Search Results (636)

The smart home contributions in energy management systems can help the microgrid operator overcome technical problems and ensure economically viable operation by flattening the load profile. The purpose of this paper is to propose a smart home energy management system (SHEMS) that enables smart homes to monitor, store, and manage energy efficiently. SHEMS relies heavily on energy storage systems (ESSs) and electric vehicles (EVs), which enable smart homes to be more flexible and enhance the reliability and efficiency of renewable energy sources. It is vital to study the optimal operation of batteries in SHEMS; hence, a multi-objective optimization approach for SHEMS and demand response programs is proposed to simultaneously reduce the daily bills, the peak-to-average ratio, and the number of battery discharging cycles of ESSs and EVs. An inverter-based air conditioner, photovoltaic system, ESS, and EV, shiftable and non-shiftable equipment are considered in the suggested smart home. In addition, the amount of energy purchased and sold throughout the day is taken into account in the suggested mathematical formulation based on the real-time market pricing. The suggested multi-objective problem is solved by an improved gray wolf optimizer, and various weather conditions, including rainy, sunny, and cloudy days, are also analyzed. Additionally, simulations indicate that the proposed method achieves optimal results, with three objectives shown on the Pareto front of the optimal solutions. Full article

We address surrogate-assisted multi-objective optimization for computationally expensive structural designs. The testbed is an axisymmetric laminated composite shell whose geometry, ply angles, and plywise materials are optimized to simultaneously (i) maximize separation of selected natural frequencies from a known excitation and (ii) minimize material cost. To reduce high-fidelity (HF) finite element evaluations, we develop a deep neural network surrogate framework with three variants: an HF-only baseline; a multi-fidelity (MF) pipeline using an auxiliary refinement network to convert abundant low-fidelity (LF) data into pseudo-HF labels for a single-fidelity evaluator; and a cascaded ensemble that emulates HF responses and then maps them to pseudo-experimental targets. During optimization, only surrogates are queried—no FEM calls—while final designs are verified by FEM. Pareto-front quality is quantified primarily by a normalized relative hypervolume indicator computed against an envelope approximation of the True Pareto Front, complemented where appropriate by standard indicators. A controlled training protocol and common validation regime isolate the effect of fidelity strategy from architectural choices. Results show that MF variants markedly reduce HF data requirements and improve Pareto-front quality over the HF-only baseline, offering a practical route to scalable, accurate design under strict computational budgets. Full article

This paper proposes an innovative hybrid optimization framework for the optimal installation and operation of thyristor-switched capacitors (TSCs) within medium-voltage distribution networks, targeting both energy losses reduction and cost efficiency. The core of the approach combines the exploratory capabilities of the atan-sinc optimization algorithm (ASOA), a recent metaheuristic inspired by mathematical functions, with the local refinement power of the IPOPT solver within a master–slave architecture. This integrated method addresses the inherent complexity of a multi-objective, mixed-integer nonlinear programming problem that seeks to balance conflicting goals: minimizing annual system losses and investment costs. Extensive testing on IEEE 33- and 69-bus systems under fixed and dynamic reactive power injection scenarios demonstrates that our framework consistently delivers superior solutions when compared to traditional and state-of-the-art algorithms. Notably, the variable operation case yields energy savings of up to 12%, translating into annual monetary gains exceeding USD 1000 in comparison with the fixed support scenario.The solutions produce well-distributed Pareto fronts that illustrate valuable trade-offs, allowing system planners to make informed decisions. The findings confirm that the proposed strategy constitutes a scalable, and robust tool for reactive power planning, supporting the deployment of smarter and more resilient distribution systems. Full article

In recent years, numerous constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed to tackle constrained multi-objective optimization problems (CMOPs). However, most of them still struggle to achieve a good balance among convergence, diversity, and feasibility. To address this issue, we develop an adaptive constraint-boundary learning-based two-stage dual-population evolutionary algorithm for CMOPs, referred to as CL-TDEA. The evolutionary process of CL-TDEA is divided into two stages. In the first stage, two populations cooperate weakly through environmental selection to enhance the exploration ability of CL-TDEA under constraints. In particular, the auxiliary population employs an adaptive constraint-boundary learning mechanism to learn the constraint boundary, which in turn enables the main population to more effectively explore the constrained search space and cross infeasible regions. In the second stage, the cooperation between the two populations drives the search toward the complete constrained Pareto front (CPF) through mating selection. Here, the auxiliary population provides additional guidance to the main population, helping it escape locally feasible but suboptimal regions by means of the proposed cascaded multi-criteria hierarchical ranking strategy. Extensive experiments on 54 test problems from four benchmark suites and three real-world applications demonstrate that the proposed CL-TDEA exhibits superior performance and stronger competitiveness compared with several state-of-the-art methods. Full article

