Trade-off Problem Research Articles

UDTCWT-PHFMs Domain Statistical Image Watermarking Using Vector BW-Type R Distribution

For any image watermarking algorithm, how to achieve the trade-off among robustness, imperceptibility, and watermark capacity is a challenging problem because of their mutually constrained relationship. In this paper, we design a statistical-based image watermarking system to solve the trade-off problem. In embedding process, consider the imperceptibility and the robustness, we inventively combine the undecimated dual tree complex Wavelet transform (UDTCWT) and the polar harmonic Fourier moments (PHFMs) to obtain the UDTCWT-PHFMs magnitudes as the watermark carriers, and we embed watermark signals in multiplicative manner. In modeling phase, by analyzing the statistical property and the multiple correlations of the UDTCWT-PHFMs magnitudes, we bound the magnitudes of different directions into the vector groups, and then we model them with the vector Roy type bivariate Weibull (BW-Type R) distribution so that we can capture accurately the marginal characteristics, the inter-scale dependencies, and the inter-orientation dependencies at the same time. Moreover, we obtain the model parameters with the double looped maximum likelihood estimation (double-looped MLE). Benefit from the reliable modeling result, we finally derive a novel specific image watermark decoder to blindly extract the hidden watermarks. Extensive experiment results declare the designed statistical image watermarking system achieves the better balance among imperceptibility, robustness, and payload.

CO2 pollution reduction: a tradeoff for fully fuzzy parameters in a megaproject optimization

PurposeThis paper aims to conduct a fuzzy discrete time cost quality risk in the ambiguous mode CO2 tradeoff problem (FDTCQRP*TP) in a megaproject based on fuzzy ground.Design/methodology/approachA combinatorial evolutionary algorithm using Fuzzy Invasive Weed Optimization (FIWO) is used in the discrete form of the problem where the parameters are fully fuzzy multi-objective and provide a space incorporating all dimensions of the problem. Also, the fuzzy data and computations are used with the Chanas method selected for the computational analysis. Moreover, uncertainty is defined in FIWO. The presented FIWO simulation, its utility and superiority are tested on sample problems.FindingsThe reproduction, rearrangement and maintaining elite invasive weeds in FIWO can lead to a higher level of accuracy, convergence and strength for solving FDTCQRP*TP fuzzy rules and a risk ground in the ambiguous mode with the emphasis on the necessity of CO2 pollution reduction. The results reveal the effectiveness of the algorithm and its flexibility in the megaproject managers' decision making, convergence and accuracy regarding CO2 pollution reduction.Originality/valueThis paper offers a multi-objective fully fuzzy tradeoff in the ambiguous mode with the approach of CO2 pollution reduction.

Spectral and Energy Efficiency Trade-Off in UAV-Based Olive Irrigation Systems

Precision agriculture, also referred to as smart farming, is one of the main pillars of modern society to achieve the Sustainable Development Goals (SDGs). Precision agriculture aims to improve the quality and quantity of production while conserving scarce natural resources. Smart farming has grown in recent years thanks to the adoption of modern technologies, including artificial intelligence (AI) and the Internet of Things (IoT). In this work, we consider an irrigation system for olive orchards based on unmanned aerial vehicles (UAVs). Specifically, UAVs ensure remote sensing (RS), which offers the advantage of collecting vital information on a large temporal and spatial scale (which cannot be achieved with traditional technologies). However, UAV-based irrigation systems face tremendous challenges due to the various requirements of a powerful computing ability, battery capacity, energy efficiency, and spectral efficiency for different connected devices. This paper addresses the energy efficiency and spectral efficiency trade-off problem of UAV-based irrigation systems. We propose to adopt massive multiple input, multiple output (M-MIMO) technology to ensure wireless communication. In fact, this technology plays a significant role in future sixth-generation (6G) wireless mobile networks and has the potential to enhance the energy efficiency as well as the spectral efficiency. We design a network model with a three-layered architecture and analytically compute the achievable spectral efficiency and the energy efficiency of the studied system. Then, we numerically determine the optimal number of ground base station antennas as well as the optimal number of IoT devices that should be used to ensure the maximum energy efficiency while guaranteeing a high spectral efficiency. The numerical results prove that the proposed UAV-based irrigation system outperforms conventional systems and demonstrate that the best spectral and energy efficiency trade-off is obtained by using the M-MMSE combiner.

