhances the exploration of high-dimensional design spaces and improves the computational efficiency of complex optimization tasks. Early experiments on benchmark aerothermal–structural models demonstrate improved convergence rates, superior Pareto-optimal solutions, and enhanced predictive accuracy compared to classical methods. The findings show that quantum computing has the potential to fundamentally transform mobility system design by enabling faster iteration cycles, better thermal–structural co-optimization, and more energy-efficient architectures suitable for sustainable transportation futures.
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Messaoud Belazzoug, Mohamed Boudour, Karim Sebaa · Archives of Control Sciences · 2010
FACTS location and size for reactive power system compensation through the multi-objective optimization The problem of the FACTS (Flexible Alternative Current Transmission System Devices) location and size for reactive power system compensation through the multi-objective optimization is presented in this paper. A new technique is proposed for the optimal setting, dimension and design of two kinds of FACTS namely: Static Volt Ampere reactive (VAR) Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) handling the minimization of transmission losses in electrical network. Using the proposed scheme, the type, the location and the rating of FACTS devices are optimized simultaneously. The problem to solve is multi criteria under constraints related to the load flow equations, the voltages, the transformer turn ratios, the active and reactive productions and the compensation devices. Its solution requires the the advanced algorithms to be applied. Thus, we propose an approach based on the evolutionary algorithms (EA) to solve multi-criterion problem. It is similar to the NSGA-II method (Ellitist Non Dominated Sorting Genetic Algorithm). The Pareto front is obtained for continuous, discrete and multiple of five MVArs (Mega Volt Ampere reactive) of compensator devices for the IEEE 57-bus test system (IEEE bus test is a standard network).
Cited by 13Open access
Pengxiang Zhao, Jialin Yang, Zhen Li et al. · Journal of Physics Conference Series · 2024
Abstract Sustainable development goals, such as energy conservation, emission reduction, cost reduction, and efficiency enhancement, present pressing challenges for the iron and steel industry. Process optimization technology for efficient production, energy conservation, and consumption reduction in the steel manufacturing process is based on the coordination of material and energy flow. Therefore, this article takes the production process of a converter steelmaking plant as the object, considers the regulatory requirements of multi-process and multi constraint operation optimization under the cooperation of production organization and energy conservation and consumption reduction, and it explores a production scheduling optimization method for steelmaking plants oriented towards material flow and energy flow synergy. Based on the characteristics of the problem space, this study suggests a hybrid genetic algorithm with local search (MOHGALS) that employs both standard and advanced evolution techniques. Tests on concrete problems demonstrate that the MOHGALS approach yields a superior set of multi-objective Pareto schemes. Additionally, random-generated test cases were used to evaluate the algorithm’s performance. The outcomes show that MOHGALS performs better than competing algorithms on multi-objective evaluation indicators, and that the suggested improved evolution approach may effectively boost the algorithm’s performance.
Cited by 2Open access
Journal of Science and Technology - HaUI · 2025
This paper proposes an effective interference suppression method for a half-wavelength dipole uniform linear array (HDULA) by combining a constrainthandling technique (CHT) with the multi-verse optimization (MVO) algorithm.The proposed method is applied to optimize the complex weights of array elements under four different scenarios: convergence characteristics, single-null, multi-null, and wide null placement.CHT is employed to construct the constrained objective function, while MVO is used to efficiently search for the optimal solution.The proposed approach demonstrates significant improvement in interference suppression performance while maintaining the main beam of the HDULA system.Simulation results validate the effectiveness of the CHT-MVO scheme and highlight its potential for integration of constraint-handling techniques with metaheuristic optimization algorithms in the design and operation of advanced antenna systems.
