Multi-objective Optimization Task Research Articles

The justification of functional properties of agricultural moving power units in the multi-objective scenario

BACKGROUND: Considering the developed technical facilities like moving power units (MPU) used in agricultural industry are described with numerous quality criteria, the design development issues should be solved in multi-objective and multi-factorial scenario. Therefore, the development of mathematical tools and software for solving these issues is a relevant aim [1-3].
 AIMS: Determination of optimal functional properties of an agricultural MPU with numerous quality criteria including the conflictive ones.
 METHODS: In this study, the algorithmic software for multi-objective optimization of functional properties of agricultural moving power units has been developed. The simulation models of MPUs dominating quality criteria, functional limits and initial conditions for solving the multi-objective optimization task of MPUs properties determination at the design and improvement stages are given. According to the expert assessment, the chosen dominating criteria are pressure on soil, productive capacity, energetic assessment based on relative reduction of total specific fuel-energy consumption, total maintenance costs and energy efficiency.
 RESULTS: The calculations of quality criteria values in testing points in the studied parameter space were performed. According to the developed algorithm, the range of acceptable results for the MPUs functional and design properties, which meet all criterial and functional limits given by the decision-making person (DMP), was derived. In addition, the Pareto set of solutions (points), which are the best options of the MPUs functional properties and do not disgrace each other on totality of criteria, was derived. According to the optimization analysis, the DMP chooses the only one point from the Pareto set.
 CONCLUSIONS: The developed mathematical models and the software based on the models are helpful to optimize the properties of agricultural moving power units with regard to various quality criteria including conflictive, functional, service criteria, etc. The software set allows performing the multi-objective optimization of an object with the total amount of considered quality criteria of up to 20 and with up to 50 variable parameters. . In order to use this software set to optimize other technical objects and tools, it is necessary to update the mathematical apparatus and, as a consequence, the software.

An Accurate UAV 3-D Path Planning Method for Disaster Emergency Response Based on an Improved Multiobjective Swarm Intelligence Algorithm.

Planning a practical three-dimensional (3-D) flight path for unmanned aerial vehicles (UAVs) is a key challenge for the follow-up management and decision making in disaster emergency response. The ideal flight path is expected to balance the total flight path length and the terrain threat, to shorten the flight time and reduce the possibility of collision. However, in the traditional methods, the tradeoff between these concerns is difficult to achieve, and practical constraints are lacking in the optimized objective functions, which leads to inaccurate modeling. In addition, the traditional methods based on gradient optimization lack an accurate optimization capability in the complex multimodal objective space, resulting in a nonoptimal path. Thus, in this article, an accurate UAV 3-D path planning approach in accordance with an enhanced multiobjective swarm intelligence algorithm is proposed (APPMS). In the APPMS method, the path planning mission is converted into a multiobjective optimization task with multiple constraints, and the objectives based on the total flight path length and degree of terrain threat are simultaneously optimized. In addition, to obtain the optimal UAV 3-D flight path, an accurate swarm intelligence search approach based on improved ant colony optimization is introduced, which can improve the global and local search capabilities by using the preferred search direction and random neighborhood search mechanism. The effectiveness of the proposed APPMS method was demonstrated in three groups of simulated experiments with different degrees of terrain threat, and a real-data experiment with 3-D terrain data from an actual emergency situation.

BIM-supported automatic energy performance analysis for green building design using explainable machine learning and multi-objective optimization