Effect of H2O vapor on plasma-assisted partial oxidation of CH4 over PtOx/BN nanoribbon aerogel catalysts

Ultralight BN nanoribbon aerogels confined highly dispersed PtOx nanoparticles (NPs) catalysts (PtOx/BN-na) were prepared and utilized for plasma-assisted partial oxidation of CH4 (POM) in a CH4−O2 flow system with H2O vapor cofeed at low temperature. The synergistic effect of H2O vapor and Pt2/BN-na catalyst overcomes a tradeoff problem between CH4 conversion and oxygenate selectivity, achieving 95% selectivity towards incomplete oxidation products at a 13% CH4 conversion. The total oxygenate productivity reached 106.5 mmoloxy gcat−1 h−1, which is one of the best results among the state-of-the-art catalysts for gas and liquid POM reaction. Based on experimental results of D and O18 isotope labelling, in situ OES, and in situ DRIFTS, the surface reactive hydroxyl groups or/and •OH radical, generated from the plasma-activated reaction of H2O + O2 on PtOx NPs active sites, are suggested as key factors to promote the POM reaction. The results of the Catalyst Surface Plasma-Assisted Oxidation Reaction (CSPOR) demonstrate that H2O not only promotes the selective oxidation of chemisorbed hydrocarbon species to oxygenate species, but also facilitates the desorption of oxygenates from the catalysts.

Solution of discrete time–cost trade-off problem with adaptive search domain

PurposeExact solution of time–cost trade-off problem (TCTP) by the state-of-the-art meta-heuristic algorithms can be obtained for small- and medium-scale problems, while satisfactory results cannot be obtained for large construction projects. In this study, a hybrid heuristic meta-heuristic algorithm that adapts the search domain is developed to solve the large-scale discrete TCTP more efficiently.Design/methodology/approachMinimum cost slope–based heuristic network analysis algorithm (NAA), which eliminates the unfeasible search domain, is embedded into differential evolution meta-heuristic algorithm. Heuristic NAA narrows the search domain at the initial phase of the optimization. Moreover, activities with float durations higher than the predetermined threshold value are eliminated and then the meta-heuristic algorithm starts and searches the global optimum through the narrowed search space. However, narrowing the search space may increase the probability of obtaining a local optimum. Therefore, adaptive search domain approach is employed to make reintroduction of the eliminated activities to the design variable set possible, which reduces the possibility of converging into local minima.FindingsThe developed algorithm is compared with plain meta-heuristic algorithm with two separate analyses. In the first analysis, both algorithms have the same computational demand, and in the latter analysis, the meta-heuristic algorithm has fivefold computational demand. The tests on case study problems reveal that the developed algorithm presents lower total project costs according to the dependent t-test for paired samples with α = 0.0005.Research limitations/implicationsIn this study, TCTP is solved without considering quality or restrictions on the resources.Originality/valueThe proposed method enables to adapt the number of parameters, that is, the search domain and provides the opportunity of obtaining significant improvements on the meta-heuristic algorithms for other engineering optimization problems, which is the theoretical contribution of this study. The proposed approach reduces the total construction cost of the large-scale projects, which can be the practical benefit of this study.

A superconducting quantum processor architecture design method for improving performance and reducing frequency collisions

More physical qubits and qubit connections integrated on a superconducting quantum processor can improve the ability to execute quantum programs, but on the other hand, they might increase the probability of frequency collisions. Considering a performance and frequency collisions trade-off, it is a feasible method to optimize processor architecture aiming at running specific quantum programs. To this end, we propose a method for designing superconducting quantum processor architectures for the purpose of running specific quantum programs. Different from existing methods, our method is mainly based on graph theory to optimize processor architecture design. First, we convert the trade-off problem into the optimization of the distance between two points as well as the maximum degree in the processor architecture graph. Second, we consider the actual physical routing constraints and build a mathematical model for the optimization problem. Finally, we propose an automatic processor architecture design flow based on the mathematical model, which is implemented with an improved genetic algorithm. To show the effectiveness of our method, we selected sixteen quantum programs with different qubit numbers and different functions for comparison. Simulation results show that the architecture schemes of our method outperform IBM’s general-purpose square lattice architecture schemes. Compared to the general-purpose architecture schemes, the architecture schemes of our method have an average performance improvement of 15.61% and a minimum reduction of 21.33% in the probability of frequency collisions. Furthermore, in most of the selected quantum programs, our architecture schemes perform better than the eff-5-freq’s architecture schemes, with an average 6.58% improvement in performance and a minimum 6.45% reduction in the probability of frequency collisions. Therefore, our method can provide superconducting quantum processor architecture design with better performance and lower probability of frequency collisions for quantum programs.

