Non-dominated Sorting Research Articles

Ply Optimization of Composite Laminates for Processing-Induced Deformation and Buckling Eigenvalues Based on Improved Genetic Algorithm

The structure of thermoset composite laminated plates is made by stacking layers of plies with different fiber orientations. Similarly, the stiffened panel structure is assembled from components with varying ply configurations, resulting in thermal residual stresses and processing-induced deformations (PIDs) during manufacturing. To mitigate the residual stresses caused by the geometric features of corner structures and the mismatch between the stiffener-skin ply orientations, which lead to PIDs in composite-stiffened panels, this study proposes a multi-objective stacking optimization strategy based on an improved adaptive genetic algorithm (IAGA). The viscoelastic constitutive model was employed to describe the modulus variation during the curing process to ensure computational accuracy. In this study, the IAGA was proposed to optimize the ply-stacking sequence of L-shaped stiffeners in composite laminated structures. The results demonstrate a reduction in the spring-in angle to 0.12°, a 50% improvement compared to symmetric balanced stacking designs, while the buckling eigenvalues were improved by 20%. Additionally, the IAGA outperformed the traditional non-dominated sorting genetic algorithm (NSGA), achieving a threefold increase in the Pareto solution diversity under identical constraints and reducing the convergence time by 70%. These findings validate the effectiveness of asymmetric ply design and provide a robust framework for enhancing the structural performance and manufacturability of composite laminates.

Improved dynamic programming method for solving multi-objective and multi-stage decision-making problems

Multi-objective and multi-stage decision-making problems require balancing multiple objectives at each stage and making optimal decision in multi-dimensional control variables, where the commonly used intelligent optimization algorithms suffer from low solving efficiency. To this end, this paper proposes an efficient algorithm named non-dominated sorting dynamic programming (NSDP), which incorporates non-dominated sorting into the traditional dynamic programming method. To improve the solving efficiency and solution diversity, two fast non-dominated sorting methods and a dynamic-crowding-distance based elitism strategy are integrated into the NSDP algorithm. The proposed algorithm has been verified on 12 benchmark test functions and a multi-objective travelling salesman problem. The results demonstrate that the NSDP algorithm achieves better outcome in multiple performance metrics and higher solving efficiency, compared with non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization.

Geometric parameters optimization of tap via response surface methodology and nondominated sorting dung beetle optimization algorithm

Geometric parameters optimization of tap via response surface methodology and nondominated sorting dung beetle optimization algorithm

Bayesian Learning Based Elitist Nondominated Sorting Algorithm for a Kind of Multi-objective Integrated Production Scheduling and Transportation Problem

This paper focuses on a kind of multi-objective integrated production scheduling and transportation problem (MIPSTP), which is encountered in many real-life industrial processes. MIPSTP contains a production stage for processing products and a transportation stage for transporting products. In MIPSTP, we consider a dedicated integration of distributed scheduling and transportation regarding the two stages, arising from a practical project for a cooperative iron and steel company. The objective of MIPSTP is to minimize the total completion time of each factory and total transportation cost. To solve the problem, a Bayesian learning-based elitist nondominated sorting algorithm (BLENSA) is proposed. The primary highlights of this work are two-fold: 1) new solution structures of MIPSTP and 2) novel search model of BLENSA. For the solution structures, we propose for the first time the mathematical description of MIPSTP, accounting for several features in applications and so is different from conventional scheduling problems. For the search model of BLENSA, we propose a Bayesian learning-based probability model to learn valuable knowledge about nondominated solutions. Thereby, BLENSA combines the Bayesian learning-based probability model to produce a candidate population (CP) and the nondominated sorting method to generate a main population (MP). Next, we propose a greedy competition strategy to construct MP from CP and MP for the next generation. Results of experiments on 57 test instances and a real-life case study demonstrate the effectiveness and practical values of BLENSA.