To address the challenges of weight redundancy, low material utilization, and excessive performance margins in the design of electric cable-hauling machines, this study proposes a novel multi-objective optimization framework. The framework integrates Latin hypercube experimental design, Kriging surrogate modeling, a Non-dominated Sorting Genetic Algorithm III (NSGA-III), and a coupled TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach. A high-fidelity finite element model based on extreme operating conditions was established to simulate the performance of the electric towing winch. The Kriging model was employed to replace time-consuming finite element calculations, significantly improving computational efficiency. The NSGA-III algorithm was then utilized to search for the Pareto front, identifying a set of optimal solutions that balance multiple design objectives. Finally, the TOPSIS method was applied to select the most preferable solution from the Pareto front. The results demonstrate a 7.32% reduction in the overall mass of the towing winch, a 7.34% increase in the safety factor, and a 4.57% reduction in maximum structural deformation under extreme operating conditions. These findings validate the effectiveness of the proposed Kriging-NSGA-III-TOPSIS strategy for lightweight design of ship deck winch machinery. Full article

The constrained bi-objective Minimum Spanning Tree (MST) problem is a fundamental challenge in network design, as it simultaneously requires minimizing both total edge weight and maximum hop distance under strict feasibility limits; however, most existing algorithms tend to emphasize one objective over the other, resulting in imbalanced solutions, limited Pareto fronts, or poor scalability on larger instances. To overcome these shortcomings, this study introduces a Hybrid MOCPO–AGE-MOEA algorithm that strategically combines the exploratory strength of Multi-Objective Crested Porcupines Optimization (MOCPO) with the exploitative refinement of the Adaptive Geometry-based Evolutionary Algorithm (AGE-MOEA), while a Kruskal-based repair operator is integrated to strictly enforce feasibility and preserve solution diversity. Moreover, through extensive experiments conducted on Euclidean graphs with 11–100 nodes, the hybrid consistently demonstrates superior performance compared with five state-of-the-art baselines, as it generates Pareto fronts up to four times larger, achieves nearly 20% reductions in hop counts, and delivers order-of-magnitude runtime improvements with near-linear scalability. Importantly, results reveal that allocating 85% of offspring to MOCPO exploration and 15% to AGE-MOEA exploitation yields the best balance between diversity, efficiency, and feasibility. Therefore, the Hybrid MOCPO–AGE-MOEA not only addresses critical gaps in constrained MST optimization but also establishes itself as a practical and scalable solution with strong applicability to domains such as software-defined networking, wireless mesh systems, and adaptive routing, where both computational efficiency and solution diversity are paramount Full article

Multi-Robot Task Assignment (MRTA) is a critical and inherently multi-objective problem in diverse real-world applications, demanding the simultaneous optimization of conflicting objectives such as minimizing total travel distance and balancing robot workload. Existing multi-objective evolutionary algorithms (MOEAs) often struggle with slow convergence and insufficient diversity when tackling the combinatorial complexity of large-scale MRTA instances. This paper introduces the Collaborative Swarm-Differential Evolution (CSDE) algorithm, a novel MOEA designed to overcome these limitations. CSDE’s core innovation lies in its deep, operator-level fusion of Differential Evolution’s (DE) robust global exploration capabilities with Particle Swarm Optimization’s (PSO) swift local exploitation prowess. This is achieved through a unique fused velocity update mechanism, enabling particles to dynamically benefit from their personal experience, collective swarm intelligence, and population diversity-driven knowledge transfer. Comprehensive experiments on various MRTA scenarios demonstrate that CSDE consistently achieves superior performance in terms of convergence, solution diversity, and Pareto front quality, significantly outperforming standard multi-objective algorithms like Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Differential Evolution (MODE), and Multi-Objective Genetic Algorithm (MOGA). This study highlights CSDE’s substantial contribution to the MRTA field and its potential for more effective and efficient multi-robot system deployment. Full article

Decarbonizing the industrial sector is essential to achieving net-zero targets and ensuring a sustainable future. Carbon–Hydrogen–Oxygen Symbiosis Networks (CHOSYN) are a set of interconnected hydrocarbon-processing plants that optimize the synergistic use of mass and energy resources in pursuit of both environmental objectives and profitability enhancement. However, this interconnectedness also introduces fragility, arising from technical and administrative dependencies among the participating facilities. In this work, a systematic framework is introduced to incorporate resilience assessment and sustainability enhancement within CHOSYNs. A CHOSYN representation is developed for a proposed industrial cluster, where processes are linked through interceptor units, which facilitate the exchange and conversion of carbon-, hydrogen-, and oxygen-based streams to meet demands. A multi-objective optimization framework is formulated with four competing goals: minimizing cost, minimizing net CO₂ emissions, maximizing internal CO₂ utilization, and minimizing the number of interceptors’ processing steps. The augmented $\epsilon$-constraint method is used to generate a Pareto front that captures the trade-offs among these objectives. To complement the synthesis, a resilience assessment framework is applied to evaluate network performance under disruption by incorporating inter-plant dependencies and modeling disruption propagation. The results show that even under worst-case scenarios, integration through CHOSYN can achieve significant gains in CO₂ utilization and reductions in raw material procurement requirements. Resilience analysis adds an important dimension by quantifying the economic impacts of disruptions to both highly connected and sparsely connected yet critical nodes, revealing vulnerabilities not evident from topology alone. Full article