Feasibility study of optical imaging of the boron-dose distribution by a liquid scintillator in a clinical boron neutron capture therapy field.

Evaluation of the boron dose is essential for boron neutron capture therapy (BNCT). Nevertheless, a direct evaluation method for the boron-dose distribution has not yet been established in the clinical BNCT field. To date, even in quality assurance (QA) measurements, the boron dose has been indirectly evaluated from the thermal neutron flux measured using the activation method with gold foil or wire and an assumed boron concentration in the QA procedure. Recently, we successfully conducted optical imaging of the boron-dose distribution using a cooled charge-coupled device (CCD) camera and a boron-added liquid scintillator at the E-3 port facility of the Kyoto University Research Reactor (KUR), which supplies an almost pure thermal neutron beam with very low gamma-ray contamination. However, in a clinical accelerator-based BNCT facility, there is a concern that the boron-dose distribution may not be accurately extracted because the unwanted luminescence intensity, which is irrelevant to the boron dose is expected to increase owing to the contamination of fast neutrons and gamma rays. The purpose of this research was to study the validity of a newly proposed method using a boron-added liquid scintillator and a cooled CCD camera to directly observe the boron-dose distribution in a clinical accelerator-based BNCT field. A liquid scintillator phantom with 10 B was prepared by filling a small quartz glass container with a commercial liquid scintillator and boron-containing material (trimethyl borate); its natural boron concentration was 1wt%. Luminescence images of the boron-neutron capture reaction were obtained in a water tank at several different depths using a CCD camera. The contribution of background luminescence, mainly due to gamma rays, was removed by subtracting the luminescence images obtained using another sole liquid scintillator phantom (natural boron concentration of 0wt%) at each corresponding depth, and a depth profile of the boron dose with several discrete points was obtained. The obtained depth profile was compared with that of calculated boron dose, and those of thermal neutron flux which were experimentally measured or calculated using a Monte Carlo code. The depth profile evaluated from the subtracted images indicated reasonable agreement with the calculated boron-dose profile and thermal neutron flux profiles, except for the shallow region. This discrepancy is thought to be due to the contribution of light reflected from the tank wall. The simulation results also demonstrated that the thermal neutron flux would be severely perturbed by the 10 B-containing phantom if a relatively larger container was used to evaluate a wide range of boron-dose distributions in a single shot. This indicates a trade-off between the luminescence intensity of the 10 B-added phantom and its perturbation effect on the thermal neutron flux. Although a partial discrepancy was observed, the validity of the newly proposed boron-dose evaluation method using liquid-scintillator phantoms with and without 10 B was experimentally confirmed in the neutron field of an accelerator-based clinical BNCT facility. However, this study has some limitations, including the trade-off problem stated above. Therefore, further studies are required to address these limitations.

Control-oriented imitation learning for atmospheric flight control of launch vehicles under wind disturbance

The atmospheric flight control of launch vehicle is required to track the nominal trajectory while obeying path constraints on the aerodynamic load peak. Due to the wind disturbance, wind-induced angle of attack may increase the aerodynamic load peak, and reducing of it will lead to larger trajectory tracking error. Because of the cumbersome tuning of gain schedule and the dependency on accurate wind prediction, it is difficult for existing methods to handle the trade-off problem between reduction of the aerodynamic load peak and trajectory tracking error under uncertain wind disturbance. The presented research is intended to remedy this difficulty through learning based methods. The ascent flight phase is modelled as a multiple objective Markov decision process, where the trade-off under wind disturbance is precisely modelled as the optimization of a multi-objective vector under the stochastic state transition. A neural network flight control policy is then trained by a novel control-oriented imitation learning (CoIL) scheme. The CoIL scheme incorporates two control-oriented design within the standard reinforcement learning scheme: the inverse reinforcement learning that infers a proper reward from demonstration trajectories to represent the multi-objective vector of atmospheric flight control, as well as the policy hierarchy that simplifies interaction with the high order vehicle dynamics. Compared with previous learning-based attempts in the literature, this scheme uses orders of magnitude fewer demonstration trajectories and avoids hand-programmed reward design. Evaluations are performed under an ascent flight mission, indicating that the obtained policy outperforms the prevalent baseline controllers with reduction of the normalized maximum aerodynamic load by 28.3%, altitude error by 38.6%, velocity error by 17.1% and path angle error by 42.7% under unpredicted wind disturbance.

