Multiple Criteria Optimization Research Articles

Optimizing dry milling of stir-cast and heat-treated AZ80 magnesium alloy using multiple criteria optimization technique: an experimental study.

The primary aspects of this research are to evaluate surface roughness, cutting force, and material removal rate and optimize it with dry milling process parameters for heat-treated and stir-cast AZ80 magnesium alloy. Multiple methodologies are utilized in the research, which includes the Integration of design of experiments-response surface methodology for experimental design with the technique for order of preference by similarity to ideal solution for multi-criteria optimization. In order to evaluate the effect of process parameters on the response, the experimental design manipulates the depth of cut, feed rate, and cutting speed in a systematic manner. An evaluation of the machined surface's quality is conducted via surface roughness measurements. Likewise, insights into the forces exerted during milling can be obtained through continuous monitoring of cutting forces. The calculation of material removal rate is predicated on weight reduction. The interaction between the depth of cut and feed rate has a significant impact on the critical-to-quality characteristics of the alloy, which has contribution percentage greater than 25%. This finding validates that despite the heat-treated alloy having a similar composition to the as-cast alloy (where the closeness coefficient is 0.9843), the optimal process parameters of the former are not applicable to the latter. Nevertheless, the technique used to prepare the specimen has no bearing on the material removal rate, which is a process parameter-specific effect.

Distributed enhanced multi-objective evolutionary algorithm based on decomposition for cluster analysis in wireless sensor network

Conventional clustering algorithms do not recognize patterns and structures with contradicting objectives in large, distributed datasets. Distributed clustering leverages rapid processing capabilities to allow multiple nodes to work together. This paper proposes a Distributed clustering based on Multiobjective Evolutionary Algorithm by Decomposition (D-MOEA/d) to solve various multiobjective optimization problems in wireless sensor networks (WSNs). In MOEA/d, a multiobjective optimization problem decomposes into several scalar optimization subproblems, each focusing on a distinct objective. Each subproblem is expressed as a clustering problem that uses local data to perform distributed clustering. The proposed method has been extended to achieve improved accuracy in less time by using a smaller feature subset with less redundancy. The Distributed Enhanced MOEA/d (DE-MOEA/d) avoids local optima by achieving diversity in the population using fuzzy-based nearest neighbor selection, sparse population initialization, and evolved mutation operator. This integration improves the accuracy of the clustering process at WSN nodes, ensuring the attainment of well-balanced solutions across multiple optimization criteria in the distributed environment. Average Euclidean and total symmetrical deviations are the two cost functions used to minimize while clustering on the MOEA/d framework. Six real-life WSN datasets are used to assess the performance of the proposed technique: (1) the Delhi air pollution dataset, (2) the Canada weather station dataset, (3) the Thames River water quality dataset, (4) the Narragansett Bay water quality dataset, (5) the Cook Agricultural land dataset and 6) Gordon Soil dataset. The simulation results of both proposed algorithms are compared with Multiobjective distributed particle swarm optimization (DMOPSO) and Distributed K-means (DK-Means). The proposed algorithm DE-MOEA/d performs better in terms of the Silhouette index (SI), Dunn index (DI), Davies–Bouldin index (DBI), and Kruskal–Wallis (KW) statistical test.

Optimizing Last-Mile Delivery: A Multi-Criteria Approach with Automated Smart Lockers, Capillary Distribution and Crowdshipping

Background: This publication presents a review, multiple criteria optimization models, and a practical example pertaining to the integration of automated smart locker systems, capillary distribution networks, crowdshipping, last-mile delivery and supply chain management. This publication addresses challenges in logistics and transportation, aiming to enhance efficiency, reduce costs and improve customer satisfaction. This study integrates automated smart locker systems, capillary distribution networks, crowdshipping, last-mile delivery and supply chain management. Methods: A review of the existing literature synthesizes key concepts, such as facility location problems, vehicle routing problems and the mathematical programming approach, to optimize supply chain operations. Conceptual optimization models are formulated to solve the complex decision-making process involved in last-mile delivery, considering multiple objectives, including cost minimization, delivery time optimization, service level minimization, capacity optimization, vehicle minimization and resource utilization. Results: The multiple criteria approaches combine the vehicle routing problem and facility location problem, demonstrating the practical applicability of the proposed methodology in a real-world case study within a logistics company. Conclusions: The execution of multi-criteria models optimizes automated smart locker deployment, capillary distribution design, crowdshipping and last-mile delivery strategies, showcasing its effectiveness in the logistics sector.

