Multi-objective Framework Research Articles

Optimizing sustainable bio-fuel supply chain: a multi-objective model utilizing micro-algae resources

AbstractAs energy demands continue to rise and concerns about the impact on the environment of fossil fuels, renewable energy sources have emerged as viable alternatives. Bio-fuels can replace non-renewable energy sources. Micro and macro-algae, animal fat, leftover cooking oil and vegetable oil can be utilised to make bio-diesel. Micro-algae is readily available and affordable, making it a viable alternative to these sources. A bio-diesel supply chain that supports large-scale production must be developed quickly. A multi-objective framework is used to construct a sustainable micro-algal bio-diesel supply chain. Bio-diesel supply chain economic objective and environmental impact is minimized in the proposed approach. Bio-diesel is advertised as a fossil fuel replacement. Bio-refineries, bio-diesel depositories, multi-harvesting centres and supply sites for micro-algae (Chlorella, chlamydomonas and Scenedesmus) are considered. The suggested multi-objective model is optimized by implementing the $$Genetic-Algorithm$$ G e n e t i c - A l g o r i t h m . The framework and solution technique are validated using numerical illustrations. Findings indicate that bio-diesel production facility starting expenses comprise 59% of overall costs, showing high installation costs in the supply chain. Additionally, their environmental impact accounts for 67% of total environmental installation impact. A sensitivity analysis is performed to determine the feasibility of this study. The suggested paradigm could potentially be beneficial for traders and governing bodies with an interest in bio-fuel industry.

A Robust Multi-Objective Evolutionary Framework for Artificial Island Construction Scheduling Under Dynamic Constraints

A Robust Multi-Objective Evolutionary Framework for Artificial Island Construction Scheduling Under Dynamic Constraints

Branching evolution for unknown objective optimization in biclustering

Biclusters hold significant importance in microarray analysis. Given the EA algorithm’s efficacy in tackling nonlinear problems, it has become a prevalent choice for evolutionary biclustering in microarray analysis. However, in conventional approaches, the objective of bicluster volume remains elusive, as it heavily relies on the yet-to-be-discovered real bicluster. This discrepancy introduces a novel research problem termed “unknown objective optimization” in our study. To address this issue, our paper introduces an innovative branching evolution strategy within a multi-objective framework. This strategy aims to resolve the challenge of unknown objectives. Throughout the biclustering search process, we meticulously observe the evolution of optimal bicluster individuals. Stability in both mean squared residue (MSR) and volume suggests a high likelihood of reaching an optimal solution, whether local or global. If a global optimal solution is attained at the end of the final evolution, our initial assumption is validated; otherwise, it necessitates an update. The proposed branching strategy is subsequently implemented to bifurcate the original evolution into two branches. One continues the original evolution with an unknown objective of bicluster volume, while the other pursues a new evolution with an estimated objective of bicluster volume. Our algorithm’s performance is assessed through comparisons with various traditional and evolutionary biclustering algorithms. The experimental results affirm its enhanced efficacy on both synthetic datasets and real gene datasets.

A multi-objective decision-making framework for the choice between mutually exclusive alternatives under uncertainty: Assessing the competitiveness of offshore wind for a gas field electrification on the NCS

A multi-objective decision-making framework for the choice between mutually exclusive alternatives under uncertainty: Assessing the competitiveness of offshore wind for a gas field electrification on the NCS

A Multi-Objective Simulation-Optimization framework for water resources management in canal-well conjunctive irrigation area based on nexus perspective

