Priority based [formula omitted] dominance: A new measure in multiobjective optimization

As the world is progressing towards faster and more efficient computing techniques, cloud computing has emerged as an efficient and cheaper solution to such increasing and demanding requirements. Cloud computing is a computing model which facilitates not only the end-users but also organizational and other enterprise users with high availability of resources on demand basis. This involves the use of scientific workflows that require large amount of data processing, which can be costly and time-consuming if not properly scheduled in cloud environment. Various scheduling strategies have been developed, which include swarm-based optimization approaches as well. Due to the presence of multiple and conflicting requirements of users, multi-objective optimization techniques have become popular for workflow scheduling. This paper deals with cat swarm-based multi-objective optimization approach to schedule workflows in a cloud computing environment. The objectives considered are minimization of cost, makespan and CPU idle time. Proposed technique gives improved performance, compared with multi-objective particle swarm optimization (MOPSO) technique.

In data center companies, cloud computing can host multiple types of heterogeneous virtual machines (VMs) and provide many features, including flexibility, security, support, and even better maintenance than traditional centers. However, some issues need to be considered, such as the optimization of energy usage, utilization of resources, reduction of time consumption, and optimization of virtual machine placement. Therefore, this paper proposes an alternative multiobjective optimization (MOP) approach that combines the salp swarm and sine-cosine algorithms (MOSSASCA) to determine a suitable solution for virtual machine placement (VMP). The objectives of the proposed MOSSASCA are to maximize mean time before a host shutdown (MTBHS), to reduce power consumption, and to minimize service level agreement violations (SLAVs). The proposed method improves the salp swarm and the sine-cosine algorithms using an MOP technique. The SCA works by using a local search approach to improve the performance of traditional SSA by avoiding trapping in a local optimal solution and by increasing convergence speed. To evaluate the quality of MOSSASCA, we perform a series of experiments using different numbers of VMs and physical machines. The results of MOSSASCA are compared with well-known methods, including the nondominated sorting genetic algorithm (NSGA-II), multiobjective particle swarm optimization (MOPSO), a multiobjective evolutionary algorithm with decomposition (MOEAD), and a multiobjective sine-cosine algorithm (MOSCA). The results reveal that MOSSASCA outperforms the compared methods in terms of solving MOP problems and achieving the three objectives. Compared with the other methods, MOSSASCA exhibits a better ability to reduce power consumption and SLAVs while increasing MTBHS. The main differences in terms of power consumption between the MOSCA, MOPSO, MOEAD, and NSGA-II and the MOSSASCA are 0.53, 1.31, 1.36, and 1.44, respectively. Additionally, the MOSSASCA has higher MTBHS value than MOSCA, MOPSO, MOEAD, and NSGA-II by 362.49, 274.70, 585.73 and 672.94, respectively, and the proposed method has lower SLAV values than MOPSO, MOEAD, and NSGA-II by 0.41, 0.28, and 1.27, respectively.

Wire Cut Electrical Discharge Machining (WEDM) is a non-conventional thermal machining process which is capable of accurately machine alloys having high hardness or part having complex shapes that are very difficult to be machined by the conventional machining processes. The WEDM finds applications in automobiles, aero–space, medical instruments, tool and die industries, etc. The input parameters considered for WEDM are pulse on time, pulse off time, flushing pressure, servo voltage, wire feed rate and wire tension. Performance of WEDM is mainly assessed by output variables such as, material removal rate (MRR), kerf width (Kw) and surface roughness (Ra) of the work piece being machined. Looking at the need of a suitable optimization model, the present work explores the feasibility of machine learning concepts to predict optimum surface roughness and kerf width simultaneously by making use of experimental data available in the literature for machining of Hastelloy C– 276 using WEDM. In most of the literatures, single objective optimization has been carried out for predicting optimum cutting parameters for WEDM. Hence, the present work presents a methodology that makes use of a machine learning algorithm namely, gradient descent method as an optimization technique to optimize both surface roughness and kerf width simultaneously (multi objective optimization) and compare the results with the existing literatures. It was observed that the input parameters such as pulse on time, pulse off time, and peak current have significant effect on both surface roughness and kerf width. The gradient descent method was successfully used for predicting the optimum values of response variables.

Multi-objective optimization technique has become an essential step in the selection of cutting parameters. The intension of this research study is to analyze the performance characteristics of coated carbide inserts on their measured output responses during machining AISI 1015 steel. This paper targets to optimize the machining parameters such as speed, cutting depth, feed rate, cutting fluid flow rate, and coating material when multiple responses like surface roughness and flank wear were considered at the same time during turning. This research study also intends to examine scientifically the effect of machining parameters on quality measures during machining structural AISI 1015 steel. Cathodic arc evaporation–coated titanium aluminum nitride (TiAlN), titanium aluminum nitride/tungsten carbide-carbon (TiAlN/WC-C), and uncoated CNC inserts were used for the study. SEM and energy-dispersive X-ray analysis were performed to confirm the presence of coated elements. Micro-hardness was measured for coated, pure inserts, and TiAlN/WC-C-coated tool exhibited a higher hardness of 22.11 GPa compared with pure and coated tools. Five process parameters were used for this study, each at three stages. The experimental design was laid based on Taguchi’s L27 orthogonal array. In this research study, a multi-objective hybrid optimization technique comprising grey relation and fuzzy logic conjugated with the Taguchi design of experiments was used. The process parameters were optimized by grey relation analysis followed by fuzzification using Mamdani fuzzy engine and then optimized through Taguchi analysis. The parameter combination of speed 500 rpm, depth of cut of 1 mm, a feed rate of 0.05 mm/rev, cutting fluid flow rate at high level, and TiAlN/WC-C coating was found to be the optimum input parameters. The confirmatory test was also performed to validate the hybrid optimization approach.