A multiscale and multicriteria generative adversarial network to synthesize 1-dimensional turbulent fields

This article introduces a new neural network stochastic model to generate a 1-dimensional stochastic field with turbulent velocity statistics. Both the model architecture and training procedure ground on the Kolmogorov and Obukhov statistical theories of fully developed turbulence, so guaranteeing descriptions of (1) energy distribution, (2) energy cascade and (3) intermittency across scales in agreement with experimental observations. The model is a generative adversarial network (GAN) with multiple multiscale optimization criteria. First, we use three physics-based criteria: the variance, skewness and flatness of the increments of the generated field, that retrieve respectively the turbulent energy distribution, energy cascade and intermittency across scales. Second, the GAN criterion, based on reproducing statistical distributions, is used on segments of different length of the generated field. Furthermore, to mimic multiscale decompositions frequently used in turbulence’s studies, the model architecture is fully convolutional with kernel sizes varying along the multiple layers of the model. To train our model, we use turbulent velocity signals from grid turbulence at Modane wind tunnel.

Energy efficiency measures in existing buildings by a multiple-objective optimization with a solar panel system using Marine Predators Optimization Algorithm

To enhance the energy effectiveness of old buildings, retrofitting strategies are an efficient technique. The objective here is to apply a multiple-criteria optimization method to retrofit the building envelope. In this respect, a new metaheuristic algorithm, called Marine Predators Optimization Algorithm (MPOA) is used in this paper. The components that are investigated to be retrofitted are including roofs, windows, and outer walls. A photovoltaic system is considered to be employed on the rooftop. The degradation duration of the solar panels is studied. Also, for renewable energy development and its long-lasting impact on the retrofitting scheme, a maintenance design is considered. The optimum utilization of the financial investment for the maximization of energy savings and economic-related advantages are the main targets. Specifically, the major operation indicators of the retrofitting scheme are energy savings, payback period, and net present worth. A weighted sum technique is applied to solve the non-linear problem. The efficiency of the suggested model is a result of the designed retrofitting plan.

A compromise decision-support technique with an augmented scoring function within circular intuitionistic fuzzy settings

The VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method provides a valuable means of evaluating options based on assessment criteria, intending to generate the finest compromise options through multiple-criteria optimization. However, decision-making often involves uncertainty, where decision-makers may lack complete information or struggle to predict outcomes accurately. Circular intuitionistic fuzzy (C-IF) sets offer a versatile way to represent uncertainty and indecision, adding circularity to intuitionistic fuzzy sets' membership and non-membership. C-IF sets bring sophistication by incorporating circular functions to address complicated ambiguity, alongside assigning membership and non-membership components. This research aims to create a C-IF VIKOR decision-support method to handle multiple-criteria compromise solutions with C-IF uncertainties. The study focuses on enhancing the augmented scoring function and Chebyshev distance metric in C-IF surroundings. The augmented scoring function exhibits unique characteristics, including a direct relationship between membership and function value, an inverse correlation with nonmembership, and reflection of information reliability. The enhanced C-IF Chebyshev distance measure combines radial and membership/nonmembership distances, considering their special features. The proposed C-IF VIKOR technique utilizes these concepts to identify superior and inferior options and determine the finest compromise solution using an identification mechanism and VIKOR indices. The method is demonstrated in the context of healthcare waste disposal and will undergo sensitivity analyses and comparative studies to showcase its advantages, adaptability, and robustness.

On the Advantages of Searching Infeasible Regions in Constrained Evolutionary-Based Multi-Objective Engineering Optimization

Abstract Solving a multiple-criteria optimization problem with severe constraints remains a significant issue in multi-objective evolutionary algorithms. The problem primarily stems from the need for a suitable constraint handling technique. One potential approach is balancing the search in feasible and infeasible regions to find the Pareto front efficiently. The justification for such a strategy is that the infeasible region also provides valuable information, especially in problems with a small percentage of feasibility areas. To that end, this paper investigates the potential of the infeasibility-driven principle based on multiple constraint ranking-based techniques to solve a multi-objective problem with a small feasibility ratio. By analyzing the results from intensive experiments on a set of test problems, including the realistic multi-objective car structure design and actuator design problem, it is shown that there is a significant improvement gained in terms of convergence by utilizing the generalized version of the multiple constraint ranking techniques.