Water scarcity drives the nexus of water-food-energy-ecosystem (WFEE) in arid and semi-arid agricultural irrigation areas, while the impacts of different irrigation strategies can propagate from water sector to other sectors through groundwater system. In this work, a Multi-Objective Simulation Optimization (MOSO) framework that couples the numerical groundwater model (MODFLOW) with the multi-objective optimization model (NSGA-III) is proposed to coordinate multiple resource systems with conflicting interests based on nexus perspective by reasonably allocating irrigation water resources from surface water diversion and groundwater abstraction. The management objectives include maximization of water use efficiency and food economic efficiency, as well as minimization of energy consumption efficiency and the impact of irrigation on ecosystem subject to groundwater levels, surface water supply and irrigation water demand. To demonstrate the feasibility of the MOSO framework, a water resources management problem in typical canal-well irrigation area − Baojixia Irrigation Area (BIA), located in Shaanxi province in northwest China, has been solved. The major findings are: (a) for every 10 % increase in the proportion of groundwater usage across the total irrigation area, water use efficiency decreases by 0.05 kg/m3, energy consumption efficiency increases by 900 CNY/ha, the ecosystem impact index decreases by 0.1, and food economic efficiency remains unchanged; (b) the four-dimensional Pareto-optimal solutions comprehensively consider all the impacts of the WFEE nexus system, avoiding decision shortsightedness caused by a low-dimensional perspective; (c) spatial differences in irrigation strategies across different zones are primarily influenced by crop structure, hydrogeological parameters, and surface elevation, while temporal variations are mainly concentrated during the maize irrigation period. The framework based on water resources management and groundwater dynamics can efficiently evaluate trade-offs and synergies among nexus sectors, providing far-sighted suggestions for policy makers to improve resource-use efficiency, while mitigating the impact of irrigation on ecosystem.

A Practical Multiobjective Learning Framework for Optimal Hardware-Software Co-Design of Control-on-a-Chip Systems

A Practical Multiobjective Learning Framework for Optimal Hardware-Software Co-Design of Control-on-a-Chip Systems

Multi-objective Optimization Design Framework and Flight Performance Prediction for Reentry Module Using a Rapid Analysis Program

Multi-objective Optimization Design Framework and Flight Performance Prediction for Reentry Module Using a Rapid Analysis Program

A multi-objective reinforcement learning framework for real-time drilling optimization based on symbolic regression and perception

In the context of the global energy transition, optimizing deep-water oil and gas drilling parameters is crucial for ensuring safety while improving efficiency. Traditional methods face limitations in highly dynamic and nonlinear drilling environments, struggling to balance speed and cost-effectiveness. Furthermore, these methods rely on real-time logging-while-drilling (LWD) data for decision-making, but delays in data collection and processing hinder timely adjustments of drilling parameters, affecting decision accuracy and responsiveness. This paper proposes a multi-objective drilling parameter optimization framework, incorporating symbolic regression, time-series networks, and Markov decision processes to precisely predict ROP, formation conditions, and optimize drilling parameters in real time. Key innovations include a multi-population evolutionary symbolic regression algorithm for constructing empirical equations, the integration of variational mode decomposition (VMD) and sample entropy for data preprocessing, and multi-head self-attention time-series networks to enhance prediction accuracy. Quantile regression further estimates the range of drilling parameter adjustments. Additionally, a drilling parameter optimization deep deterministic policy gradient (DPODDPG) algorithm was developed to automate real-time parameter adjustments. Empirical analysis on the Ledong 10-1 block in the South China Sea demonstrated significant improvements: ROP increased from 54.18 m/hr to 122.17 m/hr, mechanical specific energy (MSE) decreased from 100.82 MPa to 97.78 MPa, and cost per foot reduced from 121.16 × 102 CNY/m to 51.31 × 102 CNY/m. Compared to traditional methods, the proposed framework showed clear advantages in enhancing ROP, reducing MSE, and controlling costs, further validating its superiority in complex drilling environments. This method not only significantly improves drilling efficiency and economic benefits but also adapts to complex and changing drilling conditions, showing broad application potential, particularly in challenging deep-water oil and gas drilling operations, where it can provide more efficient and reliable optimization solutions.