Comprehensive benefit evaluation of mineral resources development based on dual population constrained multi-objective evolutionary algorithm

With the increasing global demand for mineral resources, the comprehensive benefit evaluation of mineral resources development is playing an increasingly important role in the field of mineral resources development. However, there are few attempts to use multi-objective evolutionary algorithms (MOEAs) to study the comprehensive benefit evaluation problem of mineral resources development. To effectively solve this problem, this study established a multi-objective optimization model for comprehensive benefit evaluation of mineral resources development and proposed a dual-population constrained multi-objective evolutionary algorithm (CMOEA). The algorithm uses two populations to organically combine the efficient convergence of the non-dominated sorting genetic algorithm II (NSGA-II) with the advantage of the adaptive geometry estimation based multi-objective evolutionary algorithm (AGE-MOEA) in estimating the shape of the Pareto front, named co-evolution based on non-dominated sorting genetic algorithm and adaptive geometry estimation (C-NSGA-AGE). 3 test suites are used to verify the performance of the proposed algorithm. Experimental results show that compared with competing algorithms, C-NSGA-AGE has strong competitiveness in most test functions. The proposed method is used to solve the model, and five optimal comprehensive benefit evaluation schemes are given for different objectives. Among them, the maximum benefit is obtained when each objective is developed in a balanced way, which reaches 0.837181.

Rolling schedule design for the ESP rolling process based on NSGA-II-DE.

Multiple processes connected closely during the endless strip production (ESP) rolling, it is difficult to obtain the global optimal solution by multi-objective modelling of a single process, and the parameters to be optimized coupled with each other. To obtain the optimal solution, a multi-objective optimization model combining the power consumption, product quality, and loading balance was proposed for the design of an ESP rolling schedule. The thickness and heating temperature were simultaneously taken as the decision variables for coupling the temperature and loading in the rolling process, and the non-dominated sorting genetic algorithm-II (NSGA-II) based on differential evolution (NSGA-II-DE) was applied to obtain the Pareto solutions. To select an optimal solution, a satisfaction function was designed and applied to fully utilize the Pareto solutions. Furthermore, to prove the precision and efficiency of the method, the online schedule and that obtained by the NSGA-II method were compared. The results proved that the final selected solution had better quality and a more balanced loading force than the other two types, which could provide guidance for the actual production process.

A new multi-objective optimization algorithm for separation processes

This paper proposes a new algorithm, named guided population adaptive genetic algorithm (GAGA), to solve optimization problem of the complex separation processes. In GAGA, the strategies of adaptive crossover and mutation, leader selection, boundary random walk, neighborhood guidance and spiral updating position are introduced to enhance the guidance of population evolution. The capability of GAGA is investigated by 8 multi-objective benchmark problems. The results are compared with four well-known multi-objective optimization algorithms. The average values of inverted generational distance (IGD) and generational distance (GD) are 0.1626 and 0.5363, respectively, which is proved to be a robust and reliable model. Moreover, GAGA is validated through methacrylic acid (MAA) separation process with multi-cycle flows. The optimization efficiency of GAGA has accelerated by 2 times compared with non-dominated sorting genetic algorithm-II (NSGA-II), with results of 4.7% reduction in total annual cost (TAC) and 4.3% reduction in global energy consumption (GEC).

A Cross-Layer Game-Theoretic Approach to Resilient Control of Networked Switched Systems Against DoS Attacks.

This article investigates the resilient control strategies of networked switched systems (NSSs) against denial-of-service (DoS) attacks and external disturbance. In the network layer, both the defender and the attacker allocate energy over multiple channels. Considering the impact of switching characteristic in the physical layer on the network layer, a dynamic regulating factor is proposed to adjust the total energy of the defender. To optimize the signal-to-interference-noise ratio and energy consumption simultaneously at each player's side, a multiobjective game problem is formulated. Furthermore, a nondominated sorting genetic algorithm framework is employed, incorporating the knee point selection mechanism to attain the Pareto-Nash equilibrium, based on which the optimal defense strategy can be derived to achieve resilience against DoS attacks. In the physical-layer, taking the dynamic packet loss caused by DoS attacks and external disturbance into account, an H∞ minimax controller containing control inputs and the switching signal is designed to guarantee the optimal performance for NSSs through the dynamic game-theoretic approach. Finally, the networked continuous stirred tank reactor system is provided to verify the effectiveness of the proposed method.

Multi-objective mixed-model assembly line balancing with hierarchical worker assignment: A case study of gear reducer manufacturing operations

Assembly lines, generally speaking, can reduce production costs, shorten cycle times, and achieve higher quality levels. Since the current market is characterized by increasing product variability, mixed-model assembly lines, in which similar product models can be assembled simultaneously, are more suitable to respond to varied market demands than traditional single-model assembly lines. In addition, in an assembly line, tasks often differ in processing requirements, and workers may have different qualification levels. This study, therefore, aims to construct models for the multi-objective mixed-model assembly line balancing problem with hierarchical worker assignment (MO-MALBP-HW). The goal is to generate a suitable plan for a mixed-model assembly line balancing problem considering the constraint of a hierarchical workforce, the cost of a hierarchical workforce, and production cycle time. When the problem is simple, it can be solved by a mixed integer programming (MIP) model. When the problem becomes complex, it can be solved by a multi-objective genetic algorithm (MOGA) and a non-dominated sorting genetic algorithm II (NSGA-II) to obtain a near-optimal solution. The implementation of this model can effectively manage the multi-objective mixed-model assembly line balancing plan, thereby improving plant efficiency and reducing cost.