VERIFICATION OF A TRAIN ON RAILWAY TRACK SIMULATION AND OPTIMIZATION SYSTEM

This article presents a Train on Railway Track simulation model and program developed by the authors. The model implements the module for multiple-criteria optimization with a set of proposed objective functions allowing reductions in train passing time, total costs, energy consumption, and adverse environmental impacts. The Train on Railway Track simulator has been developed to allow both the simulation itself and the ride optimization. The main achievement is the development of an algorithm that simulates the passage of a train over 500 km, the duration of which did not exceed two minutes. We present an analysis of the impact of model changes on the duration of the simulation and the accuracy of the results obtained. This allows the use of these achievements in simulations carried out for the railway, automotive, or aviation industries as well. Changes in the classical approach to optimization proposed by the authors made it possible to obtain results directly by solving classical systems of equations. The change in the approach to the optimization and system algorithm has reduced the operating time of the optimization system from thousands of simulations to a single simulation with an additional optimization process that takes several minutes to calculate. This article is a continuation of the description of the work performed, and basic information about the developed simulation model and software functionality is included in a separate publication [13].

A currency trading system based on simplified models using fuzzy multi-criteria hierarchical optimization

This paper is based on some assumptions validated using real data and the most used Forex trading platform Meta Trader 4. First, we assume that any reasonable and relatively simple models, reflecting some trading hypotheses, can be profitable for a certain period. Having a sufficient set of such models optimized for selected currency pairs, we can use the model that provides the greatest profit in the current trading period. The second proposal is to use a fuzzy multiple-criteria approach at the training optimization stage using historical data in order to overcome or reduce the negative effect of overfitting. Here for the first time, the problem of fuzzy multiple-criteria optimization of trading was formulated and solved based on the output parameters of the developed simple single-criteria crisp models. This provides significantly greater profit than that obtained using single-criteria crisp models. The third proposal is to use the hierarchical structure of fuzzy local criteria to solve the multiple-criteria problem. It is shown that this additionally provides a significant increase in profit. The profitability and riskless of the developed trading models are studied based on real quotations of currency pairs USDJPY, EURUSD and AUDUSD using H4 timeframe.

An advanced approach to multiple criteria optimization and compromise solutions under circular intuitionistic fuzzy uncertainty

The intention of this work is to create an advanced VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approach that deals with multiple criteria optimization and compromise solutions in the face of circular intuitionistic fuzzy (C-IF) uncertainty. There has been limited research on enhancing VIKOR techniques to manipulate multiple criteria decision analysis (MCDA) with sophisticated C-IF information. Specifically, the relative score function in current C-IF VIKOR methods loses its meaning when the radius of the C-IF number is zero, restricting its applicability to regular intuitionistic fuzzy contexts. Furthermore, converting C-IF information into intuitionistic fuzzy contents prematurely using existing C-IF VIKOR algorithms results in inadequate preservation of the C-IF connotation, hindering the primary objective of using C-IF theory to manage MCDA issues. This study addresses the limitations of the current relative score system in C-IF contexts and recommends a more legitimate and fair scoring function. The upgraded C-IF scoring function includes scaling and exponential parameters, making it more pragmatic and adaptable than the present relative scoring mechanism. By connecting the suggested C-IF scoring function with the recently published C-IF Minkowski distance measurements, the study establishes a parameterized C-IF VIKOR methodology to analyze MCDA issues arising from C-IF uncertainty. The suggested parameterized C-IF VIKOR model provides accessible assessments of individual regret, collective utility, and trade-off indices by utilizing three- and four-term C-IF metric ratios. The model constructs a synthesized mechanism to investigate the requirements for acceptable advantage and decision-making stability for locating agreeable compromise solutions. The viability and performance of the suggested technique are manifested through applications in waste disposal location selection, multi-expert supplier evaluation, and medical waste disposal techniques. Comparative analysis is executed to highlight implications for theory, practice, and future research. This study provides significant contributions, including (i) an innovative approach to the C-IF VIKOR methodology for broadening the coverage of higher-order fuzzy research models, (ii) a new C-IF scoring function for decision aiding to overcome the limitation of the current relative score function, and (iii) a novel conceptual framework of a parameterized VIKOR for empowering decision-makers with more leeway in adapting the VIKOR solution to intuitively judged outcomes.