A comparative study of evolutionary algorithms and particle swarm optimization approaches for constrained multi-objective optimization problems

Many real-world optimization problems contain multiple conflicting objectives as well as additional problem constraints. These problems are referred to as constrained multi-objective optimization problems (CMOPs). Many meta-heuristics for solving CMOPs, called constrained multi-objective meta-heuristics (CMOMHs) have been introduced in the literature, including those using particle swarm optimization (PSO)(Kennedy and Eberhart, 1995), genetic algorithms (GAs)(Man et al., 1996), and differential evolution (DE)(Storn and Price, 1997). CMOMHs can be grouped into four different classes: classic CMOMHs, co-evolutionary approaches, multi-stage approaches, and multi-tasking approaches. An extensive comparative study of twenty different CMOMHs on a wide variety of test problems, including real-world CMOPs in the fields of science and engineering, is conducted. A multi-swarm PSO approach called constrained multi-guide particle swarm optimization (ConMGPSO) is introduced and compared to the best-performing previous approaches according to the comparative study. The performance of each algorithm was found to be problem dependent, however the best overall approaches were ConMGPSO, paired-offspring constrained evolutionary algorithm (POCEA)(He et al., 2021), adaptive non-dominated sorting genetic algorithm III (A-NSGA-III)(Jain and Deb, 2014), and constrained multi-objective framework using Q-learning and evolutionary multi-tasking (CMOQLMT)(Ming and Gong, 2023). ConMGPSO and POCEA had the best performance on the CF benchmark set, which contains examples of bi-objective and tri-objective CMOPs with disconnected CPOFs. The CMOQLMT approach had the best performance on the DAS-CMOP benchmark set, which contain additional difficulty in terms of feasibility-, convergence-, and diversity-hardness. For the selected real-world CMOPs, A-NSGA-III had the best performance overall. ConMGPSO was shown to have the best performance on the process, design, and synthesis problems, and had competitive performance for the power system optimization problems.

The synergetic competition of cross-regional integrated energy systems with low-carbon technology heterogeneity under carbon emission trading mechanism to achieve carbon reduction target

The integration of renewable and fossil energy in a Region Integrated Energy System (RIES) is recognized as an effective measure for carbon emission reduction. The emergence of carbon emission trading (CET) markets worldwide has raised the need to combine RIES optimal planning with CET to achieve carbon reduction targets. This study focuses on the synergetic competition of a cross-RIES system with low-carbon technology heterogeneity under the CET mechanism. A cross-RIES model is constructed, incorporating fossil Carbon Capture System (fossil-CCS), renewable Combined Cooling Heating and Power (renewable-CCHP), and cleaner production paradigms. An improved version of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to propose a bi-level multi-objective optimal planning framework for the cross-RIES model, employing a grandfather permit allocation method. Comparative analysis is conducted between cross-RIES optimal planning with and without considering the CET mechanism, with a focus on environmental indexes such as carbon emission, carbon intensity, and marginal abatement cost. Sensitivity studies are also performed to explore the impact of key CET design parameters on the system's environmental performance. The simulation results demonstrate that the CET mechanism effectively reduces carbon emission, primary energy consumption, and carbon intensity by promoting synergetic competition among the low-carbon technology heterogeneity of RIESs. Furthermore, the environmental performance varies among the three types of RIES. In terms of CET parameter design, lowering the cap constraint, reducing the allowance allocation ratio of fossil-CCS RIES, and increasing the trading price contribute significantly to reducing carbon emission and carbon intensity, albeit at the expense of higher marginal abatement cost.

