Multiple Objective Optimization Problems Research Articles

Optimizing EDM for titanium alloys: an in-depth comparison of five MCDM techniques

This experimental study investigates the effect of the cutting parameters of the Electrical Discharge Machining (EDM) process of Ti-6Al-4V alloy material on surface roughness (Ra), cutting time (t) and Material Removal Rate (MRR) then solve the Multiple Objective Optimization Problem using separate Multi-Criteria Decision-Making (MCDM) methods namely Entropy-Weighted TOPSIS (E-TOPSIS), MOORA, SAW, VIKOR, and WPM. Focusing on nine 3-levels variants including Operating Voltage (OV), Pulse-On Time (Ton), Pulse-Off Time (Toff), Short-Circuit Off Time (AFF), Secondary Voltage (SV), Feed Rate (WF), Tension (WT), Water Pressure (WL) and Material Cutting Speed (F). Due to the large number of variants studied, the Taguchi L27 experimental design was chosen to reduce the number of experiments while still ensuring reliability in assessing the impact of technological parameters on responses in the study. The optimization results from the different methods indicated two distinct optimal outcomes. According to the E-TOPSIS, MOORA, and SAW, the optimal result is a Ra of 3.27 µm, a t of 7.37 min, and an MRR of 7.45 mm3/min. This result suggests a balanced and harmonious optimization among all criteria. On the other hand, the figures VIKOR and WPM methods are 2.87 µm, 9.57 min, and 5.74 mm3/min, respectively. These results indicate a higher priority for certain criteria, reflected in the lower Ra, longer cutting time, and a smaller MRR in comparison to the figures for the remaining MCDM methods. The different optimal results achieved by various methods highlight that each method is suited to and excels with different sets of values. Therefore, in each specific research or production process, comparing and choosing results calculated by different methods provides a comprehensive view, aiding in making appropriate decisions

CSCT: Charging Scheduling for Maximizing Coverage of Targets in WRSNs

In recent years, wireless rechargeable sensor networks (WRSNs), as a crucial technology in cyber-physical-social systems (CPSSs), have gradually become a hotspot of research, with the development of wireless energy transmission technology. In previous works, the objective is to maximize the survival rate of sensor nodes. However, in this article, we focus on maintaining more targets. First, it details the charging scheduling problem of maximizing coverage of targets (CoT) in on-demand charging architecture of WRSNs. Also, the problem is formalized as a multiple-objective optimization problem, which aims at maximizing the CoT and the energy efficiency simultaneously. After that, the charging scheduling for maximizing coverage of targets (CSCT) scheme is proposed to achieve the above objectives. Then, the problem is reformulated as a Deadline-TSP problem that is NP-hard. To address this problem, we design an energy predictive model and propose the CSCT with an n-path (n-CSCT) scheme that has an O(|N|ⁿ) computational complexity. In addition, the resurrection of sensor nodes is considered in this article. Thus, the n-CSCT with node resurrection (n-CSCT-R) scheme is proposed for this case. Finally, we validate the effectiveness of the proposed schemes via extensive simulations.

Attaining robust performance targets in data envelopment analysis with application to efficiency evaluation of airports

We consider a ratio-based efficiency analysis in view of achieving performance targets by Decision Making Units. The targets refer to the efficiency scores or ranks and the truth of pairwise preference relations. These outcomes can be instantiated for at least one or all feasible weights associated with inputs and outputs. We discuss Multiple Objective Optimization problems that minimize the required reductions of consumed resources or increases of produced results needed to reach the pre-defined aims. The proposed models offer great flexibility to the Decision Maker, who can indicate which factors should be modified and to which extent. The evolutionary optimization algorithm computes a set of solutions that exhibit the trade-offs between expected modifications of various factors. They can serve as the basis for interactively selecting the most preferred solution to be implemented in practice. The use of the proposed framework is illustrated in the problem of analyzing the efficiency of Polish airports.