In recent years, multi-objective optimization techniques have gained popularity as a powerful approach to the solution of complex engineering problems that involve multiple competing objectives. This popularity can be attributed to the fact that multi-objective optimization techniques can solve complex engineering problems. Engineers are able to think about many goals at the same time using this method, which enables them to discover the best solution that fits all of their requirements. Yet, there are obstacles connected with setting objectives, dealing with high computational costs, comprehending complicated Pareto fronts, coping with uncertainty, and having limited accessibility to data from the real world. The purpose of this paper is to present a literature review of multi-objective optimization techniques for the solution of complex engineering problems. The review covers a variety of techniques that are available for multi-objective optimization as well as their applications in a variety of engineering fields. It also draws attention to the difficulties involved with their application and the necessity of giving serious consideration to these difficulties while attempting to solve actual engineering problems that occur in the real world. The research comes to the conclusion that multi-objective optimization is an effective method that can assist engineers in solving complicated engineering issues by concurrently taking into consideration numerous competing objectives.

This article has been retracted by the publisher.

This paper aims at developing an innovative technique of concurrent multi-objective optimisation for updating the multi-scale model of long-span bridges. A multi-scale model is established for the purpose of concurrently analysing the global response of the structure and nonlinear local damages in order to assess structural state and local damage evolution or deteriorating, respectively. A multi-objective optimisation technique is proposed in this work for concurrent multi-scale model updating, in which several key issues including the determination of the objective functions and constraint conditions, the multi-objective optimisation algorithm and how to find the optimal solution from many non-inferior solutions are studied. The proposed concurrent multi-objective optimisation technique is applied to update the initial multi-scale model of Runyang Suspension Bridge (RYSB) near Shanghai, and the updated model is validated by the data from the field tests conducted for obtaining the response in global (dynamic properties) and local levels.

Sustainable design is often practiced and assessed through the consideration of three essential areas: economic sustainability, environmental sustainability, and social sustainability. For even the simplest of products, the complexities of these three areas and their tradeoffs cause decision-making transparency to be lost in most practical situations. The existing field of multiobjective optimization offers a natural framework to define and explore a given design space. In this paper, a method for defining a product’s sustainability space (defined by economic, environmental, and social sustainability objectives) is outlined and used to explore the tradeoffs within the space, thus offering both the design team and the decision makers a means of better understanding the sustainability tradeoffs. This paper concludes that sustainable product development can indeed benefit from tradeoff characterization using multiobjective optimization techniques – even when using only basic models of sustainability. Interestingly, the unique characteristics of the three essential sustainable development areas lead to an alternative view of some traditional multiobjective optimization concepts, such as weak-Pareto optimality. The sustainable redesign of a machine to drill boreholes for water wells is presented as a practical example for method demonstration and discussion.

Basically, all stock investments aim to obtain maximum profit with low risk. The formation of a stock investment portfolio is always accompanied by measuring returns and risks that show its performance. Portfolio risk measurement is often faced with the challenge that returns are not normally distributed, so that measurements using the normality assumption cannot be applied. This study proposes the development of a modification of stock portfolio risk measurement so that it is not limited to the normality assumption. The development is carried out by modifying the calculation of Value at Risk (VaR) to consider the skewness and kurtosis values ​​(hereinafter referred to as modified VaR), so that the normal distribution assumption can be eliminated. As a method for compiling a stock portfolio, the Multi-Objective Optimization technique was chosen because it can modify risk averse so that the risk can be adjusted to the risk profile of each investor and is able to stabilize the mean return value. For its implementation, this paper uses real stock data which of course has returns that are not normally distributed, namely the four Indonesian stocks based on the largest capitalization recorded in January 2025 (blue chip), namely BREN, BBCA, BYAN, and BBRI obtained through finance.yahoo.com. The analysis method is divided into three steps, including multi-objective optimization completion, portfolio return calculation, and finally modified VaR estimation. The results of the study show that BBCA has the largest weight with a portion of more than 40% of the four stocks, so BBCA will be the priority stock for this portfolio. The portfolio formed using multi-objective optimization is proven to have a stable mean return because the portfolio mean return is between several of its constituent stocks (vice versa) which is around 0.01%, and the smallest estimated value of the portfolio modified VaR is 1.67%. Thus, a portfolio based on multi-objective optimization is not only able to create a portfolio that provides a small risk in risk measurement without assuming a normal distribution, but at the same time multi-objective optimization is also able to provide competitive returns with its constituent stocks.

