Iftikhar Farahad, Siddhartha Nath · SciEn Conference Series Engineering · 2025
In the modern world of technology and innovation, electric vehicle (EV) integration into supply chain management has gained much interest as a feasible way to address the problems connected with freight transportation. The Electric Vehicle Routing Problem (EVRP) is carefully examined in this paper, which covers crucial aspects such as EVRP variations, optimization strategies, infrastructure development, and sustainability issues. Alongside specialized and efficient algorithms such as Ant Colony Optimization (ACO) and modified NSGA-II for multi-objective optimization, variants such as Vehicle Routing Problem with Pickup and Delivery (VRPPD), Time Windows (VRPTW), and Capacitated Routing Problem (CVRP) are inspected. To outplay issues with range restrictions, charging time, and cost, the study insists the critical roles that battery technology, effective charging techniques, and infrastructure expansion play. In order to promote green supply chain techniques, route optimization and truckload consolidation are stressed. Furthermore, new developments such as 5G connectivity and driverless EVs are recognized as revolutionary for EVRP. This work synthesizes impactful studies to discover research gaps and recommends future possibilities, such as accommodating multi-objective models and hybrid metaheuristic techniques to fulfill environmental and operational objectives in EVRP. This paper offers an intensive framework for enhancing EV routing research and practice in the context of supply chain management. The study's research requires the inclusion of the inevitability of transforming more conventional VRP variants to their electric equivalents by adding charging station adaptability to existing EVRP models and creating accurate algorithms for optimal routing solutions. The paper also identifies a shortage of research on multi-objective models that take resilience, social, and green elements into priority, as well as hybrid and advanced metaheuristic algorithms. Hence, new models and algorithms are needed to handle the issues posed by emerging technologies namely 5G connection and driverless cars. Furthermore, governments and the business sector are not working together to establish comprehensive charging infrastructure for effective EV routing. The results show how crucial Electric Vehicle Routing Problems (EVRP) are becoming to supply chain management, signifying the necessity of refined optimization techniques, reliable charging infrastructure, and environmentally friendly procedures. The study is to fill in knowledge gaps and improve optimization models for electric vehicle routing that is both operationally and environmentally efficient with refined performance.
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Wei Liu, Thomas Bäck, Yingjie Fan · Leiden Repository (Leiden University) · 2024
Solving multi-objective vehicle routing problem with time windows (VRPTW) is a challenge in last-mile logistics. While traditional methods have predominantly focused on minimizing travel distances and vehicle numbers, recent innovations acknowledge the need for a more refined balance across various objectives. These advancements incorporate a range of heuristic algorithms, blending hybrid techniques that merge genetic algorithms with local search strategies, and employing advanced methodologies such as particle swarm optimization and multi-objective evolutionary algorithms based on decomposition (MOEA/D). Such developments signify a shift towards more effective solutions for VRPTW. In response to these challenges, this paper introduces a novel approach that integrates MOEA/D with K-means clustering for generating efficient solutions. In our approach, we refine the tri-objective VRPTW efficiency by establishing a criterion that prioritizes customer sequences based on their spatial proximity and angular alignment relative to cluster centers and depot, utilizing an integrated metric of distance and cosine angles, which can enhance the local search process and effectively balance the three objectives in VRPTW. Experimental results on Solomon's 56 VRPTW 100-customer dataset instances demonstrate the superiority of our approach.
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Yang Jinghu · Theory and Practice of Science and Technology · 2025
This paper systematically reviews the core issues and latest research progress in the field of unmanned aerial vehicle (UAV) airfoil design. First, the unique low Reynolds number flight environment of UAVs and its challenges to the aerodynamic performance of airfoils are explained, and the formation mechanism and influence of key flow phenomena such as laminar separation bubbles (LSBs) are analyzed in detail. Subsequently, the computational fluid dynamics (CFD) method and wind tunnel experimental technology used for airfoil analysis are discussed in depth, and their advantages and limitations are compared. On this basis, the airfoil geometry parameterization method, various optimization algorithms (such as genetic algorithm, particle swarm optimization) and advanced optimization strategies such as multi-objective and multi-point design are systematically sorted out. Finally, the main challenges faced by current research are summarized, and future development directions such as intelligent airfoils and multidisciplinary design optimization are prospected, aiming to provide comprehensive theoretical reference and technical guidance for the research and development of airfoils for high-performance UAVs.