Finite mixture models for multiscale dynamics in Chinese financial markets

A novel statistical machine learning technique for decomposing, analyzing, and modeling complex time series is presented in this paper. This methodology is based on unsupervised classification methods using finite mixtures, which have been widely used in factor analysis to extract essential information from multivariate static data in a parsimonious manner. Here, they are applied in a purely dynamic framework to create a new family of autoregressive models. The selection of the number of components and appropriate geometric template in these models is based on a set of information criteria that require solving a multiple objective optimization problem. To ensure the most adequate models are selected, the problem is augmented with a simulation instruction, providing a robust criterion for assignment. Experiments on real financial data sampled over the COVID-19 pandemic demonstrate the potential of this approach as a promising alternative to the well-known Box-Jenkins methodology. The proposed method offers flexibility, accuracy, and stability, making it suitable for a variety of applications in financial modeling and other fields where complex time series data analysis is required.

Efficient Multi-Objective Simulation Metamodeling for Researchers

Solving multiple objective optimization problems can be computationally intensive even when experiments can be performed with the help of a simulation model. There are many methodologies that can achieve good tradeoffs between solution quality and resource use. One possibility is using an intermediate “model of a model” (metamodel) built on experimental responses from the underlying simulation model and an optimization heuristic that leverages the metamodel to explore the input space more efficiently. However, determining the best metamodel and optimizer pairing for a specific problem is not directly obvious from the problem itself, and not all domains have experimental answers to this conundrum. This paper introduces a discrete multiple objective simulation metamodeling and optimization methodology that allows algorithmic testing and evaluation of four Metamodel-Optimizer (MO) pairs for different problems. For running our experiments, we have implemented a test environment in R and tested four different MO pairs on four different problem scenarios in the Operations Research domain. The results of our experiments suggest that patterns of relative performance between the four MO pairs tested differ in terms of computational time costs for the four problems studied. With additional integration of problems, metamodels and optimizers, the opportunity to identify ex ante the best MO pair to employ for a general problem can lead to a more profitable use of metamodel optimization.

Enhanced Solutions to Intuitionistic Fuzzy Multiobjective Linear Fractional Optimization Problems via Lexicographic Method

Optimization involving fuzzy numbers generally, and intuitionistic fuzzy numbers particularly is more and more an essential part of any computational intelligence method; and might be of interest in modeling fuzzy control systems, or carrying out a fuzzy sensitivity analysis. The recent literature includes many research papers related to both theoretical modelling, and practical implementation of fuzzy decision support systems. Fuzzy optimization is one of the main part in such decisionmaking tools. A realistic solution to a fuzzy optimization problem is always desired, and similarly, a Pareto optimal solution to a multiple objective optimization problem is always a need. In this paper we analyze the shortcomings; and eliminate the weaknesses of a solution approach from the literature proposed by Moges et al. in 2023. Firstly, Moges et al. used an accuracy function to de-fuzzify the original intuitionistic fuzzy optimization problem; then linearized the obtained crisp problem; and finally involved fuzzy goals to solve the derived multiple objective linear problem. We present two faulty key points of their approach, namely the de-fuzzification and linearization steps; prove the inappropriateness of their results; and propose some improvements. The final aim in addressing the first key point is to clarify the border between defuzzifications made in accordance to true theoretical statements, and those made for modeling reasons. The methodological improvement we propose is related to the second key point and it assures that Pareto optimal solutions - highly required in multiple objective optimization - are obtained. To illustrate our point of view, we use a numerical example from the literature, and report better numerical results derived by the improved methodology.