Antarctic research stations require reliable low-carbon power under extreme conditions. This study compiles a synchronized PV-meteorological time-series data set on Horseshoe Island (Antarctica) at 30 s, 1 min, and 5 min resolutions and compares four PV module types (monocrystalline, polycrystalline, flexible mono, and semitransparent) under controlled field operation. Model development adopts an interpretable, multi-objective framework: a modified SPEA-2 searches rule sets on the Pareto front that jointly optimize precision and recall, yielding transparent, physically plausible decision rules for operational use. For context, benchmark machine-learning models (e.g., kNN, SVM) are evaluated on the same splits. Performance is reported with precision, recall, and complementary metrics (F1, balanced accuracy, and MCC), emphasizing class-wise behavior and robustness. Results show that the proposed rule-based approach attains competitive predictive performance while retaining interpretability and stability across panel types and sampling intervals. Contributions are threefold: (i) a high-resolution field data set coupling PV output with solar radiation, temperature, wind, and humidity in polar conditions; (ii) a Pareto-front, explainable rule-extraction methodology tailored to small-power PV; and (iii) a comparative assessment against standard ML baselines using multiple, class-aware metrics. The resulting XAI models achieved 92.3% precision and 89.7% recall. The findings inform the design and operation of PV systems for harsh, high-latitude environments. Full article

Against the background of increasingly scarce water resources and intensifying water use conflicts, achieving the scientific and optimized allocation of water resources has become crucial to ensuring regional sustainable development. Based on the traditional water resource optimization models that consider social, economic, and ecological objectives, this study introduces a spatial equilibrium level as a fourth optimization objective, constructing a multi-objective water resource allocation optimization model. The model simultaneously incorporates constraints on water supply, water demand, and decision variable non-negativity, as well as coupling coordination constraints among the water resources, socio-economic, and ecological subsystems within each water use unit. The NSGA-III algorithm is employed to obtain the Pareto front solution set for the four objectives, followed by a comprehensive ranking of the Pareto solutions using an entropy-weighted TOPSIS method. The solution exhibiting the best trade-off among the four objectives is selected as the decision basis for the water allocation scheme. Taking Jiuquan, Jiayuguan, and Zhangye cities in western Gansu Province as the study area, the results indicate that the optimal allocation scheme can guide the cities to shift from “water-deficit usage” toward “water-saving usage,” achieving a reasonable balance between meeting water demand and water conservation requirements. This promotes coordinated development among the water resource, socio-economic, and ecological subsystems within each city as well as among the cities themselves, thereby facilitating sustainable utilization of water resources and sustainable development of socio-economics and the ecological environment. The findings can serve as a reference for water resource allocation strategies in the study region. Full article

This paper presents three simple and efficient advanced optimization algorithms, namely the best–worst–random (BWR), best–mean–random (BMR), and best–mean–worst–random (BMWR) algorithms designed to address unconstrained and constrained single- and multi-objective optimization tasks of the metal casting processes. The effectiveness of the algorithms is demonstrated through real case studies, including (i) optimization of a lost foam casting process for producing a fifth wheel coupling shell from EN-GJS-400-18 ductile iron, (ii) optimization of process parameters of die casting of A360 Al-alloy, (iii) optimization of wear rate in AA7178 alloy reinforced with nano-SiC particles fabricated via the stir-casting process, (iv) two-objectives optimization of a low-pressure casting process using a sand mold for producing A356 engine block, and (v) four-objectives optimization of a squeeze casting process for LM20 material. Results demonstrate that the proposed algorithms consistently achieve faster convergence, superior solution quality, and reduced function evaluations compared to simulation software (ProCAST, CAE, and FEA) and established metaheuristics (ABC, Rao-1, PSO, NSGA-II, and GA). For single-objective problems, BWR, BMR, and BMWR yield nearly identical solutions, whereas in multi-objective tasks, their behaviors diverge, offering well-distributed Pareto fronts and improved convergence. These findings establish BWR, BMR, and BMWR as efficient and robust optimizers, positioning them as promising decision support tools for industrial metal casting. Full article