A Suboptimal Optimizing Strategy for Velocity Vector Estimation in Single-Observer Passive Localization.

In a single-observer passive localization system, the velocity and position of the target are estimated simultaneously. However, this can lead to correlated errors and distortion of the estimated value, making independent estimation of the speed and position necessary. In this study, we introduce a novel optimization strategy, suboptimal estimation, for independently estimating the velocity vector in single-observer passive localization. The suboptimal estimation strategy converts the estimation of the velocity vector into a search for the global optimal solution by dynamically weighting multiple optimization criteria from the starting point in the solution space. Simulation verification is conducted using uniform motion and constant acceleration models. The results demonstrate that the proposed method converges faster with higher accuracy and strong robustness.

Planning and Multi-Objective Optimization of Production Systems by means of Assembly Line Balancing

The ever-shorter time to market demanded by customers and an increasing number of variants lead to high-frequency product development and production system planning cycles. This article therefore presents an approach that aims at increasing efficiency in the repetitive planning of variant-specific assembly systems. Compared to the state-of-the-art manual, document-based planning prevailing in industry, formal optimization methods have the potential of considering multiple optimization criteria. This article therefore proposes an approach for assigning individual process steps to stations for a given customer cycle time while minimizing the number of stations as well as optimizing additional criteria such as increasing the production system flexibility, achieving good ergonomics or reducing the deviation within provided tolerances. The solution space of the optimization is constrained by the mathematical modeling of a limited number of stations. The operations research (OR) model developed for this purpose considers given assembly precedence graphs as an input and includes variables, parameters, objective function and constraints. Especially the latter considers the process precedence relations in terms of the Assembly Line Balancing (ALB) problem as well as a divisibility of process steps. The approach is applied to the variant-specific planning of a highly automated welding assembly in the automotive supplier industry as use case. The results show its applicability and potential in reducing planning time.

The Perceived Stress Scale 2&2: a two-factorial German short version of the Perceived Stress Scale.

Stress is among the leading causes for diseases. The assessment of subjectively perceived stress is essential for resilience research. While the Perceived Stress Scale (PSS) is a widely used questionnaire, a German short version of the scale is not yet available. In the current study, we developed such a short version using a machine learning approach for item reduction to facilitate the simultaneous optimization of multiple psychometric criteria. We recruited 1,437 participants from an online panel, who completed the German long version of the PSS along with measures of mental health and resilience. An ant-colony-optimization algorithm was used to select items, taking reliability, and construct validity into account. Findings on validity were visualized by psychological network models. We replicated a bifactor structure for the long version of the PSS and derived a two-factor German short version of the PSS with four items, the PSS-2&2. Its factors helplessness and self-efficacy showed differential associations with mental health indicators and resilience-related factors, with helplessness being mainly linked to mental distress. The valid and economic short version of the PSS lends itself to be used in future resilience research. Our findings highlight the importance of the two-factor structure of the PSS short versions and challenge the validity of commonly used one-factor models. In cases where the general stress factor is of interest, researchers should use the longer versions of the PSS that allow for the interpretation of total scores, while the PSS-2&2 allows of an economic assessment of the PSS factors helplessness and self-efficacy.

Link-INVENT: generative linker design with reinforcement learning

Link-INVENT enables design of PROTACs, fragment linking, and scaffold hopping while satisfying multiple optimization criteria.

Criteria Comparative Learning for Real-Scene Image Super-Resolution

Real-scene image super-resolution aims to restore real-world low-resolution images into their high-quality versions. A typical RealSR framework usually includes the optimization of multiple criteria which are designed for different image properties, by making the implicit assumption that the ground-truth images can provide a good trade-off between different criteria. However, this assumption could be easily violated in practice due to the inherent contrastive relationship between different image properties. Contrastive learning (CL) provides a promising recipe to relieve this problem by learning discriminative features using the triplet contrastive losses. Though CL has achieved significant success in many computer vision tasks, it is non-trivial to introduce CL to RealSR due to the difficulty in defining valid positive image pairs in this case. Inspired by the observation that the contrastive relationship could also exist between the criteria, in this work, we propose a novel training paradigm for RealSR, named Criteria Comparative Learning (Cria-CL), by developing contrastive losses defined on criteria instead of image patches. In addition, a spatial projector is proposed to obtain a good view for Cria-CL in RealSR. Our experiments demonstrate that compared with the typical weighted regression strategy, our method achieves a significant improvement under similar parameter settings.