Using VIKOR and RSM-DA in the optimization of dry turning of 9XC steel

Dry turning is an effective method for reducing the production costs and environmental impact of machining processes. In this study, the effect of cutting speed (Vc), feed rate (fz), and depth of cut (ap) on the surface roughness (Ra) and material removal rate (MRR) of 9XC steel during dry turning was investigated. A Box-Behnken experimental design was employed to analyze the main effects and interaction effects of these cutting parameters. In this research, the combining Response Surface Methodology - Desirability Approach (RSM-DA) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method were both employed for solving multiple objective optimization problems, and their performance was compared. The results from both methods can be assessed based on their ability to identify the optimal set of parameters that simultaneously optimize surface quality and production rate, as well as their computational efficiency and ease of implementation. Both RSM-DA and VIKOR have been found effective for solving multi-objective optimization problems, such as optimizing cutting parameters in dry turning. While RSM-DA is a statistical tool that combines multiple objectives into a single function, VIKOR is a decision-making method that ranks alternatives based on multiple criteria. The choice of which method to use depends on the specific requirements of the problem and the availability of resources for implementation. The research results show that both VIKOR and RSM-DA are suitable for solving the multi-objective optimization problem of the turning process. According to the VIKOR method, the optimum cutting conditions were found to be a cutting speed Vc of 120 m/min, a depth of cut ap of 0.1 mm a feed rate fz of 0.06 mm/rev, and, which resulted in a surface roughness of 0.209 um and a material removal rate of 0.72 cm3/min. Meanwhile, RSM-DA predicts the optimal Ra and MRR values of 0.254 um and 3.36 cm3/min, respectively; corresponding to the Vc of 180m/min, fz of 0.06mm/rev and ap of 0.3mm; That means, the increasing the value of surface roughness by 21.5% will increase the MRR by 366.7%. The findings of this study can provide guidance for selecting the appropriate cutting parameters for dry turning of 9XC steel to achieve desired surface roughness Ra and material removal rate MRR in the specific case.

Manufacturing system reconfiguration towards sustainable production: a novel hybrid optimization methodology.

Developing a sustainable manufacturing system is a progressively challenging issue as governments across the world have been enforcing increasingly severe regulations by promoting the reduction of environmental waste for manufacturing and energy-saving production activities. Thus, there is a need for developing a sustainable manufacturing system that can be fully examined by incorporating ecological aspects (e.g., consumed energy) for related operations of a manufacturing system using computer-based discrete event simulation tools. In this study, a combined framework of a novel hybrid fuzzy multi-objective optimization and discrete event simulation approach is presented. We combine ecological and economic data and optimization techniques that are aimed at minimizing economic and ecological objectives in a manufacturing system at an early design phase. Hence, the fuzzy multi-objective optimization model is formulated by incorporating economic and ecological parameters. Again, the discrete event simulation model is established based on a comprehensive performance evaluation of the production system. This study also supports design decisions in determining optimum machine numbers, lighting, and cooling equipment required for the production processes within the sustainable manufacturing in conjunction with the most effective level of material flows. In addition, an integrated Decision-Making Trial and Evaluation Laboratory (DEMATEL)-epsilon constraint approach is applied to handle the multiple-objective optimization problem towards a set of trade-offs among the optimization objectives. A real-life application is carried out for investigating the applicability of the created hybrid framework. The findings of this study demonstrate that this framework is useful as a decision-making tool since it can develop a sustainable manufacturing system design considering an optimal solution associated with amounts of energy usage and CO2 emissions under economic constraints.

A two-level parallel decomposition-based artificial bee colony method for dynamic multi-objective optimization problems

Many real-world multiple-objective optimization problems have objectives that change over time. These multiple-objective optimization problems are called dynamic multiple-objective optimization problems (DMOPs) and have received an increased attention. To track the changing Pareto front in DMOPs, the Pareto front at a certain moment needs to be obtained as efficiently as possible, which is challenging for most of existing methods. To this end, we propose a two-level parallel decomposition-based artificial bee colony method for solving DMOPs. To sufficiently accelerate the process of obtaining the Pareto front, a two-level parallel structure is designed in our method. In the first-level parallel structure, the dynamic multi-objective optimization problem at a certain moment is decomposed into a set of single-objective optimization problems that could be solved in parallel. In the second-level parallel structure, a parallel artificial bee colony algorithm is applied to solve each decomposed single-objective optimization problem. Specially, the parallel bee colony algorithm in our method is improved to support the exchange of information among neighbor problems, which is widely accepted to be effective in improving the efficiency of obtaining optimal solutions. To support the implementation of our improved parallel artificial bee colony algorithm, a two-level shared memory structure is designed. Our proposed method is compared with 4 widely used methods on CEC’ 2018 multi-objective optimization benchmarks and two constrained dynamic multi-objective optimization problems. The experimental results show that our method outperforms other compared methods in efficiency while maintaining good scalability and convergence.