Cited by 0Open access
Ahmad Mozaffari · International Journal of Intelligent Computing and Cybernetics · 2018
Purpose In recent decades, development of effective methods for optimizing a set of conflicted objective functions has been absorbing an increasing interest from researchers. This refers to the essence of real-life engineering systems and complex natural mechanisms which are generally multi-modal, non-convex and multi-criterion. Until now, several deterministic and stochastic methods have been proposed to cope with such complex systems. Advanced soft computational methods such as evolutionary games (cooperative and non-cooperative), Pareto-based techniques, fuzzy evolutionary methods, cooperative bio-inspired algorithms and neuro-evolutionary systems have effectively come to the aid of researchers to build up efficient paradigms with application to vector optimization. The paper aims to discuss this issue. Design/methodology/approach A novel hybrid algorithm called synchronous self-learning Pareto strategy (SSLPS) is presented for the sake of vector optimization. The method is the ensemble of evolutionary algorithms (EA), swarm intelligence (SI), adaptive version of self-organizing map (CSOM) and a data shuffling mechanism. EA are powerful numerical optimization algorithms capable of finding a global extreme point over a wide exploration domain. SI techniques (the swarm of bees in our case) can improve both intensification and robustness of exploration. CSOM network is an unsupervised learning methodology which learns the characteristics of non-dominated solutions and, thus, enhances the quality of the Pareto front. Findings To prove the effectiveness of the proposed method, the authors engage a set of well-known benchmark functions and some well-known rival optimization methods. Additionally, SSLPS is employed for optimal design of shape memory alloy actuator as a nonlinear multi-modal real-world engineering problem. The experiments show the acceptable potential of SSLPS for handling both numerical and engineering multi-objective problems. Originality/value To the author’s best knowledge, the proposed algorithm is among the rare multi-objective methods which fosters the use of automated unsupervised learning for increasing the intensity of Pareto front (while preserving the diversity). Also, the research evaluates the power of hybridization of SI and EA for efficient search.
Cited by 3
Kristina P. Sinaga · arXiv (Cornell University) · 2025
This paper introduces mathematical frameworks that address the challenges of multi-view clustering in federated learning environments. The objective is to integrate optimization techniques based on new objective functions employing heat-kernel coefficients to replace conventional distance metrics with quantum-inspired measures. The proposed frameworks utilize advanced tensor decomposition methods, specifically, PARAFAC2 and Tucker decomposition to efficiently represent high-dimensional, multi-view data while preserving inter-view relationships. The research has yielded the development of four novel algorithms, an efficient federated kernel multi-view clustering (E-FKMVC) model, FedHK-PARAFAC2, FedHK-Tucker, and FedHK-MVC-Person with PARAFAC2 Decomposition (Personalized FedHK-PARAFAC2). The primary objective of these algorithms is to enhance the efficacy of clustering processes while ensuring the confidentiality and efficient communication in federated learning environments. Theoretical analyses of convergence guarantees, privacy bounds, and complexity are provided to validate the effectiveness of the proposed methods. In essence, this paper makes a significant academic contribution to the field of federated multi-view clustering through its innovative integration of mathematical modeling and algorithm design. This approach addresses the critical challenges of data heterogeneity and privacy concerns, paving the way for enhanced data management and analytics in various contexts.
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Ogbonnaya I. Okoro, Chiweta E. Abunike, Udochukwu B. Akuru et al. · 2022 IEEE PES/IAS PowerAfrica · 2022
Interest on the wound-field flux switching machines (WF-FSMs) in the scientific community have increased due to their high reliability, low cost, and absence of magnets. In the context of global sustainability, it is required that WF-FSMs must fulfill physical, technological, and environmental requirements, through design and optimization studies. This study provides a comprehensive evaluation of the state-of-the-art technologies relevant to multi-objective optimization and performance analysis of WF-FSMs. Several efficient optimization strategies from recent research works are highlighted and compared, showing that there are research gaps on the integration of advanced control algorithms and use of robust optimization methods. Some future optimization initiatives are offered including smart design optimization method for robust design and production of WF-FSMs. The proposed novel system uses cloud computing and big data services for smart optimization of WF-FSMs.
Cited by 10
ABDULHUSSEIN HAREEJA REFAISH, Ihab Omar, Muntadher Abed Hussein et al. · International Journal of Thermofluids · 2025
This study addresses the challenge of predicting and optimizing the viscosity of nano-lubricants containing Multi-walled Carbon Nanotubes and Zinc Oxide nanopowders suspended in 10W40 base oil. Accurate viscosity control is crucial for enhancing lubrication system performance. To achieve this, an artificial neural network based on the Group Method of Data Handling was developed, integrated with eight advanced evolutionary algorithms to improve prediction accuracy and optimize viscosity under varying conditions of solid volume fraction, temperature, and shear rate. The research bridges a significant gap by combining predictive modeling with multi-objective optimization, outperforming traditional artificial neural network methods. The use of advanced evolutionary algorithms enabled precise optimization of nano-lubricant properties, while the expanded parameter space provided deeper insights into the impact of operational conditions. The framework achieved a root mean square error of 13.569 and a correlation coefficient of 0.9965, highlighting its superior accuracy. Temperature was identified as the most influential factor, with a viscosity function margin of deviation of -0.88. Further optimization using a Genetic Algorithm determined optimal conditions of 1 % solid volume fraction, 55 °C temperature, and 875.577 s⁻¹ shear rate, resulting in an optimal viscosity of 32.722 cP. This study fills a critical gap in the literature, offering a novel framework for designing high-performance nano-lubricants and significantly advancing the field of lubrication science with improved prediction and optimization methodologies for industrial applications.