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Alexiou, Alexios, Aretakis, Nikolaos, Kapsoulis, Dimitrios et al. · Figshare · 2019
A flexible and extensible modular multi-disciplinary framework has been developed, in order to undertake design space exploration and multi-disciplinary optimization assessments of novel Ultra High Bypass Ratio (UHBR) and open rotor concepts at aircraft mission level. The framework comprises advanced engine performance simulation modelling capabilities, coupled with turbomachinery component aerodynamic design, flow path and weight estimation as well as aircraft performance, for mission level assessments. Advanced numerical solvers and optimization algorithms are integrated in the framework. The capabilities developed are used to optimize the design of a UHBR (Ultra High Bypass Ratio) geared turbofan configuration with a Variable Area Nozzle, for short/medium range applications and an EIS of 2025. The objective is to minimize the amount of fuel burn, an objective that leads to both minimum CO2 production and fuel costs. Both single and multi-objective (several missions) optimizations are performed, for representative civil aircraft missions (range from 300 nm to 4000 nm). Based on the technology level assumptions made and the constraints imposed in the design, fuel burn reduction (and therefore CO2 emissions) in excess of 18% are anticipated, compared to conventional current day engines. The work has been undertaken within Clean Sky 2 project DEMOS, in line with efforts to meet the long term ACARE targets to reduce aircraft CO2, NOx and noise emissions.
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Alexiou, Alexios, Aretakis, Nikolaos, Kapsoulis, Dimitrios et al. · Zenodo (CERN European Organization for Nuclear Research) · 2019
A flexible and extensible modular multi-disciplinary framework has been developed, in order to undertake design space exploration and multi-disciplinary optimization assessments of novel Ultra High Bypass Ratio (UHBR) and open rotor concepts at aircraft mission level. The framework comprises advanced engine performance simulation modelling capabilities, coupled with turbomachinery component aerodynamic design, flow path and weight estimation as well as aircraft performance, for mission level assessments. Advanced numerical solvers and optimization algorithms are integrated in the framework. The capabilities developed are used to optimize the design of a UHBR (Ultra High Bypass Ratio) geared turbofan configuration with a Variable Area Nozzle, for short/medium range applications and an EIS of 2025. The objective is to minimize the amount of fuel burn, an objective that leads to both minimum CO2 production and fuel costs. Both single and multi-objective (several missions) optimizations are performed, for representative civil aircraft missions (range from 300 nm to 4000 nm). Based on the technology level assumptions made and the constraints imposed in the design, fuel burn reduction (and therefore CO2 emissions) in excess of 18% are anticipated, compared to conventional current day engines. The work has been undertaken within Clean Sky 2 project DEMOS, in line with efforts to meet the long term ACARE targets to reduce aircraft CO2, NOx and noise emissions.
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Maowei He, Hongxia Zheng, Hanning Chen et al. · Research Square · 2024
<title>Abstract</title> In recent years, multi-objective optimization evolutionary algorithms (MOEAs) have been proven to be effective methods for solving multi-objective optimization problems (MOPs). However, most of existing MOEAs that are limited by the shape of the Pareto fronts (PFs) are only suitable for solving a certain type of problem. Therefore, in order to ensure the generality of the algorithm in practical applications and overcome the constraints brought by the shapes of PFs, a new adaptive MOEA (CAVA-MOEA) based on hierarchical clustering and vector angle to solve various MOPs with irregular PFs is proposed in this article. Firstly, a set of adaptive generated clustering centers is used to guide the population to converge quickly in many search directions. Secondly, the vector angle-based selection further exploits the potential of the clustering algorithm, which keeps a good balance between the diversity and convergence. The proposed CAVA-MOEA is tested and analyzed on 24 MOPs with regular PFs and 18 MOPs with irregular PFs. The results show that CAVA-MOEA has certain competitive advantages compared with other six advanced algorithms in solving MOPs with irregular PFs.