Minimizing Single-Family Homes’ Carbon Dioxide Emissions and Life Cycle Costs: An Improved Billiard-Based Optimization Algorithm Approach

In recent years, research has focused on designing buildings with higher energy efficiency and lower emissions by considering multiple objectives. This can impact financial savings, smaller environmental footprints, and energy consumption optimization. The purpose of the current study is to develop a new technique to solve this challenging multiple-objective optimization problem. While there are different methods to solve optimization problems, based on the NLP theory, there is not any metaheuristic algorithm that can solve all the problems accurately. Sometimes, the outcome of a basic algorithm is a local optimum. Therefore, to reach the global optimum, we propose the Improved Billiard-based Optimization Algorithm (IBOA). Moreover, in some cases, the basic model suffers from premature convergence, which prevents reaching an accurate result. Hence, this study aims to solve this problem and attain better convergence results using the proposed method to minimize CO2-eq emissions and life cycle costs. The design variables include some parameters of the envelope of a single-family residential dwelling to indicate the efficiency of the presented method. Based on the Pareto optimum solutions achieved, it is proved that the method is effective.

A comprehensive review on Internet of Things application placement in Fog computing environment

With the rise in Internet of Things (IoT) technology in recent years the amount of resource requirement of various IoT applications substantially increases. The data generated by various geo-distributed IoT devices is growing continuously. The existing IoT–Cloud paradigm limits the number of various emergent IoT applications as per the requirement. To utilize various IoT applications effectively and efficiently resource decentralization mechanism is a time of need. Fog computing is a novel computing approach for various IoT applications, specifically time-sensitive. Though, the resource-constrained nature of Fog devices in the fog computing model certainly fails to cater to multiple services for these IoT applications. The heterogeneity and dynamicity of the application request from the IoT devices need a quick decision regarding the placement of the application in the fog layer. Hence, optimal allocation of resources is quite essential for providing uninterrupted services to the end-users. This article comprehensively analyzes the different types of possible IoT application models and strategies for allocating resources to these applications. Since the resource allocation problem has already been proven to be a computationally NP-hard problem, finding a non-deterministic algorithm for allocating resources is our target. In this article, we have taken the IoT application placement problem in fog computing (APFC) as a single and multiple objective optimization problems. We have conducted a survey based on single and multiple objectives to address other possible issues in APFC. Finally, the challenges and promising directions for further research are presented.

Social Influence-Maximizing Group Recommendation

In this paper, we revisit the group recommendation problem, by taking into consideration the information diffusion in a social network, as one of the main criteria that must be maximised. While the well-known influence maximization problem has the objective to select k users (spread seeds) from a social network, so that a piece of information can spread to the largest possible number of people in the network, in our setting the seeds are known (given as a group), and we must decide which k items (pieces of information) should be recommended to them. Therefore, the recommended items should at the same time be the best match for that group's preferences, and have the potential to spread as much as possible in an underlying diffusion network, to which the group members (the seeds) belong. This problem is directly motivated by group recommendation scenarios where social networking is an inherent dimension that must be taken into account when assessing the potential impact of a certain recommendation. We present the model and formulate the problem of influence-aware group recommendation as a multiple objective optimization problem. We then describe a greedy approach for this problem and we design an optimisation approach, by adapting the top-k algorithms NRA and TA. We evaluate all these methods experimentally, in three different recommendation scenarios, for movie, micro-blog and book recommendations, based on real-world datasets from Flixster, Twitter, and Douban respectively. Unsurprisingly, with the introduction of information diffusion as an optimization criterion for group recommendation, the recommendation problem becomes more complex. However, we show that our algorithms enable spread efficiency without loss of recommendation precision, under reasonable latency.

Multiple Response Prediction and Optimization in Thin-Walled Milling of 6061 Aluminum Alloy

In this study, the multi-objective optimization method for thin-wall milling of 6061 aluminum alloy is addressed. The technological parameters including the cutting speed Vc, the feed of tooth fz, and the width of cut ar are considered input variables, while the manufacturing responses are surface roughness Ra, production rate MRR, and flatness deviation FL. The goal is to find the optimum cutting parameters to minimize Ra and FL and maximize MRR, at the same time. To solve this problem, the desirability function approach was used based on Taguchi orthogonal array. Twenty-seven experiments were conducted and the measured data were collected. The mathematical regression models for responses Ra, MRR, and FL were then generated and evaluated by using the analysis of variance method. Then, the multiple objective optimization problems were solved by using the desirability function approach. The optimum cutting parameters set are Vc=120m/min, fz=0.06mm, and ar=0.13131mm, corresponding to Ra=0.1613µm, MRR=17197.45cm3/min, and FL=0.0995mm.