Application of integrated CRITIC and GRA-based Taguchi method for multiple quality characteristics optimization in laser-welded blanks

There is a need to study the multi-objective optimization technique as the appropriate method to enhance welding qualities under optimal process conditions. Therefore, this study investigated the application of the integrated Criteria Importance Through Intercriteria Correlation (CRITIC), which is an objective weighting technique, and the Grey Relational Analysis (GRA)-based Taguchi method to solve multiple criteria optimization problem in the Nd:YAG laser welding process. The Taguchi-based L36 orthogonal array table was employed in this study to optimize six process parameters, including the beam diameter, laser power, flow rate, welding speed, laser offset, and pulse shape, with the aim to simultaneously achieve maximum weld strength and minimum weld width. The base metal JIS G3141 SPCC steel with 0.5 and 1.0 mm thicknesses was used in the present experiment. Following the welding process and optimization, the weld strength was measured using a Cometech QC-506M1 universal testing machine, while the weld width was determined under a Nikon SMZ25 stereomicroscope. Based on the results, the weight fractions of the weld strength and weld width from the applied CRITIC method were equal to 0.4157 and 0.5843, respectively. Meanwhile, the GRA revealed that the process parameters recorded an optimal setting for beam diameter of 0.8 mm, flow rate of 8 L/min, laser power of 0.6 kW, welding speed of 2.5 mm/s, laser offset of 0.2 mm, and pulse shape I. Furthermore, the weld strength and the weld width were enhanced from 236 to 328 MPa and from 1.13 to 1.04 mm, respectively. Additionally, the Analysis of Variance (ANOVA) indicated that the laser power and welding speed were the most influential parameters on the welding qualities. Most importantly, the findings of the confirmation experiment showed that the proposed approach was able to effectively identify the optimal laser welding parameters, which ultimately improved the multiple quality characteristics.

Multiple Criteria Optimization (MCO): A gene selection deterministic tool in RStudio.

Identifying genes with the largest expression changes (gene selection) to characterize a given condition is a popular first step to drive exploration into molecular mechanisms and is, therefore, paramount for therapeutic development. Reproducibility in the sciences makes it necessary to emphasize objectivity and systematic repeatability in biological and informatics analyses, including gene selection. With these two characteristics in mind, in previous works our research team has proposed using multiple criteria optimization (MCO) in gene selection to analyze microarray datasets. The result of this effort is the MCO algorithm, which selects genes with the largest expression changes without user manipulation of neither informatics nor statistical parameters. Furthermore, the user is not required to choose either a preference structure among multiple measures or a predetermined quantity of genes to be deemed significant a priori. This implies that using the same datasets and performance measures (PMs), the method will converge to the same set of selected differentially expressed genes (repeatability) despite who carries out the analysis (objectivity). The present work describes the development of an open-source tool in RStudio to enable both: (1) individual analysis of single datasets with two or three PMs and (2) meta-analysis with up to five microarray datasets, using one PM from each dataset. The capabilities afforded by the code include license-free portability and the possibility to carry out analyses via modest computer hardware, such as personal laptops. The code provides affordable, repeatable, and objective detection of differentially expressed genes from microarrays. It can be used to analyze other experiments with similar experimental comparative layouts, such as microRNA arrays and protein arrays, among others. As a demonstration of the capabilities of the code, the analysis of four publicly-available microarray datasets related to Parkinson´s Disease (PD) is presented here, treating each dataset individually or as a four-way meta-analysis. These MCO-supported analyses made it possible to identify MMP9 and TUBB2A as potential PD genetic biomarkers based on their persistent appearance across each of the case studies. A literature search confirmed the importance of these genes in PD and indeed as PD biomarkers, which evidences the code´s potential.