Optimization of design parameters and operation conditions of solar-air source heat pump coupled system for rural buildings in cold and severe cold regions

Integrating solar energy utilization into air source heat pump heating systems can effectively cut down on energy consumption. However, the complexity of coupled systems poses a challenge to system performance optimization. In this paper, a solar-air source heat pump coupled system designed for heating in cold (Beijing) and severe cold (Changchun) regions is developed and analyzed by TRNSYS software. Response surface methodology (RSM) is employed to establish regression models for different performance indicators, and then the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted to solve the multi-objective optimization of interactive performance parameters through Pareto optimal solution set. The regression models of the performance indicators, i.e., the energy consumption (P), the solar energy assurance rate (Ar) and the ratio of heating supply from solar energy storage tank to building load (Hs), are found to be closely related to the area of solar collectors (A) and the volumes of storage tanks (Vl for solar collector loop and Vs for ASHP loop). The obtained Pareto set successfully balances the optimal values of interactive performance indicators. In cold regions, P, Ar, Hs of the coupled system can be improved respectively by 31.79%, 33.00% and 40.07% compared to the single air source heat pump system. While in severe cold regions, these improvements are 16.25%, 15.35% and 28.38%, respectively. The design method and optimization procedure of this study provide a basis for the optimization of the solar and auxiliary heat source coupled heating systems.

Enhancing the performance of recycled aggregate green concrete via a Bayesian optimization light gradient boosting machine and the nondominated sorting genetic algorithm-III

Enhancing the performance of recycled aggregate green concrete via a Bayesian optimization light gradient boosting machine and the nondominated sorting genetic algorithm-III

Evolutionary architecture search for generative adversarial networks using an aging mechanism-based strategy

Generative Adversarial Networks (GANs) have emerged as a key technology in artificial intelligence, especially in image generation. However, traditionally hand-designed GAN architectures often face significant training stability challenges, which are effectively addressed by our Evolutionary Neural Architecture Search (ENAS) algorithm for GANs, named EAMGAN. This one-shot model automates the design of GAN architectures and employs an Operation Importance Metric (OIM) to enhance training stability. It also incorporates an aging mechanism to optimize the selection process during architecture search. Additionally, the use of a non-dominated sorting algorithm ensures the generation of Pareto-optimal solutions, promoting diversity and preventing premature convergence. We evaluated our method on benchmark datasets, and the results demonstrate that EAMGAN is highly competitive in terms of efficiency and performance. Our method identified an architecture achieving Inception Scores (IS) of 8.83±0.13 and Fréchet Inception Distance (FID) of 9.55 on CIFAR-10 with only 0.66 GPU days. Results on the STL-10, CIFAR-100, and ImageNet32 datasets further demonstrate the robust portability of our architecture.

Application of multilayered porous media for heat transfer optimization in double pipe heat exchangers using neural network and NSGA II

This study investigates the use of multi-layered porous media (MLPM) to enhance thermal energy transfer within a counterflow double-pipe heat exchanger (DPHE). We conducted computational fluid dynamics (CFD) simulations on DPHEs featuring five distinct MLPM configurations, analyzed under both fully filled and partially filled conditions, alongside a conventional DPHE. The impact of various parameters such as porous layer arrangements, thickness, and flow Reynolds numbers on pressure drop, logarithmic mean temperature difference (LMTD), and performance evaluation criterion (PEC) was assessed. The results demonstrate that the PEC improves by approximately three times with the optimal MLPM configuration, where the porous layers decrease from the interfacial wall outward under fully filled conditions. To further optimize performance, we trained a neural network using 507 simulations to establish a continuous correlation between input variables and output results. A multi-objective optimization was then implemented using the non-dominated sorting genetic algorithm II (NSGA-II) to identify the optimal operating conditions. The Pareto front of the optimal points was established, allowing designers to select specific points based on their operational requirements. This research provides valuable insights into the potential of MLPM to significantly enhance the thermohydraulic performance of DPHEs and establishes a framework for future optimization studies.