Interactive co-evolutionary multiple objective optimization algorithms for finding consensus solutions for a group of Decision Makers

We introduce interactive algorithms for identifying consensus solutions to multiple objective optimization problems. They learn the Decision Makers’ (DMs’) preferences from indirect judgments and represent the recognized aspirations using scalar optimization goals. Their role is to set guidelines for the evolution and discovery of consensuses. The novelty of our proposals lies in co-evolving two populations: primary and supportive. The former’s role is to discover solutions relevant to the committee. The latter approximates the entire Pareto front, revealing a variety of trade-offs between objectives. The method improves the potential of conducting more informative interactions with the DMs and prevents the evolution from stagnation. Furthermore, it improves the evolutionary pace by dynamically removing no longer worthwhile goals from the supportive population in favor of increasing the primary population size. We confirm the importance of using co-evolution and dynamic resource allocation in extensive experiments. Also, we prove the competitiveness of our proposals by comparing them with the state-of-the-art methods on the WFG benchmarks. Finally, we demonstrate their practical usefulness when applied to the real-world problem of designing an environmentally friendly supply chain.

Reliability-based robust geotechnical design of braced excavations considering multiple failure modes

Reliability design of braced excavation is still a challenge for geotechnical community. Optimization design is a normal method to control the safety and cost of braced excavations. This study presents an advanced reliability-based robust geotechnical design method, which can consider multiple failures and uncertainty of statistical information. A universal design sample was conducted to verify the necessity of considering the uncertainty of statistical information. Ultimate limit state and serviceability limit state of braced excavations were defined, and point estimating method was used to evaluate the standard deviation of failure probabilities. Two-objective and three-objective optimization models were developed to illustrate the application of proposed methods in detail. In addition, the performance of optimization algorithms and further application of multiple-objective models were discussed. The results from this study indicate that the proposed method has a good performance in determining the optimal design with reasonable robustness and cost. New algorithms have higher efficiency in solving nonlinear and multiple-objective optimization problems than the 2nd Non-dominated sorting genetic algorithm. This study can guide the design of retaining systems of braced excavations in clay.

Energy Management Strategy and Optimal Sizing for Hybrid Energy Storage Systems Using an Evolutionary Algorithm

Energy management strategy (EMS) of hybrid energy storage systems has an essential mission of ensuring safety, enhancing reliability and improving system efficiency. This paper focuses on optimizing sizing of HESS and parameters of EMS simultaneously. Firstly, an improved model is employed in adaptive predictive model control (AMPC). Secondly, in order to minimize the cost of supercapacitors and the capacity degradation of batteries at the same time, the multiple objective optimization problems are solved by Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D). The nine variables are decided including seven parameters of AMPC and series-parallel number of supercapacitors. Subsequently, the support vector machine combined with the gray wolf optimization algorithm (GWO-SVM) is employed to enhance the applicability of EMS. Finally, in order to prove the superiority of the proposed EMS, a combined cycle and a real cycle are performed for validation. The results illustrate that the proposed method can improve the efficiency of the system and enhance the life span of the battery. Comparing with AMPC-only, the total energy loss can be reduced by at least 17.9%. A reduction of not less than 8.8% in RMS of battery current is obtained. The Ah-throughput can be reduced by at least 2.5%. In further discussions, the superiority of MOEA/D and GWO-SVM is proved by comparing with other methods.

Multiple-objective optimization of a reconfigurable assembly system via equipment selection and sequence planning