Supported by the combination of the advanced BIM technique with intelligent algorithms, this paper develops a systematic framework using explainable machine learning and multi-objective optimization to realize the automatic prediction and optimization of building energy performance towards the sustainable development goal. There are three critical parts incorporated, including the DesignBuilder simulation, BO-LGBM (Bayesian optimization-LightGBM) and an explainable method SHAP (SHapley Additive explanation)-based prediction and explanation of building energy performance, and AGE-MOEA algorithm-based multi-objective optimization (MOO) under sources of uncertainty. It has been verified in a case study for green building design. Results show that: (1) The predictive BO-LGBM model can make a highly precise prediction in agreement with the simulation data, reaching up the R2 larger than 93.4% and MAPE smaller than 2.13%. From the SHAP calculation, features related to the HAVC (Heating Ventilation and Air Conditioning) system tend to contribute more to affecting the prediction results. (2) The AGE-MOEA-based optimization can identify a set of Pareto optimal solutions in simultaneously minimizing the building energy consumption, CO2 emission, and indoor thermal discomfort degree, arriving at the highest optimization rate of 13.43% under proper adjustment of certain features. (3) In the MOO task, the consideration of model and data uncertainty by prediction intervals and Monte-Carlo simulation can further increase the optimization rate by around 4.0% than the deterministic scenario, resulting in more desired strategies in optimizing the green building performance. In short, this paper enriches the area of green building development. For one thing, it raises the transparency and interpretability of machine learning to make the prediction more convincing. For another, it efficiently determines the passive and active design solutions along with the detailed profile of influential factors for green building design.

Expert opinion-based multi objective optimization: an application in plasma coating technology

Multi-objective optimization is a very important activity which is applied in many different fields. When solving this problem, it is important to determine weights for criteria. If the weight of criteria is determined according to dry mathematical formulas, the opinion of researchers will be ruined. On the contrary, if the weight of criteria is determined according to the subjective opinion of researchers, it is also easy to make mistakes. This study applies a method of determining the weight of criteria based on experts' opinions and conditions must be also strictly satisfied, thereby both of the above limitations have been remedied. Such method is known as FUCOM (FUll COonsistency Method). An application example was carried out for multi-objective optimization in the plasma coating process. Plasma coating is a modern coating technology. This method is increasingly used in many different fields. However, determining the value of technological parameters to ensure the quality of high-quality products is a very complicated job. In order to ensure many requirements of the product, it is necessary to determine the optimal value of the technological parameters. Four criteria to evaluate a coating process include the adhesion strength of the coating, the shear strength of the coating, the tensile strength of the coating, and the porosity of the coating. The task of multi-objective optimization in this study is to determine the values of three input parameters (including: spray current intensity, powder feed flow, and spray distance) to ensure that the desired values of the four criteria are simultaneously achieved. After the weight of criteria is determined by the FUCOM method, the multi-objective optimization problem has been solved. Experiments to verify the optimal results were also conducted, thereby demonstrating the correctness of the methodology. The optimal values of the technology parameters (spray current intensity, powder feed flow, and spray distance) have been determined to be 568.69 A, 31.87 g/min, and 170.19 mm, respectively

Sustainable Manufacturing Decisions through the Optimization of Printing Parameters in 3D Printing

The 3D printers integrated with fused filament fabrication (FFF) are highly valued worldwide because of their properties, which include fast proofing, compatibility with various materials, and low printing cost. The competitiveness of FFF can be enhanced by improving printing quality. However, due to the increasing sustainability issues worldwide, there is an urgent need to lower energy consumption. In this study, we focused on fan rate, printing speed, nozzle temperature, build plate temperature, and layer thickness as factors that directly impact the dimensional accuracy, carbon dioxide emissions, and printing cost of FFF printers. Several single-objective and multiobjective optimization tasks were performed using the Taguchi method and desirability approach to implement sustainable manufacturing decisions. In single-objective optimization, the inner width, outer width, material cost, and labor cost were most easily affected by the layer thickness. The outer length, carbon dioxide emissions, and electricity cost were significantly affected by the build plate temperature. In multiobjective optimization, a different set of printing parameters can be used to optimize dimensional accuracy, carbon dioxide emissions, material cost, labor cost, and electricity cost. This study helps users to obtain optimal solutions under different optimization requirements to cope with diverse manufacturing characteristics.