Time-optimal trajectory planning for a six-degree-of-freedom manipulator: a method integrating RRT and chaotic PSO

This study proposes an innovative algorithm based on a hybrid optimization strategy, integrating the rapidly-exploring random tree (RRT) and an improved particle swarm optimization (PSO) method to address the time-optimal trajectory planning problem for six-degree-of-freedom robotic arms. The proposed approach emphasizes obstacle avoidance and motion smoothness. RRT is utilized within a dynamic three-dimensional environment to rapidly generate an initial collision-free path. Subsequently, improved PSO enhances global search performance by introducing a multi-source chaotic mapping-based population initialization strategy, dynamically adjusted inertia weights, and nonlinear learning factors. These enhancements effectively mitigate the limitations of traditional PSO methods, which are prone to premature convergence in complex optimization problems. Furthermore, the proposed 3-5-3 polynomial interpolation method significantly smooths the trajectory, reducing fluctuations in velocity and acceleration, and thereby improving the precision and energy efficiency of trajectory planning. Experimental results demonstrate that the proposed algorithm outperforms existing methods, such as the improved RRT and improved non-dominated sorting genetic algorithm, across multiple metrics. Notable achievements include a reduction of total motion time by approximately 21%, improved stability in robotic arm motion, and enhanced adaptability to dynamic environments. Particularly, the method achieves superior trajectory diversity and uniformity through joint optimization of multiple chaotic sources, overcoming the inherent limitations of single chaotic mappings. This research expands the application scenarios of RRT and PSO and provides a novel solution for intelligent control and real-time planning of high-degree-of-freedom robotic arms.

An Integrated Method for Selecting Architecture Alternatives and Reconfiguration Options Towards System-of-Systems Resilience

Delivering persistent values in a dynamic environment is a challenging but imperative capability for a system-of-systems (SoS). Practitioners in the SoS and defense domains are exploring the benefits of the operational-level reconfiguration strategies via new operational concepts such as mosaic warfare. However, an architecture design that allows reconfiguration is also a crucial task, but has not yet received adequate attention, not to mention accounting for the mutual impact between architecture design alternatives and reconfiguration options. Therefore, this paper proposes an integrated method that can select the architecture with a specific inherent structure in the design phase that supports dynamic reconfiguration during the operational phase. This method firstly builds a structural framework that connects architecture design and reconfiguration, and identifies the enablers for SoS architecture reconfiguration. After developing an SoS effectiveness evaluator, the method constructs an integrated multi-objective formulation for the initial architecture selection and reconfiguration process, and provides a solution algorithm based on a fast non-dominated sorting genetic algorithm. An application to an air and missile defense SoS illustrates the effectiveness of the proposed method. The generated Pareto optimal set of solutions that have non-dominated recoverability and survivability provide useful decision support for SoS composition and initial architecture configuration, based upon which an SoS can also respond effectively to disruptions by computing the reconfiguration decisions.

Multi-objective optimisation for urban road network maintenance based on spatial design network analysis: trade-offs in performance, environmental impacts and costs

ABSTRACT Pavement maintenance decision-making needs to account for multiple conflicting strategic objectives and the complex interdependencies among heterogeneous road sections within road networks. Using spatial design network analysis which allows to capture the complex interdependencies among interconnected road sections, a multi-objective optimisation model is developed for urban road network maintenance. This model integrates multi-dimensional objectives: maximising road network condition, minimising environmental impacts, and minimising agency costs. The model, which uses the non-dominated sorting genetic algorithm II to approximate the Pareto front, is implemented in a real-world urban road network in Shanghai, China. The results show that the trade-offs among the three objectives are more intricate than those found in prior research using traditional decision-making models, which often overlooked the complex interdependencies among heterogeneous road sections. Sensitivity analyses indicate prominently non-linear relationships among objectives: marginal changes in road network condition and environmental impacts fluctuate with different agency costs. As an exploratory effort to develop a network-level multi-objective optimisation model for urban road maintenance through modelling complex interconnections among heterogeneous road sections, this study contributes to a deepened understanding of the complex non-linear associations among performance-environment-cost objectives in pavement maintenance decision-making and offers a network-based approach to perform trade-offs in these conflicting objectives.

Multi-Objective Optimization Accelerates the De Novo Design of Antimicrobial Peptide for Staphylococcus aureus.