With the development of society and technology, the modern manufacturing system is required to handle unpredictable and rapidly changing market demands during various demand periods. This is a new challenge to the production scheduling of the production line, which requires a faster and smarter response to changes in production demand. To meet the challenge, the reconfigurable manufacturing system (RMS) has emerged as a competitive and promising manufacturing model. The reconfigurable assembly system (RAS), a critical feature of RMS, can be seamlessly incorporated into multi-variety, variable-batch, multi-functional, and fast-delivery manufacturing modes, opening up the possibility of rapid changes for production scheduling. In this paper, we first build a RAS model that can more accurately represent scheduling problems that may occur in real-world industrial operations. More specifically, we consider the assembly process of products could be divided into a number of major steps which are performed in different stations and optimize the assembly sequence of products and the equipment selection within individual assembly stations. After that, three objective functions corresponding to the workload balance, reconfiguration/assembly cost, and lead time are proposed to jointly optimize the assembly sequence and the equipment selection in individual assembly stations. Finally, a hybrid particle swarm optimization (MOHPSO) method is utilized to solve the multiple-objective optimization problem, which combines the advantages of particle swarm optimization and the hybrid method. Experimental results show the superiority of the proposed MOHPSO method over other alternatives including MOPSO and NSGA-II, achieving lower Spacing Metric (SM) and Mean Ideal Distance (MID) indexes and is suitable for solving production scheduling problems.

Design of framework for multi-objective workflow planning for IaaS platform in cloud environment

Today’s, we have seen in business third party companies making huge charges to customer in cloud computing platform. So, we are providing solution for how much, we are used cloud service as per timing slots we pay to company as making assets based usages on worked and service level agreement. Despite the fact that there are many existing work process accounting calculations in common widespread or heterogeneous registration conditions, they have problems to be directly connected to the cloud situations as cloud stands out from common heterogeneous situations by its management-based asset monitoring strategy and payment -Pro- Use evaluation methods. In this paper, we have introduce such type challenges and making the model workflow of planning problem that improves both brand throughput and costs as a Multiple objective Optimization Problem (MOP) of the cloud scenarios using more than one assessment plan, event sorting or even Multi-clouds in a lonely roadmap.

A Combination Weighting Method for the Risk Evaluation of Water Saving Management Contract

At present, the shortage of water resources has severely restricted the sustainable development of economy in China, and water saving management contract (WSMC) is an important measure to effectively alleviate the problem of water shortage. However, the complexity of WSMC projects makes it difficult for water saving service enterprises to distinguish the risk of different projects. Therefore, based on combination weighting method, this paper tries to establish a risk evaluation method for WSMC projects. First, the risk evaluation index system of WSMC is constructed via the hierarchical holographic modeling method. Second, to evaluate the risk of different projects, a score function is defined to reflect the statistical dispersion of the comprehensive risk of each project, and then a multiple objective optimization problem is constructed. Third, after solving this optimization problem, a comprehensive risk score function is defined, and then a combination weighting method for the risk evaluation of WSMC project, which effectively incorporate expert knowledge and historical data, is established combing with the analytic hierarchy process and anti-entropy method. Finally, numerical examples are given to verify the feasibility of the proposed method. The proposed method not only enriches the research on the risk evaluation method of WSMC projects, but also provides reference for the project selection of water saving service enterprises.

Location Determination of Electric Vehicles Parking Lot With Distribution System by Mexican AXOLOTL Optimization and Wild Horse Optimizer

In this manuscript proposes a hybrid approach for locating and sizing Electric vehicle charging stations (EVCSs) optimally and managing the vehicle charging process. The proposed hybrid approach is to work in conjunction with Mexican Axolotl optimization (MAO) and Wild Horse Optimizer (WHO) hence it is named as MAOWHO approach. The major purposes of the proposed approach are to site and size of the electric vehicle parking lot (EVPL) and to improve the benefit of EVPL for the participation of the reserve market. In addition, power loss and voltage fluctuations occur due to the stochastic nature of renewable energy sources (RES) and electric vehicles (EV) demand load, which is reduced by the proposed approach. To optimally determine the size of the parking lot, the MAOWHO approach is adopted. The integration of the EV and PV systems, especially in parking lots, enhances the reliability and flexibility of the electrical system at critical moments. Multiple objective optimization problems are calculated to achieve objective variables to reduce power losses, voltage fluctuations, charging and supply costs, and EV costs. The location and capacity of the RES and EV charging stations in this optimization problem are objective variables. The MAOWHO approach enhances Solar Powered Electric Vehicle Parking Lot (EVSPL) participation in various energy and ancillary service markets that includes the effects of capacity payments. Besides, the implementation of MAOWHO approach is done by the MATLAB/Simulink platform and the performance of the MAOWHO approach is compared to the existing approaches. From the simulation outcome, it concludes that the proposed approach based performance provides a profit of 880 € compared to other approaches like SMO, CGO, SBLA.