Surrogate-assisted multi-objective Bayesian optimization for improved rheological design of bioinks

Extrusion bioprinting is a popular biofabrication technique for creating viable biological constructs with applications ranging from regenerative medicine and cancer research to therapeutics screening. A primary challenge within extrusion bioprinting lies in preserving cell viability and function amidst the shear forces generated during extrusion. Therefore, the formulation of shear-thinning bioinks with tailored flow properties is essential to minimize shear stress accumulation during printing. However, this task is complex, as the bioink’s flow properties are contingent upon numerous factors, including but not limited to the polymer concentration, spatial cell density in the bioink, molecular weight of the polymer, and the nozzle’s geometry. The current methods for adjusting flow properties are heuristic, posing challenges to the rapid development models and automation of the bioprinting workflow. In this study, we introduce a Gaussian Process-based multi-objective Bayesian optimization framework that rapidly identifies Pareto-optimal flow property values to minimize hydrodynamic shear stresses during extrusion. The optimization focuses on four flow parameters, minimum viscosity, maximum viscosity, consistency index, and flow behavior index from the power-law model of shear-thinning fluids alongside the nozzle outlet diameter. Following an initial set of 24 experimental runs, our sequential design approach required 21 iterations to achieve convergence on a set of flow parameters that meet the Pareto optimality criteria. The presented strategy, which integrates physics-based numerical models with data-driven optimization models, emerges as a tool for the rapid fine-tuning of bioink flow behaviors. The multi-objective framework also sets the stage for future exploration into cell viability and functional outcomes post-bioprinting.

Logistics Performance Index-driven in operational planning for logistics companies: A smart transportation approach

The responsibility of Logistics Companies (LCs) extends beyond simply handling logistical processes and choosing transportation routes; they focus on being competitive and efficient in their operations. In multiple origins and destinations networks, LCs attempt to select the most optimal routes from each point. To address this challenge and reduce the time needed to identify potential routes, a novel method has been developed in this research for LCs. In operational planning, additional factors need to be considered to increase efficiency, customer satisfaction, and competitiveness. To confront this issue, the study proposes a novel model and contributions, inspired by the LPI, to apply the transformative impact of smart ports, the integration of smart containers, the choices of clearing and forwarding agents and port operators, and time uncertainty in the multi-objective framework. Specifically, the mathematical model's objectives include cost, time, customer satisfaction, and environmental impact in the periodic multimodal network. An innovative customer satisfaction function is introduced by integrating the selection of smart containers and smart ports, emphasizing their impact on enhancing customer satisfaction, while addressing time uncertainty through a chance-constrained approach and coefficients of variation. The model is solved using goal programming, and the results show that smart ports and smart containers can majorly affect transportation time and customer satisfaction. Furthermore, comparing the results to other studies demonstrates its superiority in decision-making for LCs, particularly by including time uncertainty and the role of clearing and forwarding agents and port operators. Therefore, the model holds practical significance in lowering costs, enhancing customer satisfaction, and facilitating smart international logistics. This research offers insights that are not only useful for the LCs but also for other stakeholders in the transport industry.

Optimization of ultrasound-assisted extraction of bioactive compounds from Carthamus caeruleus L. rhizome: Integrating central composite design, Gaussian process regression, and multi-objective Grey Wolf optimization approaches

The prediction of ultrasound-assisted extraction (UAE) for total phenolic content (TPC) and total flavonoid content (TFC) from Carthamus caeruleus L. rhizomes was conducted using a Gaussian process regression model (GPR) with a multi-objective Grey Wolf optimization approach (MOGWO). A central composite design (CCD) was employed first, examining ethanol concentration, temperature, time, and solvent-to-solid ratio as independent variables. TPC and TFC responses were analyzed under various conditions, revealing significant quadratic and interaction effects (p < 0.05). The GPR was then utilized to predict TPC and TFC, showing high accuracy with correlation coefficients near 1 and minimal root mean square error (RMSE) values. To simultaneously maximize TPC and TFC, the MOGWO was used in a multi-objective framework. Validation through CCD and GPR highlighted GPR's superior predictive accuracy. Optimal conditions (10% ethanol, 40°C, 20minutes sonication, and 50mLg-1 solvent to solid ratio) showed significant discrepancies in CCD predictions but high accuracy in GPR predictions. An interactive tool predicts TPC and TFC using CCD and GPR models. Users input extraction parameters and receive predictions, with a GWO-based optimization module for optimal conditions. The interface enables model comparison, improves process understanding, and optimizes bioactive compound extraction.