Humans have long used antibiotics to fight bacteria, but increasing drug resistance has reduced their effectiveness. Antimicrobial peptides (AMPs) are a promising alternative with natural broad-spectrum activity against bacteria and viruses. However, their instability and hemolysis limit their medical use, making the design and improvement of AMPs a key research focus. Designing antimicrobial peptides with multiple desired properties using machine learning is still challenging, especially with limited data. This study utilized a multi-objective optimization method, the non-dominated sorting genetic algorithm II (NSGA-II), to enhance the physicochemical properties of peptide sequences and identify those with improved antimicrobial activity. Combining NSGA-II with neural networks, the approach efficiently identified promising AMP candidates and accurately predicted their antibacterial effectiveness. This method significantly advances by optimizing factors like hydrophobicity, instability index, and aliphatic index to improve peptide stability. It offers a more efficient way to address the limitations of AMPs, paving the way for the development of safer and more effective antimicrobial treatments.

Task scheduling in cloud computing systems using multi-objective honey badger algorithm with two hybrid elite frameworks and circular segmentation screening

In cloud computing environment, task scheduling is the most critical problem to be solved. Two different multi-objective honey badger algorithms (MOHBA-I and MOHBA-II) based on hybrid elitist framework and circular segmentation screening are proposed for the multi-objective problem of task scheduling optimization in cloud computing systems. MOHBA-I and MOHBA-II combine the grid indexing mechanism and decomposition technique, respectively, to select better populations based on elite non-dominated sorting. A circular segmentation screening mechanism was proposed to retain the superior individuals when the regional density is too high to further maintain the diversity of the populations, and attach an external archive to preserve the uniformly diversified Pareto decomposition set. The performance of the proposed algorithms is verified by using test functions. MOHBA-I and MOHBA-II achieve the first and third rankings, respectively, compared to other classical multi-objective algorithms. Solve the cloud computing task scheduling problem using time, load and price cost as metrics, test for different task sizes, and compare MOHBA-I with algorithms such as NSGA-III, MOPSO and MOEA/D in the same experimental environment. When facing a large-scale task, MOHBA-I ranks first in HyperVolume value with 2.4449E−02 for two objectives and 9.2950E−03 for three objectives. The experimental results show that MOHBA-I finds the highest number of solutions with better convergence and coverage, obtaining a satisfactory Pareto front, which can provide more and better choices for decision makers.

Optimising intersection signal plan under mixed traffic flow: hybrid non-dominated sorting genetic algorithm III and simulation approach

Traffic signalling plays a leading role in effectively alleviating traffic congestion. Previous studies have mainly focused on optimising traffic signal plans under homogeneous traffic flow. However, real-life traffic is predominantly mixed flow, limiting the widespread use of existing methods. This study considers the multi-objective signal plan optimisation problem for intersections under mixed traffic flow. The aim is to obtain a signal plan that improves operational efficiency. A multi-objective model is constructed using average delay, average waiting time and average speed through the intersection as objective functions. A hybrid method that combines non-dominated sorting genetic algorithm III (NSGA-III) with simulation is proposed to solve the above model. The simulation software SUMO is utilised to simulate traffic flow. Two existing left-turn bypass intersections in Pulau Pinang, Malaysia are selected as case studies. Results indicate that traffic signal plans obtained by the proposed method have more obvious advantages compared with existing signal plans. The proposed method reduces delay and waiting time by 48% and 56%, respectively, and increases the speed by 6% for the first intersection, and reduces delay and waiting time by 38% and 43%, respectively, and increases speed by 15% for the second intersection.

Transformer model-based multi-scale fine-grained identification and classification of regional traffic states

To address the limitations in precision of conventional traffic state estimation methods, this article introduces a novel approach based on the Transformer model for traffic state identification and classification. Traditional methods commonly categorize traffic states into four or six classes; however, they often fail to accurately capture the nuanced transitions in traffic states before and after the implementation of traffic congestion reduction strategies. Many traffic congestion reduction strategies can alleviate congestion, but they often fail to effectively transition the traffic state from a congested condition to a free-flowing one. To address this issue, we propose a classification framework that divides traffic states into sixteen distinct categories. We design a Transformer model to extract features from traffic data. The k-means algorithm is then applied to these features to group similar traffic states. The resulting clusters are ranked by congestion level using non-dominated sorting, thereby dividing the data into 16 levels, from Level 1 (free-flowing) to Level 16 (congested). Extensive experiments are conducted using a large-scale simulated traffic dataset. The results demonstrate significant advancements in traffic state estimation achieved by our Transformer-based approach. Compared to baseline methods, our model exhibits marked improvements in both clustering quality and generalization capabilities.