Rate, Power, and Energy Efficiency Trade-Offs in Massive MIMO Systems With Carrier Aggregation

This work considers a multi-cell, multi-carrier massive MIMO network with carrier aggregation capabilities, and tackles both the rate versus power consumption and the rate versus energy efficiency (EE) trade-offs, by jointly optimizing the number of employed carriers, transmit antennas, base station density, and transmit power. Provably convergent algorithms for both trade-off problems are developed, together with closed-form results for the individual optimization of the considered resources, taking three main carrier aggregation techniques into account, namely inter-carrier, intra-carrier contiguous, and intra-carrier non-contiguous. Numerical results show how the use of carrier aggregation represents an effective way of increasing the network rate and EE, while keeping the power consumption at bay. By using carrier aggregation, it is possible to reduce the number of deployed antennas, without sacrificing the rate performance and increasing the system EE.

A Review of Public Safety Power Shutoffs (PSPS) for Wildfire Mitigation: Policies, Practices, Models and Data Sources

Public Safety Power Shutoffs (PSPS), also known as proactive de-energizations, proactively de-energize a portion of power systems to mitigate the risk of catastrophic wildfires caused by electric infrastructure. Since the first practice in Southern California in 2012, PSPS have been widely discussed in government and industry, but seldom in the academic literature. This paper surveys the PSPS program in California, including its history, policies, and practices. In practice, PSPS present strong interactions between electric utilities and customers, yielding PSPS a trade-off problem to balance the risk of power-line-ignited wild-fires (i.e., wildfire risk) against the harms of power shutoff (i.e., PSPS risk). In this regard, this paper summarizes the industry-standard and research methods for PSPS studies, including models, data sources, and test systems. It is suggested to integrate engineering solutions with social-economic science, such as energy innovation, energy equity, and PSPS uncertainties, in future PSPS studies.

Spectral Variability in Fixed Windows using Fractional Fourier Transform: Application in Power Spectral Density Estimation

In statistical signal processing, power spectral density estimation is a frequency domain analysis in which power contents of a signal are measured with respect to frequency components of that signal. The power estimation of a signal can be carried out more precisely by using a window with a narrower 3-dB bandwidth and higher side-lobe attenuation. Theoretically, these two spectral parameters show trade-off in variable windows and remain constant in fixed windows. In this work, spectral behavior of fixed windows has been elaborated using Fractional Fourier Transform (FRFT) keeping their inherent time domain behavior intact. The FRFT is an extension of conventional Fourier transform with an additional variable parameter, known as rotation angle, which makes it more flexible and useful in various signal processing applications viz. power estimation and designing of tunable transition band FIR filters. In this article, variability in 3-dB bandwidth and sidelobe attenuation of fixed windows has been achieved by exploiting the available flexibility in FRFT and obtained variability has been applied in the estimation of signal power. Simulation results demonstrate that both of these two spectral parameters are improved and hence, trade-off problem between resolution and spectral leakage in the power spectral density estimation is overcome upto an extent.

A discrete time/resource trade-off problem with a critical chain method under uncertainty: a hybrid meta-heuristic algorithm

A discrete time/resource trade-off problem with a critical chain method under uncertainty: a hybrid meta-heuristic algorithm