With the rapid advancement of intelligent technologies, permanent magnet synchronous motor (PMSM) servo systems have seen increasing applications in industrial fields, accompanied by continuously rising control performance demands. Moreover, the adjustment of controller parameters is pivotal for the performance optimization of servo systems. This paper presents an optimization method for PMSM servo systems based on the coupling technique of the neural network surrogate model and intelligent optimization algorithm. A hybrid model is constructed by the proposed method, integrating a mathematical model based on transfer functions with an artificial neural network surrogate model, which is employed to compensate for the discrepancies between the mathematical model and the actual measured values. The accuracy and superiority of the hybrid model are comprehensively validated through training and validation loss analysis, fitting plot construction, and ablation experiments. Subsequently, based on the hybrid model, the qualitative and quantitative comparative analysis of the Pareto fronts of five commonly used multi-objective intelligent optimization algorithms is conducted. The optimal algorithm is determined through experimental validation of the optimization results to obtain the optimal result. The optimal result demonstrates that, compared to the initial result before optimization, the overshoot is reduced by 89.7%, and the settling time is reduced by 80.1%. Additionally, several other non-dominated solutions are available for selection, and all optimized results are superior to the initial result. This study provides a novel idea and method for the performance optimization of PMSM servo systems, contributing to the field with a robust and effective approach to enhance system control performance. Full article

A novel rolling scheme incorporating an inclined oval-caliber configuration is proposed to enhance plastic deformation mechanisms in the traditional oval–round rolling sequence. Finite Element Method (FEM) simulations were performed using DEFORM-3D to evaluate and optimize this new scheme across multiple objectives: maximizing average effective strain, minimizing strain non-uniformity (captured via the standard deviation of effective strain), and minimizing rolling force. Numerical modeling was conducted for calibration angles of γ = 0°, 25°, 35°, and 45°, from which Pareto-optimal solutions were identified based on classical non-dominance criteria. Pairwise 2D projections of the Pareto front enabled visualization of trade-offs and revealed γ = 35° as the Pareto knee-point, representing the most balanced compromise among high deformation intensity, increased uniformity, and reduced energy consumption. This optimal angle was further corroborated through a normalized weighted sum of the objective functions. The findings provide a validated reference for designing prototype deforming tools and support future experimental validation. Full article

Passive acoustic sensing with arrays has applications in many fields, including atmospheric monitoring of low frequency sounds (i.e., infrasound). Beamforming of array signals to gain spatial information about the signal is common, but the performance is often degraded due to limited resources (e.g., number of sensors, array size). Such sparse arrays create ambiguities due to reduced resolution and spatial aliasing. While previous work has focused on either maximizing array resolution or minimizing spatial aliasing, the current study demonstrates how evolutionary algorithms can be utilized to identify array configurations that optimize for both properties. The non-dominated sorting genetic algorithm II (NSGA-II) was used with the beamwidth and maximum sidelobe level as the fitness functions to iteratively identify a group of optimized synthesized array configurations. This group is termed a Pareto-front and is optimized such that one fitness function cannot be improved without a decrease in the other. These optimized solutions were studied for a single frequency (8 Hz) and a multi-frequency (3 to 20 Hz) signal using either a 36-element or 9-element array with a 60 m aperture. The performance of the synthesized arrays was compared against established array configurations (baseline) with most of the Pareto-front solutions outperforming these baseline configurations. The largest improvements to array performance using the synthesized configurations were with fewer array elements and the multi-frequency signal. Full article

Precision component displacement, processing, and manipulation in an industrial environment require the high-precision positioning and orientation of industrial robots. However, industrial robots’ positioning includes uncertainties due to assembly and manufacturing tolerances. It is therefore required to use calibration techniques for industrial robot parameters. One of the major sources of uncertainty is the one associated with industrial robot geometrical parameter values. In this paper, using multi-objective meta-heuristic optimization approaches and optical metrology measurements, more accurate Denavit–Hartenberg (DH) geometrical parameters of an industrial robot are estimated. The sensor data used to perform this calibration are the absolute 3D position readings using a highly accurate laser tracker (LT) and industrial robot joint angle readings. Other than position accuracy, the mean absolute deviation of the DH parameters from the manufacturer’s given parameters is considered as the second objective function. Therefore, the optimization problem investigated in this paper is a multi-objective one. The solution to the multi-objective optimization problem is obtained using different evolutionary and swarm optimization approaches. The evolutionary optimization approaches are nondominated sorting genetic algorithms and a multi-objective evolutionary algorithm based on decomposition. The swarm optimization approach considered in this paper is multi-objective particle swarm optimization. It is observed that NSGAII outperforms the other two optimization algorithms in terms of a more diverse Pareto front and the function corresponding to the positional accuracy. It is further observed that through using NSGAII for calibration purposes, the root mean squared for positional error has been improved significantly compared with nominal values. Full article