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Chang Shao, Qi Zhao, Nana Pu et al. · Swarm and Evolutionary Computation · 2026
The field of Dynamic Multi-Objective Optimization (DMOO) has witnessed a surge of interest from both academia and industry, as numerous time-evolving real-world applications can be naturally formulated as Dynamic Multi-Objective Optimization Problems (DMOPs). This growing demand thus necessitates advanced benchmarks to rigorously evaluate optimization algorithms under realistic conditions. This paper introduces a comprehensive and principled framework for constructing highly realistic and challenging DMOO benchmarks. The proposed framework incorporates several novel components, including: a generalized formulation that allows the Pareto-optimal Set (PS) to change on hypersurfaces; a mechanism for creating controlled variable contribution imbalances to generate heterogeneous landscapes; and dynamic rotation matrices for inducing time-varying variable interactions and non-separability. Furthermore, we incorporate a temporal perturbation mechanism to simulate irregular environmental changes and propose a generalized time-linkage mechanism that systematically embeds historical solution quality into future problems, thereby capturing critical real-world phenomena such as error accumulation and time-deception. Extensive experimental results validate the effectiveness of the proposed framework, demonstrating its superiority over conventional benchmarks in terms of realism, complexity, and its capability for discriminating state-of-the-art algorithmic performance. Thus, this work establishes a new standard for dynamic multi-objective optimization benchmarking and provides a powerful tool for the development and evaluation of next-generation algorithms capable of addressing the complexities of real-world dynamic systems. • Proposes a novel, comprehensive framework for generating highly realistic DMOO benchmarks. • Introduces hypersurface-based Pareto Set changes, controlled imbalance, and dynamic rotation. • Incorporates irregular temporal perturbations and a generalized time-linkage mechanism. • Establishes a new DMOO benchmark standard and demonstrates algorithm discrimination effectiveness.
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Han Zou, Guoliang Zhang, Cong Sun et al. · npj Heritage Science · 2025
Nowadays, Cultural heritage tourism faces challenges in route planning, including weak data-mining capacity, limited multi-indicator evaluation, and inefficiencies in traditional pathfinding. This study proposes an innovative thematic and sustainable framework that integrates advanced digital technologies at both meso- and micro-spatial scales to optimize heritage route planning. The study introduces and applies the Non-dominated Sorting Genetic Algorithm III (NSGA-III)—specifically designed for high-dimensional multi-objective optimization—which outperforms existing methods in key aspects and effectively solves complex route optimization problems under multiple constraints. Experimental results confirm that Nsga3ip demonstrating 97% rational route probability and 0.89 optimization efficiency—surpassing MOPSO (83%, 0.62) and random algorithms (12%, 0.19) under identical constraints. The findings demonstrate its strengths in planning quality, enhancement of heritage value, and practicability. This underscores the method’s innovation and applicability, further promoting the integration of data-driven approaches in heritage conservation and interdisciplinary urban research.
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S. Sanchez Cruz, Hugo Tanzarella Teixeira · Journal of Computational Methods in Engineering Applications · 2024
Motor design complexity arises from intricate parameter interdependencies, where traditional experience-driven approaches prove inefficient and difficult to optimize through experimentation alone. Growing demands for enhanced motor performance in electric vehicles and intelligent manufacturing necessitate advanced solutions for multi-objective, multi-constraint optimization challenges. This paper presents a performance optimization algorithm integrating Graph Neural Networks (GNN) with adaptive weighting to overcome these limitations. GNNs excel at modeling structured parameter relationships through feature propagation, automatically extracting critical design features that conventional methods fail to capture effectively. Complementing this, Mixed-Integer Linear Programming (MILP) provides robust global optimization under complex decision variables and constraints, resolving convergence issues inherent in traditional algorithms. An adaptive weighting mechanism dynamically prioritizes parameters based on their performance impact, ensuring context-sensitive optimization. By synthesizing GNN representation learning, MILP optimization, and adaptive weighting, our framework addresses three core deficiencies of existing methods: computational inefficiency, poor global convergence, and static parameter valuation. This integrated machine learning and optimization approach establishes an efficient paradigm for next-generation motor design.