A novel multi-objective optimization based multi-agent deep reinforcement learning approach for microgrid resources planning

In this paper, a multi-objective optimization of a time-delayed fractional order damping system is presented. In previous studies, a single objective optimization of this system was based on minimizing the integral of the squared error JISE, integral of the time-weighted squared error JITSE, integral absolute error JIAE, or integral time-weighted absolute error JITAE. However, from the practical point of view other design goals such as those related to system’s response and stability should be also included since an error-based optimized system can result in a system having the smallest error but at the expense of other crucial objectives. Moreover, the one-objective optimization returns only one design option hiding many important details about other possible design preferences. Here, the system under study is optimized by considering three objectives: peak time tp, the percentage overshoot Mp(%), and JISE. The results show that the multi-objective optimization technique provides more information about the system behavior and grants the decision maker different design options, called the Pareto set. In addition, this study investigates the meaning of the aforementioned integral errors within the multi-objective optimization scope.

This paper presents a method where a multi objective optimization technique is used together with response surface methods in order to support crashworthiness design. As in most engineering design problems there are several conflicting objectives that have to be considered when formulating a design problem as an optimization problem. Here this is exemplified by the desire to minimize the intrusion into the passenger compartment area and simultaneously obtain low maximum acceleration during vehicle impact. These two objectives are naturally conflicting, since low maximum acceleration implies large intrusion. The contribution of this paper is to show a successful application of a set of existing methods to solve a real world engineering problem. The paper also presents methods of illustrating the results obtained from the multi-objective optimization.

The present article deals with an application of multiobjective optimization (MOO) technique for fuzzy clustering. The problem of automatic clustering can be posed as one of the searching for a suitable set of cluster centers such that some measure of cluster validity is optimized. However, no single validity measure works equally well for different kinds of data sets. Moreover, it is extremely difficult to combine the different measures into one, since the modality for such combination may not be possible to ascertain, while sometimes the measures may be incompatible. Thus, it appears natural to keep a number of such measures separate, and to optimize them simultaneously by applying an MOO technique. Here, the mean value of a cluster validity index, computed over the partitionings obtained for different bootstrapped samples of the data, and a measure of its stability, that is defined newly in terms of the validity index, are used for optimization. The search capability of a recently proposed archived multiobjective simulated annealing algorithm called AMOSA is utilized for this purpose. The characteristic features of AMOSA are its concepts of amount of domination and archive in simulated annealing and situation-specific acceptance probabilities. The final solutions are stored in an archive, from which a single solution is chosen in a semisupervised manner that assumes the existence of some labeled points. Results are demonstrated for several artificial and real-life data sets. Comparison is made with another recent multiobjective evolutionary clustering scheme, in addition to a single objective approach and two classical methods.

Various evolving approaches have been extensively applied to evolve densely connected communities in complex networks. However these techniques have been primarily single objective optimization techniques, which optimize only a specific feature of the network missing on other important features. Multiobjective optimization techniques can overcome this drawback by simultaneously optimizing multiple features of a network. This paper proposes MGSO, a multiobjective variant of Group Search Optimization (GSO) algorithm to globally search and evolve densely connected communities. It uses inherent animal food searching behavior of GSO to simultaneously optimize two negatively correlated objective functions and overcomes the drawbacks of single objective based CD algorithms. The algorithm reduces random initializations which results in fast convergence. It was applied on 6 real world and 33 synthetic network datasets and results were compared with varied state of the art community detection algorithms. The results established show the efficacy of MGSO to find accurate community structures.

Although additive manufacturing (AM) offers great potential to revolutionize modern manufacturing, its layer-by-layer process results in a staircase-like rough surface profile of the printed part, which degrades dimensional accuracy and often leads to a significant reduction in mechanical performance. In this paper, we present a systematic approach to improve the surface profile of AM parts using a computational model and a multi-objective optimization technique. A photopolymerization model for a micro 3D printing process, projection micro-stereolithography (PμSL), is implemented by using a commercial finite element solver (COMSOL Multiphysics software). First, the effect of various process parameters on the surface roughness of the printed part is analyzed using Taguchi’s method. Second, a metaheuristic optimization algorithm, called multi-objective particle swarm optimization, is employed to suggest the optimal PμSL process parameters (photo-initiator and photo-absorber concentrations, layer thickness, and curing time) that minimize two objectives; printing time and surface roughness. The result shows that the proposed optimization framework increases 18% of surface quality of the angled strut even at the fastest printing speed, and also reduces 50% of printing time while keeping the surface quality equal for the vertical strut, compared to the samples produced with non-optimized parameters. The systematic approach developed in this study significantly increase the efficiency of optimizing the printing parameters compared to the heuristic approach. It also helps to achieve 3D printed parts with high surface quality in various printing angles while minimizing printing time.