Cited by 3Open access
Xuejie Yang, Jing Lei, Qibin Liu · Applied Sciences · 2025
Electrical capacitance tomography holds significant potential for multiphase flow parameter measurements, but its application has been limited by the challenge of reconstructing high-quality images, especially under complex and uncertain conditions. We propose an innovative multi-objective robust optimization model to alleviate this limitation. This model integrates advanced optimization methods, multimodal learning, and measurement physics, structured as a nested upper-level optimization problem and lower-level optimization problem to tackle the challenges of complex image reconstruction. By integrating supervised learning methodologies with optimization principles, our framework synchronously achieves parameter tuning and performance enhancement. Utilizing the regularization theory, the multimodal learning prior image, sparsity prior, and measurement physics are incorporated into a novel lower-level optimization problem. To enhance the inference accuracy of the prior image, a new multimodal neural network leveraging multimodal data is developed. An innovative nested algorithm that mitigates computational difficulties arising from the interactions between the upper- and lower-level optimization problems is proposed to solve the proposed multi-objective robust optimization model. Qualitative and quantitative evaluation results demonstrate that the proposed method surpasses mainstream imaging algorithms, enhancing the automation level of the reconstruction process and image quality while exhibiting exceptional robustness. This study pioneers a novel imaging framework for enhancing overall reconstruction performance.
Cited by 0Open access
Qinwei Fan, Dewei Yang, Jigen Peng et al. · Swarm and Evolutionary Computation · 2025
Decomposition-based multi-objective evolutionary algorithms often suffer from premature convergence when dealing with complex Pareto fronts. To address this issue, this paper proposes a decomposition-based bi-optional optimization algorithm (MOEA/D-BOS). The proposed method integrates the SPEA-II selection mechanism and an individual exploration strategy into the MOEA/D framework to enhance population diversity and information retention. In addition, a new weight vector generation method and a scalarization function are designed to improve the uniformity of solution distribution. Benchmark experiments demonstrate that, compared with several advanced algorithms, MOEA/D-BOS achieves superior performance in terms of both convergence speed and population diversity.
Cited by 0Open access
Guodong Chen, 陈国栋 · The HKU Scholars Hub (University of Hong Kong) · 2025
This thesis addresses the critical challenges in the characterization, optimization, and design of fracture networks in enhanced geothermal systems (EGS) through the development and application of advanced machine learning and optimization techniques. The research focuses on improving the accuracy, efficiency, and scalability of fracture network characterization, geothermal heat extraction optimization, and multi-objective design of geothermal systems, contributing to the sustainable development of geothermal energy resources. The first part of the thesis introduces novel deep generative models for fracture network characterization. A variational auto-encoder and generative adversarial network (VAE-GAN) framework is proposed to estimate fracture networks by capturing the complex distribution of fracture parameters and incorporating prior geological knowledge. This framework maps high-dimensional fracture parameter distributions into low-dimensional continuous fields, significantly reducing uncertainty through Bayesian ensemble smoothing. Additionally, a pre-trained denoising diffusion model is developed for generative inversion of fracture networks, leveraging sparse and noisy observational data to improve reconstruction accuracy and provide a robust probabilistic framework for uncertainty quantification. These models demonstrate superior performance in numerical experiments and real-world applications, offering a transformative approach to subsurface fracture characterization. The second part of the thesis focuses on surrogate-assisted optimization algorithms for geothermal heat extraction and well-placement optimization. A Surrogate-assisted Level-based Learning Evolutionary Search (SLLES) algorithm is proposed to optimize well-control schemes, balancing exploration and exploitation to achieve superior heat sweep efficiency. Furthermore, a Generalized Data-driven Differential Evolutionary Algorithm (GDDE) is introduced to reduce the computational cost of well-placement and control optimization by leveraging probabilistic neural networks and radial basis function surrogates. These algorithms outperform state-of-the-art methods in benchmark functions and real-world geothermal systems, laying a solid foundation for efficient geothermal energy extraction. The third part of the thesis tackles high-dimensional, computationally