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Gholamhossein Golarzi, Hamid Reza Ansari · SHILAP Revista de lepidopterología · 2022
Objective: One of the most important issues for all investors, including individual and institutional investors in the stock market, is finding the optimal portfolio. Identifying the optimal portfolio in the stock market can be considered a two-objective optimization problem. This problem maximizes and minimizes the return and risk of the portfolio, respectively. Like other multi-objective optimization problems, the portfolio optimization problem can be solved by multi-objective evolutionary algorithms (MOEAs). Accordingly, the Non-dominated Sorting Genetic Algorithm ( ) and Strength Pareto Evolutionary Algorithm ( ), as the two advanced algorithms of multi-objective evolution algorithms, can be used to solve this problem. These algorithms identify the optimal solution by ranking and archiving solutions located on the Pareto frontier. The purpose of this research is to compare the performance of and in mean-variance and mean-semi-variance approaches to identify the optimal stock portfolio. Methods: This research investigated 241 stocks enlisted on the Tehran Stock Exchange (TSE). It was conducted within 174 months from September 2006 to March 2019. The researchers first identified the optimal portfolio using NSGAII and SPEA2 algorithms through two approaches including mean-variance and mean-semi-variance. Then, by conducting a statistical hypothesis test on the average Sharp ratio of extracted portfolios, the performance of NSGAII and SPEA2 algorithms were compared. To confirm the research findings, a robustness test was done by comparing the performance of the SPEA2 algorithm with the traditional Markowitz model. Also, to ensure the stability of research findings, the performance of two algorithms in the mean-variance and mean-semi variance approaches were compared with quarterly data ending March 2022. Results: According to the obtained results, the SPEA2 algorithm has better performance than the NSGAII algorithm in both approaches. Backtesting the real data for the quarter ending inMarch 2022 confirmed the findings of the present study. Also by doing robustness tests, the researchers found the SPEA2 algorithm as the superior algorithm in this research with better performance than Markowitz's basic model. Conclusion: The results indicated that the 2 algorithm has better performance in selecting the optimal portfolio than the algorithm in both the mean-variance and mean-semi variance approaches. Regardless of how the stock returns are distributed, this study recommends that individual and institutional investors use the SPEA2 algorithm to determine the optimal portfolio arrangement.
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Rounak Saha Niloy, Hemant Kumar Singh, Tapabrata Ray · Swarm and Evolutionary Computation · 2023
For real-world design optimization, there may often exist multiple candidate concepts that may solve the problem at hand. The process of concurrently identifying the best concept and the corresponding variable values that optimize the design objective(s) is termed as a multi-concept optimization (MCO). While such problems are commonly encountered in practical domains such as engineering, transport, product design, there has been little focus on developing computationally efficient algorithms for MCO. One of the reasons contributing to this gap is the scarcity of benchmark problems with diverse challenges that could be used as a testbed for the development and systematic evaluation of advanced MCO algorithms. In this paper, we particularly focus on MCO problems with multiple conflicting objectives. We review the existing multi-objective MCO test problems and identify their shortcomings through preliminary numerical experiments. Then, we propose a methodology and provide a test problem generator for systematically constructing new multi-objective MCO instances with desired properties. We also propose 28 specific test problem instances using this generator to build a benchmark suite that poses a wide range of challenges to the prospective search strategies. Further, we conduct numerical experiments on the proposed test suite using multiple existing algorithmic strategies from the literature to demonstrate their performance. The results clearly highlight the challenges faced by the strategies for different types of problems. These observations emphasize the need for research efforts towards the development of more efficient and versatile MCO algorithms to tackle a wider range of MCO problems.
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Fang Luo, Yajuan Yang, Yu‐Cheng Geng · Internet Technology Letters · 2024
Abstract In this work, we propose a new hybrid Multi‐Objective Evolutionary Algorithm (hMOEA) specifically designed for semantic sensor data integration, targeting talent development within the burgeoning field of the Semantic Internet of Things (SIoT). Our approach synergizes the capabilities of Multi‐Objective Particle Swarm Optimization and Genetic Algorithms to tackle the sophisticated challenges inherent in Sensor Ontology Matching (SOM). This innovative hMOEA framework is adapt at discerning precise semantic correlations among diverse ontologies, thereby facilitating seamless interoperability and enhancing the functionality of IoT applications. Central to our contributions are the development of an advanced multi‐objective optimization model that underpins the SOM process, the implementation of the hMOEA framework which sets a new benchmark for accurate semantic sensor data integration, and the rigorous validation of hMOEA's superiority through extensive testing in varied real‐world SOM scenarios. This research not only marks a significant advancement in SOM but also highlights the critical role of cutting‐edge SOM methodologies in educational curricula, for example, the new business subject education proposed by China in recent years, aimed at equipping future professionals with the necessary skills to innovate and lead in the SIoT and SW domains.