A multi-objective optimization framework for regional land-use allocation: Fully utilizing terrestrial vegetation to mitigate carbon emissions

Human-induced changes in land use and land cover (LULC) considerably impact the global carbon cycle. Rational LULC optimization with full utilization of terrestrial vegetation facilitates reconciling carbon reduction and socio-economic development. To this end, this study develops a multi-objective framework for low-carbon LULC spatial optimization that incorporates carbon suitability, historical suitability, and socio-economic needs. The framework is applied to Hubei Province, China. The key findings include: (1) Considering spatial variations in vegetation carbon sinks could lead to remarkable carbon reductions of 2.75 × 106 t C by 2030 and 7.68 × 106 t C by 2060. It would also lower carbon emissions per unit of gross domestic product (GDP) and enhance carbon sequestration per unit of vegetation by 2030, signifying enhanced carbon reduction efficiency. (2) Integrating carbon sequestration potential and historical suitability can considerably minimize emissions while sustaining socio-economic growth. Our method, compared with strategies based solely on historical development laws, can reduce carbon emissions by approximately 1.3–1.6 × 106 t C·a−1. (3) We also propose targeted low-carbon development suggestions for different LULCs. This study provides optimal spatial LULC allocations and strategic advice for regional LULC planning aimed at low-carbon development, demonstrating the feasibility of reconciling the tension between carbon reduction and socio-economic growth at the regional scale.

Fuzzy optimization of the photo-Fenton process on o-toluidine degradation in the aspect wastewater treatment

Significant volumes of wastewater, particularly from the textile industry, pose environmental concerns due to the presence of hazardous substances such as ortho-toluidine (OT). The photo-Fenton process can be used to break down and remove this hazardous organic compound. Previous studies on the photo-Fenton process have focused on local optimization of operating variables without considering cost factors. The photo-Fenton process is studied in this paper with UVA irradiation, Fe2+ dosage, and H2O2 concentration considered as variables. The study uses fuzzy optimization in a multi-objective framework for making decisions to determine the optimal values of OT degradation with its corresponding cumulative uncertainty error (YA), and the total operating cost (CT), both of which are essential for assessing the techno-economic feasibility of the process. The Pareto front was generated from the objective functions to establish the boundary limits for YA and CT. The results show an overall satisfaction level of 71.81% for the objective functions, indicating a partially satisficing solution for maximizing OT degradation while minimizing operating cost. The optimum conditions of the variables require 85.70 W m−3 UVA irradiation, 0.5177 mM for Fe2+ dosage, and 7.85 mM for the H2O2 concentration. These conditions yielded an OT degradation value of 83.22% and a total operating cost of 768.61 USD·m−3. Comparison with previous literature showed an OT degradation efficiency that was 16.78% lower. However, this tradeoff in the process efficiency is offset by a total operating cost that is 2.28 times cheaper, emphasizing the cost-effectiveness of the fuzzy optimized solution.

Multi-Objective Framework to Balancing Fairness and Accuracy for Debiasing Machine Learning Models

Multi-Objective Framework to Balancing Fairness and Accuracy for Debiasing Machine Learning Models

Multi-Objective Modeling Framework for Environmental Flow Optimization in a River-Reservoir System Using Histogram Comparison Approach for Estimation of Hydrologic Alteration

Multi-Objective Modeling Framework for Environmental Flow Optimization in a River-Reservoir System Using Histogram Comparison Approach for Estimation of Hydrologic Alteration

An improved meta-heuristic method for optimal optimization of electric parking lots in distribution network