Binary-Convolution Data-Reduction Network for Edge–Cloud IIoT Anomaly Detection

Industrial anomaly detection, which relies on the analysis of industrial internet of things (IIoT) sensor data, is a critical element for guaranteeing the quality and safety of industrial manufacturing. Current solutions normally apply edge–cloud IIoT architecture. The edge side collects sensor data in the field, while the cloud side receives sensor data and analyzes anomalies to accomplish it. The more complete the data sent to the cloud side, the higher the anomaly-detection accuracy that can be achieved. However, it will be extremely expensive to collect all sensor data and transmit them to the cloud side due to the massive amounts and distributed deployments of IIoT sensors requiring expensive network traffics and computational capacities. Thus, it becomes a trade-off problem: “How to reduce data transmission under the premise of ensuring the accuracy of anomaly detection?”. To this end, the paper proposes a binary-convolution data-reduction network for edge–cloud IIoT anomaly detection. It collects raw sensor data and extracts their features at the edge side, and receives data features to discover anomalies at the cloud side. To implement this, a time-scalar binary feature encoder is proposed and deployed on the edge side, encoding raw data into time-series binary vectors. Then, a binary-convolution data-reduction network is presented at the edge side to extract data features that significantly reduce the data size without losing critical information. At last, a real-time anomaly detector based on hierarchical temporal memory (HTM) is established on the cloud side to identify anomalies. The proposed model is validated on the NAB dataset, and achieves 70.0, 64.6 and 74.0 on the three evaluation metrics of SP, RLFP and RLFN, while obtaining a reduction rate of 96.19%. Extensive experimental results demonstrate that the proposed method achieves new state-of-the-art results in anomaly detection with data reduction. The proposed method is also deployed on a real-world industrial project as a case study to prove the feasibility and effectiveness of the proposed method.

Multi-objective optimal control of multi-zone VAV systems for adaptive switching between normal and pandemic modes

The recent COVID-19 pandemic has highlighted the need to adapt the control of building HVAC systems to limit disease transmission in indoor environments. However, such operational adjustments may significantly increase energy use, creating a complex trade-off problem between infection risk mitigation and energy efficiency. Moreover, practical constraints, such as maintaining comfortable temperature and humidity levels for occupants and limited system equipment capacity, can limit the flexibility of operational adjustments, further exacerbating the challenge of finding optimal operational solutions. This paper therefore proposes an optimal control strategy for multi-zone VAV systems that adapts to switching between normal and pandemic modes. The strategy considers dual optimization objectives: minimizing system energy use and disease transmission risk, while also considering all practical constraints. Optimal operational parameters can be determined to achieve the best trade-off between these objectives based on pandemic conditions. Additionally, a new metric called “effective fresh air factor” is proposed to quantify the impact of multi-zone VAV systems on disease transmission in building zones. The effectiveness of the proposed strategy is evaluated in a simulated building and air-conditioning environment. Results show that the VAV system has superior ability to reduce transmission risk through operational adjustments, achieving a decrease in disease transmission risk by 25%–75% compared to normal operation.

Multiresponse surface methodology for hyperparameter tuning to optimize multiple performance measures of statistical and machine learning algorithms

AbstractHyperparameter tuning is an important task in machine learning for controlling model complexity and improving prediction performance. Most methods in the literature can only be used to tune hyperparameters to optimize a single performance measure. In practice, participants may want to optimize multiple measures of the model; however, optimizing one measure may worsen another. Therefore, a hyperparameter tuning method is proposed using the multiresponse surface methodology to solve the tradeoff problem between multiple measures. A search algorithm that requires fewer tuning runs is developed to find the optimal hyperparameter settings systematically based on the preferences of participants for multiple measures of the model. An example using the random forest algorithm is provided to demonstrate the application of the proposed method to improve the prediction performance of the model on unbalanced data.

Solving Time-Cost Tradeoff Problem with Resource Constraint Using Fuzzy Mathematical Model

Scheduling considered being one of the most fundamental and essential bases of the project management. Several methods are used for project scheduling such as CPM, PERT and GERT. Since too many uncertainties are involved in methods for estimating the duration and cost of activities, these methods lack the capability of modeling practical projects. Although schedules can be developed for construction projects at early stage, there is always a possibility for unexpected material or technical shortages during construction stage. The objective of this research is to build a fuzzy mathematical model including time cost tradeoff and resource constraints analysis to be applied concurrently. The proposed model has been formulated using fuzzy theory combining CPM computations, time-cost trade off analysis and resource constraint. MATLAB software has been adopted to perform ranking process, for each case, that facilitates obtaining the optimum solution. This research infers that it is possible to perform time-cost trade off analysis with resource restriction simultaneously, which ensures achieving scheduling optimum solution reducing the effort and the time when performing these techniques in succession using traditional methods.