Cited by 6Open access
Zhi Zhang, Tong Guo, Yefeng Liu et al. · AIMS Electronics and Electrical Engineering · 2025
With the widespread adoption of electric vehicles (EVs), their large-scale integration into the power grid may lead to uncoordinated charging issues, resulting in a significant increase in peak power loads. This may adversely affect the safe, stable operation and economic efficiency of the power grid. To address these challenges, this paper proposed a multi-objective optimization method for EV charging and discharging via Q-learning-based grey wolf optimizer (GWO). The proposed method follows a systematic approach: First, the system architecture was established, defining the roles of EVs and flexible devices in the charging and discharging scheduling process. Second, a multi-objective optimization dispatch model was formulated, with the objectives of minimizing system operation cost, load mean square error, and load peak-to-valley difference. The model was subject to constraints related to power balance and the operational limits of various devices. Third, an advanced multi-objective grey wolf optimizer with integrated Q-learning was developed to address the dispatch model. This optimizer aimed to achieve the Pareto front. Parameters were optimized via orthogonal experiments. The optimizer's effectiveness was measured using metrics assessing distribution and convergence. To address the problem, a case study was used to assess the performance of the proposed Q-GWO algorithm compared to benchmark algorithms, analyzing optimization outcomes and algorithm strengths. Case studies demonstrated 17.3% cost reduction and 31.5% lower peak-valley difference versus benchmark algorithms. Q-GWO's convergence speed (validated via IGD/HV metrics) outperformed GWO by 15.2%, proving efficacy for real-world V2G dispatch. Additionally, the TOPSIS multi-criteria decision analysis approach was employed to identify an optimal solution.
Cited by 1Open access
Xie Xie, Yang Ni, Tianzi Zhang · RePEc: Research Papers in Economics
Given their dominant role in energy expenditure within China’s Hot Summer and Warm Winter (HSWW) zone, high-fidelity performance prediction and multi-objective optimization framework during the early design phase are critical for achieving sustainable energy efficiency. This study presents an innovative approach integrating machine learning (ML) algorithms and multi-objective genetic optimization to predict and optimize the performance of high-rise office buildings in China’s HSWW zone. By integrating Rhino/Grasshopper parametric modeling, Ladybug Tools performance simulation, and Python programming, this study developed a parametric high-rise office building model and validated five advanced and mature machine learning algorithms for predicting energy use intensity (EUI) and useful daylight illuminance (UDI) based on architectural form parameters under HSWW climatic conditions. The results demonstrate that the CatBoost algorithm outperforms other models with an R 2 of 0.94 and CVRMSE of 1.57%. The Pareto optimal solutions identify substantial shading dimensions, southeast orientations, high aspect ratios, appropriate spatial depths, and reduced window areas as critical determinants for optimizing EUI and UDI in high-rise office buildings of the HSWW zone. This research fills a gap in the existing literature by systematically investigating the application of ML algorithms to predict the complex relationships between architectural form parameters and performance metrics in high-rise building design. The proposed data-driven optimization framework provides architects and engineers with a scientific decision-making tool for early-stage design, offering methodological guidance for sustainable building design in similar climatic regions.
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Hamiden Abd El-Wahed Khalifa, Samaher Khalaf Alharbi, Ahmed I. Awad · Research Square · 2022
Abstract The Internet of Things allows us to connect different devices and objects, generate a large volume of huge data, process it, and store it on cloud computing. Recently, cloud computing has become necessary in using pay-for-use spaces to save time and cost. Used data replication in traditional cloud computing leads to excessive use of resources and devices, performance burdens, increased loads, excessive energy consumption, advanced user waiting, and improved response time. This paper proposes the use of multi-objective optimization (MOO) in combination with harris hawks optimization (HHO) and IoT-based salp swarm algorithm (SSA) in cloud computing. The new method called Multi-objective optimization Harris Hawks Optimization with Salp Swarm Algorithm (MOHHOSSA) is based on a set of objectives and integrating two critical technologies, HHO and SSA, to identify and place data replication across nodes using the least path between nodes in cloud computing. The proposed algorithm improves energy consumption (EC), carbon dioxide emission rate (CDER), and means service time (MST). The laboratory results proved the superiority of the proposed algorithm MOHHOSSA over other algorithms in terms of energy, load balancing, Mean service time, and the least expensive path between nodes in the proposed system.