expensive multi-objective optimization problems in geothermal system design. A Classifier-assisted Rank-based Learning and Local Model-based Evolutionary Algorithm (CLMEA) is proposed, utilizing uncertainty information and rank-based learning strategies to generate promising solutions and maintain diversity in high-dimensional decision spaces. Additionally, a Learning-based Multi-Objective Generative Model (LMOGM) is developed, replacing traditional genetic operators with an attention-enhanced convolutional residual network to generate high-quality candidate solutions. These algorithms demonstrate superior performance in benchmark problems and geothermal reservoir optimization, offering a scalable and efficient approach to multi-objective design. The final part of the thesis introduces a multi-fidelity machine learning framework with knowledge transfer to enhance the efficiency of geothermal energy system design and optimization. This framework leverages coarse and fine surrogate models to iteratively query informative data points, achieving significant speed-ups in optimization while maintaining accuracy. The proposed methods are validated through extensive experimental tests, demonstrating their capability to accelerate decision-making and improve the design of complex geothermal systems. Overall, this thesis advances the state-of-the-art in geothermal energy system design and optimization by integrating deep generative models, surrogate-assisted optimization algorithms, and multi-fidelity machine learning techniques. The proposed methodologies provide accurate, efficient, and scalable solutions for fracture network characterization, heat extraction optimization, and multi-objective design, paving the way for sustainable and efficient geothermal energy extraction. The contributions of this research have broad applicability to related fields, including fluid flow, reservoir management, and renewable energy systems, enabling accelerated discovery of optimal designs for complex systems.
Cited by 0Open access
Marcelo Seido Nagano, Fernando Luís Rossi · Journal of Project Management · 2023
Advanced production systems usually are complex in nature and aim to deal with multiple performance measures simultaneously. Therefore, in most cases, the consideration of a single objective function is not sufficient to properly solve scheduling problems. This paper investigates the multi-objective mixed no-idle flowshop scheduling problem. The addressed optimization case is minimizing makespan subject to an upper bound on total completion time. To solve this problem, we proposed a two-stage iterated greedy and a multi-objective constructive heuristic. Moreover, we developed a new multi-objective improvement procedure focusing on increasing the performance of the developed methods in solving the addressed problem. and a new initialization procedure. We performed several computational tests in order to compare our developed methods with the main algorithms from similar scheduling problems in the literature. It was revealed that the proposed approaches give the best results compared with other state-of-the-art performing methods.
Cited by 2Open access
Naser S. Sanoussi, Tarek M. Belgasam, Maroua Hammami et al. · Journal of Mechanical Design · 2025
Abstract Frictional power losses in spur-geared transmission systems significantly affect the efficiency and performance of mechanical systems. Understanding and minimizing these PVZP are crucial for enhancing gear system efficiency. This study presents a comprehensive analysis and optimization of PVZP using statistical methods and advanced machine learning (ML) techniques. The advanced optimization techniques utilized the multidisciplinary design optimization (MDO) platform mode FRONTIER, which incorporated machine learning (ML) algorithms to further refine the models. An initial dataset of 2000 design points was generated using uniform Latin hypercube (ULHC) sampling. Subsequently, the adaptive space filler (ASF) technique was employed to expand the dataset to 2162 points. Sensitivity analysis was conducted using polynomial chaos expansion (PCE) and distributed random forest (DRF) methods to identify the most influential variables. Among various algorithms, the logarithmic artificial neural network (Log. ANN) model most accurately predicted PVZP. Optimization using the multi-objective genetic algorithm (MOGA) led to significant efficiency improvements. The optimized models were validated against experimental data based on Forschungsstelle für Zahnräder und Getriebebau (FZG)-A10 spur gears evaluated at various load stages and rotational speeds, demonstrating strong alignment with empirical results. The study highlights the effectiveness of integrating statistical and ML techniques in optimizing gear performance, providing valuable insights into the critical factors affecting PVZP. These findings underscore the potential for significant enhancements in gear efficiency and reliability through the use of advanced optimization methodologies.