In this study, a stochastic multi-objective structure for optimization of the intelligent electric parking lots (EPLs) is implemented in the distribution network for minimizing the power losses annual costs, power purchased from the main grid, unsupplied energy of subscribers, cost of vehicles to the grid as well as minimizing the network voltage deviations considering battery degradation cost (BDC) and network load uncertainty (NLUn). In this research, the unscented transformation method (UTM) is used for NLUn modeling and this method is easily applicable and has a low computational cost. An improved meta-heuristic algorithm named improved fire hawks optimization (IFHO) is utilized for decision variables finding defined as the site and size of the EPLs in the distribution network. The conventional fire hawks optimization (FHO) algorithm is inspired by the fire hawks foraging behavior and in this research, the Taylor-based neighborhood technique (TBNT) is used to reduce the dependency and the possibility of becoming trapped in local optimal. To evaluate the proposed methodology, the simulations are implemented in three scenarios (1) EPLs optimization without BDC and NLUn based-UTM, (2) EPLs optimization with BDC and without NLUn, and (3) EPLs optimization with BDC and NLUn. The results of the third scenario considering BDC and NLUn showed that the EPLs optimization integrated with a multi-objective framework by finding the EPL's optimal size and capacity in the network via the IFHO has reduced the annual losses, voltage deviations, ENS cost, and substation cost by 21.06%, 12.15%, 70.82%, and 39.10%, respectively compared to the base distribution network. Additionally, the results demonstrated that incorporating the BDC and NLUn, the annual losses, voltage oscillations, ENS cost, grid cost, and EPLs have increased in comparison with the EPLs optimization without BDC and NLUn based-UTM. In addition, the TBNT based-IFHO superiority has been confirmed in different scenarios by achieving better values of the objectives and also obtaining the convergence process with lower convergence tolerance and higher convergence accuracy.

A Novel Multiobjective Genetic Programming Approach to High-Dimensional Data Classification.

The development of data sensing technology has generated a vast amount of high-dimensional data, posing great challenges for machine learning models. Over the past decades, despite demonstrating its effectiveness in data classification, genetic programming (GP) has still encountered three major challenges when dealing with high-dimensional data: 1) solution diversity; 2) multiclass imbalance; and 3) large feature space. In this article, we have developed a problem-specific multiobjective GP framework (PS-MOGP) for handling classification tasks with high-dimensional data. To reduce the large solution space caused by high dimensionality, we incorporate the recursive feature elimination strategy based on mining the archive of evolved GP solutions. A progressive domination Pareto archive evolution strategy (PD-PAES), which optimizes the objectives in a specific order according to their objectives, is proposed to evaluate the GP individuals and maintain a better diversity of solutions. Besides, to address the seriously imbalanced class issue caused by traditional binary decomposition (BD) one versus rest (OVR) for multiclass classification problems, we design a method named BD with a similar positive and negative class size (BD-SPNCS) to generate a set of auxiliary classifiers. Experimental results on benchmark and real-world datasets demonstrate that our proposed PS-MOGP outperforms state-of-the-art traditional and evolutionary classification methods in the context of high-dimensional data classification.

A multi-variable calibration framework at the grid scale for integrating streamflow with evapotranspiration data to improve the simulation of distributed hydrological model

Study regionThe Ganjiang River Basin, China Study focusParameter calibration is crucial for the accurate and reliable operation of hydrological models. Traditional methods face challenges in calibrating spatially heterogeneous parameters of distributed hydrological models, and existing multi-variable calibration strategies often fall short in comprehensively improving model performance, particularly in streamflow simulations. To address these challenges, this study proposes a multi-objective calibration framework that integrates observed streamflow data and satellite-based evapotranspiration (ET) data. The spatiotemporal information of the merged ET is utilized to calibrate six hydrological parameters of the Variable Infiltration Capacity (VIC) model at the grid scale, enhancing hydrological simulations for the Ganjiang River basin. New hydrological insightsCompared to the benchmark scheme based solely on streamflow, the proposed calibration framework improves simulations of area-average ET at the sub-basin scale and soil moisture content in the Ganjiang River basin, without compromising the accuracy of daily streamflow simulations. Additionally, notable enhancements are observed in monthly streamflow simulations. This study provides a promising and comprehensive calibration framework using satellite-based data to constrain parameters and enhance the performance of distributed hydrological models.