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Emily Sanders, Richard Evans, Jasmine Lee · Frontiers in Environmental Science and Sustainability · 2025
Solar thermal energy harvesting represents a critical frontier in renewable energy technologies, offering substantial potential for sustainable power generation across both direct and indirect energy conversion pathways. This research presents a comprehensive investigation of synergistic computational and machine learning models specifically designed to optimize solar thermal energy harvesting systems through systematic parameter optimization and intelligent control strategies. The study integrates advanced computational fluid dynamics methodologies with multi-parameter optimization algorithms and thermoelectric energy harvesting circuits to create hybrid frameworks that significantly enhance energy conversion efficiency. Through systematic analysis of thermal gradient modeling, heat transfer optimization, and predictive energy forecasting using Response Surface Methodology and machine learning algorithms, this research demonstrates that combined computational and optimization approaches can achieve substantial improvements in energy harvesting efficiency compared to conventional fixed-parameter methods. The proposed hybrid framework incorporates real-time environmental parameter monitoring and multi-objective optimization to dynamically adjust system parameters including collector orientation, thermal storage management, and thermoelectric generator configurations for optimal performance. Experimental validation conducted using thermoelectric generator systems with integrated power management circuits confirms the robustness and practical applicability of the developed optimization models. The findings indicate that systematic parameter optimization using computational methods can achieve power output improvements with correlation coefficients exceeding 0.94 across diverse environmental conditions. Furthermore, the integration of advanced power management circuits within the computational framework demonstrates enhanced energy harvesting capacity and extended operational periods. This research contributes significantly to the advancement of intelligent solar thermal systems by providing a comprehensive methodology for real-time optimization and systematic parameter control, ultimately facilitating broader adoption of renewable energy technologies in industrial and residential applications.
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Asal Jameel Khudhair, Alaa Ahmed Abbood · Computers, materials & continua/Computers, materials & continua (Print) · 2025
Community detection is one of the most fundamental applications in understanding the structure of complicated networks. Furthermore, it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships. Networking structures are highly sensitive in social networks, requiring advanced techniques to accurately identify the structure of these communities. Most conventional algorithms for detecting communities perform inadequately with complicated networks. In addition, they miss out on accurately identifying clusters. Since single-objective optimization cannot always generate accurate and comprehensive results, as multi-objective optimization can. Therefore, we utilized two objective functions that enable strong connections between communities and weak connections between them. In this study, we utilized the intra function, which has proven effective in state-of-the-art research studies. We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse. Furthermore, we proposed a Multi-Objective community strength enhancement algorithm (MOCSE). The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition (MOEA/D), integrated with a new heuristic mutation strategy, community strength enhancement (CSE). The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient. The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information (NMI) and modularity (Q). It was tested using five state-of-the-art algorithms on social networks, comprising real datasets (Zachary, Dolphin, Football, Krebs, SFI, Jazz, and Netscience), as well as twenty synthetic datasets. These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.
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Qing Xue, Chunjiang Wu, Jiyang Nie et al. · Scientific Reports · 2026
Global urbanization has promoted to an increasing scale of construction projects, thereby making the optimization of construction project organization design a critical task in engineering management. However, conventional methods relying on empirical decision-making suffer from issues like low resource allocation efficiency, many difficulties in coordinating multi-objective conflicts and insufficient dynamic adjustment capabilities. To address these issues, we propose the first multi-objective extension of the Animated Oat Optimization algorithm (MOAOO), which represents a pioneering contribution in transforming the single-objective AOO into a multi-objective optimizer for construction project organization design. The developed algorithm fundamentally extends the biological mechanism of Animated Oat Optimization introducing several key innovations: (a) a novel hybrid position update rule combining Elite Reference Points and stochastic perturbations to prevent premature convergence; (b) an innovative three-layer constraint processing mechanism ensuring the generation of feasible solutions; and (c) a dual-threshold convergence monitoring system for early termination. Notably, we establish MOAOO as the inaugural multi-objective variant of AOO, integrating dynamic elite retention strategies, non-dominated sorting, and dynamic archive mechanisms to enable effective collaborative optimization of three conflicting goals. Enough experiments on ZDT test functions demonstrate that the designed MOAOO method shows competitive performance compared to advanced algorithms such as Pre-DEMO, MOEA/D-OED, and Pi-MOEA in terms of hypervolume inverted generational distance and the Spacing metrics. The indicator is improved in certain configurations. In an engineering case study, MOAOO reduces resource fluctuation by 72.7% in the compromise solution while achieving a balanced duration (279 days) and cost ($1.34 M). Moreover, the proposed algorithm converges in 118 iterations on average, thereby verifying its practical value in construction scheduling.