Cited by 0
Mohammad Aljaidi, Nikunj Mashru, Pinank Patel et al. · Results in Engineering · 2025
• Proposed MORIME Algorithm : ○ Development of a novel Multi-Objective RIME (MORIME) optimization algorithm for efficient truss structure design. ○ Incorporates advanced non-dominated sorting and crowding distance strategies to enhance solution diversity and convergence. • Superior Performance : ○ MORIME outperforms established algorithms such as NSGA-II, MOEA/D, MOMVO, MOTEO, and MOLCA in key metrics like Hypervolume (HV), Inverted Generational Distance (IGD), and Spacing (SP). ○ Demonstrated excellence across eight truss configurations, ranging from simple (10-bar) to highly complex (942-bar) designs. • Optimization Objectives : ○ Focused on minimizing weight and compliance, critical for designing lightweight yet robust truss structures. ○ Provides well-distributed Pareto-optimal solutions, offering structural engineers diverse and high-quality design options. • Applications and Benefits : ○ Applicable for a wide range of structural complexities, from medium to large-scale truss optimization problems. ○ Enables efficient runtime performance, even in high-dimensional settings, making it a practical tool for real-world engineering applications. • Evaluation Metrics and Analysis : ○ Extensive comparison using HV, IGD, and SP metrics, supported by detailed statistical analyses and runtime performance assessments. ○ Consistently achieves competitive IGD values and low SP values, ensuring well-distributed Pareto fronts. • Future Scope : ○ Potential for further computational improvements and dynamic loading considerations. ○ Extensible to other structural engineering problems, including multi-material design and topology optimization. • Versatile Framework : ○ MORIME provides a reliable and adaptable multi-objective optimization framework, with implications for diverse engineering challenges. Multi objective optimization (MOO) is very important in structural engineering, especially in truss design where a trade off between weight reduction and compliance is needed to maximize the efficiency. Usually, conventional optimization algorithms have difficulty in solving complex MOO tasks, and in generating diverse, high quality solutions for various structural configurations. To overcome these challenges, this study proposes a Multi Objective RIME (MORIME) algorithm that uses improved non-dominated sorting and crowding distance techniques to optimize weight and compliance over eight truss designs. With respect to Hypervolume (HV), Inverted Generational Distance (IGD), and Spacing (SP) metrics, it performed better than other established methods such as NSGA-II, MOEA/D, MOMVO, MOTEO, MOLCA, and MORIME, leading to better convergence and diversity of solution sets. The results show that MORIME is a good tool for dealing with complex multi objective optimization landscapes and that it is better than biologically inspired and hybrid optimization methods. MORIME is a powerful tool for structural engineers to produce well balanced truss designs, which meet stringent weight and compliance requirements in a multiobjective setting. MORIME is one attractive feature because it can generate optimal and diverse solutions in truss optimization, resulting in high quality design results.
Cited by 64Open access
Diego Larrahondo Chavez, Catherine Azzaro‐Pantel, Florent Montignac et al. · Renewable and Sustainable Energy Reviews · 2025
As global demand for sustainable energy solutions intensifies, reducing the environmental impact of energy systems while maintaining cost-effectiveness has become imperative, particularly for emerging energy carriers like hydrogen. This article presents a comprehensive review of life cycle assessment (LCA) and multi-objective optimization (MOO) in the context of green hydrogen (H 2 ) systems, focusing on key contributions from 2019 to 2023. The review categorizes the literature into three areas: (i) Recent LCA studies on green H 2 production, (ii) MOO studies that include LCA of renewable energy systems, (iii) Research integrating environmental criteria into MOO for green H 2 systems. The review highlights significant gaps, particularly the limited depth of environmental detail in LCAs integrated into MOO, compared to standalone environmental LCAs. Additionally, the analysis reveals a lack of end-of-life considerations across all study categories. In terms of optimization, most research adopts a bi-objective framework, typically focusing on economic performance and a single environmental metric (often CO 2 emissions), thereby constraining the development of more comprehensive, sustainable systems. The article concludes by offering recommendations: future LCAs of hydrogen systems should incorporate advanced optimization algorithms, broaden their scope to cradle-to-grave assessments, and utilize prospective methods for better anticipation of future environmental impacts. • This paper explores the use of Life Cycle Assessment (LCA) in the design of green hydrogen systems. • It reviews recent studies about Multi-Objective Optimization (MOO) including LCA for renewable energy systems. • It examines optimization with environmental criteria for hydrogen systems. • It discusses balancing detail and efficiency in LCA and optimization. • It recommends advanced decision support tools for better integration of LCA and optimization.
Cited by 16Open access