RETRACTED: Optimization of zero-energy building by multi-criteria optimization method: A case study

Many subjects and objects are included into the zero-energy building investigation while the conventional optimal approach is suffering from its limitations. The boundary of energy balance and physic of zero-energy buildings in various regions highlighted with various architectural attributions require to be more investigated and assured. As reported by the statistics, it needs further integrating materials and installing costs in zero-energy buildings. In this paper, due to the increasing energy consumption in buildings, a new global multiple-criteria optimization model for zero-energy buildings, combining this model with the comprehensive assessment approach is conducted. The optimization design approaches for renewable energy structures included here are the multiple-criteria optimization by Enhanced Red Fox Optimization Algorithm on the initial design as concerns energy optimum models and costs, and the integration of Grey Multiple-Level Complete Assessment approach is employed to optimum model, which are the energy-conserving, solar energy application effectiveness, social and economic aspects. The optimum solution is proposed based on the chosen decision according to the global optimum model. This solution can help the engineer to design practical constructions. Finally, a multiple-objective optimal model for a case study placed in Beijing, China, is proposed to achieve an optimum design effect. Based on the study, efficient decisions can be made by designers with utilization of the proposed method. This design can easily be used in the considered real applications.

Selection of Regional Sustainable Development Project Using the Method of Multiple-criteria Optimization

The purpose of this article is to reveal the method of multiple-criteria optimization for the selection of regional sustainable development projects. Projects for regional sustainable development in the strategic planning are currently considered as an important instrument sustaining competitiveness and productive capabilities of the European economy and supporting developing areas, as an aim of the Europe 2020 strategy. Methodology. European countries implement national strategies by using the broad methodology of project management for reaching strategic aims. In such a context, it becomes necessary to provide a generalized method of projects' evaluation and their selection by the corresponding committees, proceeding from the best international practices and effective policy guidelines. Thus, the aim of this paper is to provide multiple-criteria method for the selection of the optimal regional development project out of the number of competing alternatives. Results. This paper reveals the decision-making process behind the expert selection of the most competitive project, based on a set of following criteria: social, economic, ecological and budgetary efficiency. Our method is as follows: we are considering all the available components of efficiency through the lens of integral factor. For each component we propose a set of local indicators that describe its degree of efficiency in comparable values. The projects of Dnipropetrovsk regional state administration of Ukraine are considered as main case of this study. To analyze them we use the methodology of multiple-criteria evaluation of regional sustainable development projects. As a result of this process it becomes possible to choose the optimal project for regional development out of wide array of projects being filed by the applicants. Value/originality. Value and originality of the multiple-criteria optimization method is the capability to provide the selection of regional sustainable development project on the base of clear criteria which precludes the possibility of corruption and unfounded decisions being taken by the authorities during the process of the planning the economic activities. As current method of decision-making is based on quantitative assessments of projects’ value, government and local government representatives are guided by a common vision and subjective impressions on the application. The advantages of the proposed method are methodological simplicity, flexibility and universality, which are crucial in the multiple-criteria decision-making in public administration. One of the advantages of this method is that it allows providing a justification for the results and conclusions of the selection process.

Multi-objective optimization for thermal comfort enhancement and greenhouse gas emission reduction in residential buildings applying retrofitting measures by an Enhanced Water Strider Optimization Algorithm: A case study

The energy operation of the building can be enhanced by retrofitting actions considering that it influences the life quality of the residents. Since optimization of different retrofitting scenarios needs much time, a new method is proposed in this study to simplify the optimization process. Therefore, an integration of the parametric sensitivity evaluation at the early phases of the optimization procedure is employed to decrease the number of the needed computation. Two objectives of the optimization process are including interior thermal comfort without increasing greenhouse gas emissions as the environment-related effects. Four steps are involved in the process: initial energy operation evaluation of the housing prototype; suggesting retrofitting actions; parametric sensitivity evaluation for retrofitting measures to define the most effective actions; and multiple-criteria optimization. Finally, outer wall insulation, roof insulation, airtightness, and floor insulation have resulted as more significant parameters with considerable effect on energy operation. The next category is including performance schedule, artificial lighting, temperature setpoints, and window substitution with low effect on decreasing energy usage. For controlling multiple-objective retrofitting projections when handling the side effects an efficient approach is presented by best scenarios for decision-makers, especially for architects.