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Mohamed H. ElMessmary, Hatem Y. Diab, Mahmoud Abdelsalam et al. · Applied System Innovation · 2024
One of the most important issues that can significantly affect the electric power network’s ability to operate sustainably is the optimal power flow (OPF) problem. It involves reaching the most efficient operating conditions for the electrical networks while maintaining reliability and systems constraints. Solving the OPF problem in transmission networks lowers three critical expenses: operation costs, transmission losses, and voltage drops. The OPF is characterized by the nonlinearity and nonconvexity behavior due to the power flow equations, which define the relationship between power generation, load demand, and network component physical constraints. The solution space for OPF is massive and multimodal, making optimization a challenging concern that calls for advanced mathematics and computational methods. This paper introduces an innovative metaheuristic algorithm, the Egyptian Stray Dog Optimization (ESDO), inspired by the behavior of Egyptian stray dogs and used for solving both single and multi-objective optimal power flow problems concerning the transmission networks. The proposed technique is compared with the particle swarm optimization (PSO), multi-verse optimization (MVO), grasshopper optimization (GOA), and Harris hawk optimization (HHO) and hippopotamus optimization (HO) algorithms through MATLAB simulations by applying them to the IEEE 30-bus system under various operational circumstances. The results obtained indicate that, in comparison to other used algorithms, the suggested technique gives a significantly enhanced performance in solving the OPF problem.
Cited by 4Open access
Khaled Fettah, Talal Guia, Ahmed Salhi et al. · Scientific Reports · 2024
In this study, we present a comprehensive optimization framework employing the Multi-Objective Multi-Verse Optimization (MOMVO) algorithm for the optimal integration of Distributed Generations (DGs) and Capacitor Banks (CBs) into electrical distribution networks. Designed with the dual objectives of minimizing energy losses and voltage deviations, this framework significantly enhances the operational efficiency and reliability of the network. Rigorous simulations on the standard IEEE 33-bus and IEEE 69-bus test systems underscore the effectiveness of the MOMVO algorithm, demonstrating up to a 47% reduction in energy losses and up to a 55% improvement in voltage stability. Comparative analysis highlights MOMVO's superiority in terms of convergence speed and solution quality over leading algorithms such as the Multi-Objective Jellyfish Search (MOJS), Multi-Objective Flower Pollination Algorithm (MOFPA), and Multi-Objective Lichtenberg Algorithm (MOLA). The efficacy of the study is particularly evident in the identification of the best compromise solutions using MOMVO. For the IEEE 33 network, the application of MOMVO led to a significant 47.58% reduction in daily energy loss and enhanced voltage profile stability from 0.89 to 0.94 pu. Additionally, it realized a 36.97% decrease in the annual cost of energy losses, highlighting substantial economic benefits. For the larger IEEE 69 network, MOMVO achieved a remarkable 50.15% reduction in energy loss and improved voltage profiles from 0.89 to 0.93 pu, accompanied by a 47.59% reduction in the annual cost of energy losses. These results not only confirm the robustness of the MOMVO algorithm in optimizing technical and economic efficiencies but also underline the potential of advanced optimization techniques in facilitating the sustainable integration of renewable energy resources into existing power infrastructures. This research significantly contributes to the field of electrical distribution network optimization, paving the way for future advancements in renewable energy integration and optimization techniques for enhanced system efficiency, reliability, and sustainability.
Cited by 21Open access