Mathematical Multiobjective Optimization Model for Trade-Offs in Small-Scale Construction Projects

As construction projects become more complex and challenging to execute, project managers need to develop work schedules that consider as many objectives as are critical to project success. A work schedule designed to optimize all these objectives is likely to enhance project performance. However, achieving a balance among these objectives is difficult because they frequently conflict with each other. Many optimization models have been developed over the years that can factor in different objectives. Although previous optimization studies have contributed to the body of knowledge by individually adding one or two objectives to the classical time–cost trade-off (TCT) problem, none of these studies simultaneously optimized more than three or four objectives at a time. Clearly, there is a need for a multiobjective optimization model to generate work schedules that increase the likelihood of achieving as many project objectives as necessary. This study was motivated by the need to fill this research gap. The objective of this study was to develop a mathematical multiobjective optimization model for the time–cost–quality–schedule flexibility-resource fluctuation trade-off problem in small-scale construction projects. The proposed model is novel because it (1) considers five important objectives of construction project scheduling at the same time, (2) quantifies all objectives to provide an accurate reflection of construction site conditions, and (3) objectively determines the optimal compromise solution using a distance-based method rather than subjectively from a set of Pareto-optimal solutions. The applicability of the proposed model is demonstrated with an illustrative example. The key results of this study are as follows: (1) the optimal solution obtained depends on the project manager’s priorities relative to the objectives; and (2) in optimization problems with more than two objectives, considering the objectives one at a time may result in misleading solutions, because the effects of the other objectives are ignored in this optimization. Consequently, multiple objectives must be considered simultaneously rather than one at a time. The proposed model is expected to provide project managers with a work schedule that balances five or more objectives, increasing the chances of successful project performance.

Ventilation strategy for simultaneous management of indoor particulate matter and airborne transmission risks – A case study for urban schools in South Korea

During the COVID-19 pandemic, schools in South Korea augmented ventilation rates by opening windows to avoid airborne transmission risks. However, this also elevated indoor PM2.5 concentrations, creating a trade-off problem between the two risks.This study quantitatively analyzed the impact of ventilation rate on indoor PM2.5 concentrations and COVID-19 transmission risk in a typical classroom. The results showed that indoor PM2.5 concentrations asymptotically approached outdoor ones with the augmentation of ventilation. The impact of ventilation augmentation on indoor concentration decreased with the ventilation rate. The time evolution of air transmission risk was obtained from the balance equation of quanta generated by infected people to estimate the infection probability and effective reproduction number. The transmission risk was a complex function of the ventilation rate, quanta generation rate per infected person, and mask filtration efficiency.The combination of information from the analyses of the two risks provides feasible solutions to the problem of resolving the two conflicting risks simultaneously. For example, high ventilation rates are allowable in mild climates with low outdoor PM concentrations. They can ease mask filtration efficiencies and elevate allowable activity levels while wearing high-efficiency masks and limiting students' activity levels must be enforced under cold climates with high outdoor PM concentrations.This study provides important insights into the trade-off between ventilation rate and COVID-19 transmission risk in classrooms. In addition, the results can be used to develop strategies to mitigate both risks and ensure the safety of students and staff.

Gradient-Based Trade-Off Design for Engineering Applications

The goal of the trade-off design method presented in this study is to achieve newly targeted performance requirements by modifying the current values of the design variables. The trade-off design problem is formulated in the framework of Sequential Quadratic Programming. The method is computationally efficient as it is gradient-based, which, however, requires the performance functions to be differentiable. A new equation to calculate the scale factor to control the size of the design variables is introduced in this study, which can ensure the new design achieves the targeted performance objective. Three formal approaches are developed in this study for trade-off design to handle various design scenarios, which include one that can handle cases with linearly dependent constraints and with more constraints than the number of design variables. Three engineering design problems are presented as examples to validate and demonstrate the use of these trade-off approaches to find the best way to adjust the design variables to meet the revised performance requirements.

Adaptive selection slime mould algorithm in time–cost–quality–environmental impact trade-off optimization

Artificial intelligence technology is now regarded as one of the most significant innovations, aiding humans in finding solutions to a wide range of problems. Because to inventions with such cutting-edge and exceptional features, this field is receiving a lot of attention from all around the world. In this study, the hybrid model adaptive selection slime mould algorithm (ASSMA) is applied to address the project’s multi-objective time, cost, quality, and environment trade-off problem. ASSMA is contrasted with previous algorithms such as multiple-objective swarm algorithm, the opposition-based multi-objective development algorithm, and the slime mould algorithm to emphasize the outcomes of the proposed model. Using performance parameters that evaluate model quality, it is anticipated that this study will greatly outperform and expand upon previous models.