Applying Evolutionary Multitasking for Process Parameter Optimization in Polymerization Process of Carbon Fiber Production

Carbon fiber is becoming a key material for engineering applications due to its excellent comprehensive properties. The process parameter optimization is an important step in the polymerization process of carbon fiber production. At present, most of the research on process parameter optimization is usually carried out on a single production line, without considering the correlation between optimization problems. In this paper, a multiobjective mechanism model for the co-optimization of the polymerization process of carbon fiber production is established. Each of these submodels is a multiobjective process parameter optimization task, corresponding to the polymerization process of a production line. In order to solve the model effectively, we also designed an evolutionary multitasking algorithm based on transfer learning, which reuses the past experiences of one task to generate a population pool for the next iteration of another task, enabling explicit genetic transfer between different tasks and accelerating the population convergence speed. The proposed multitasking framework for operation optimization has been conducted on 10 different production conditions of the polymerization process. Experimental results show that compared with other implicit and explicit genetic algorithms, this algorithm is very competitive in generating effective solutions. This research provides important support for process parameter optimization and manufacturing of carbon fiber production, which will help engineers and technicians to make informed decisions.

Two-step robust control design of quantum gates via differential evolution

Designing highly accurate and robust controls for quantum unitary operations is vital for practical quantum computation. In this paper, we demonstrate that the robustness of quantum gate controls can be enhanced by optimizing the sampling-based infidelity variance, which is formulated as a multi-objective optimization task to achieve high robustness while maintaining high gate fidelity. A two-step approach that first optimizes the average fidelity and then turns to the infidelity variance is proposed, where two modified differential evolution (DE) algorithms, i.e., mixed-guided-strategy DE (MGSDE) and multi-objective mixed-strategy DE (MOMSDE), are designed to search fields for the two steps of robust quantum gate control, respectively. Both MGSDE and MOMSDE adopt the mixed strategy, and in particular, MGSDE adopts a guided mutation scheme to accelerate the convergence, and MOMSDE uses an optimal buffer to explore the Pareto front. Numerical results demonstrate that the proposed approach enhances the control robustness and provides an efficient in-situ learning paradigm to tackle the disturbance problem for the control design of quantum gates.

Multi-objective optimization for multi-satellite scheduling task

The satellites scheduling mission play an effective role in enhancing the role of ground station control and monitoring systems. In this search, SGSEO is re-formulated into a multi-objective optimization task. Therefore, the Gravitational Search Algorithm GSA is exploited to attain several essential objectives for generating tight scheduling. Moreover, particle swarm optimization model PSO is consolidated with GSA in a novel form for strengthening its ability of local search and slow the speed of convergence. On the other side, to make the most of the satellite resources in the right direction, we have observed targets that have fewer observational opportunities to keep them from being lost. The PageRank algorithm is used to fulfil this issue by ranking the candidate's strips. Finally, the effect of different parameters of the proposed approach was studied by experimental outcomes and compared with previous methods. It has shown that the performance of the proposed approach is superior to its peers from other methods.

Multi-objective task assignment for maximizing social welfare in spatio-temporal crowdsourcing

With the development of the Internet of Things (IoT), spatio-temporal crowdsourcing (mobile crowdsourcing) has become an emerging paradigm for addressing location-based sensing tasks. However, the delay caused by network transmission has led to low data processing efficiency. Fortunately, edge computing can solve this problem, effectively reduce the delay of data transmission, and improve data processing capacity, so that the crowdsourcing platform can make better decisions faster. Therefore, this paper combines spatio-temporal crowdsourcing and edge computing to study the Multi-Objective Optimization Task Assignment (MOO-TA) problem in the edge computing environment. The proposed online incentive mechanism considers the task difficulty attribute to motivate crowd workers to perform sensing tasks in the unpopular area. In this paper, the Weighted and Multi-Objective Particle Swarm Combination (WAMOPSC) algorithm is proposed to maximize both platform's and crowd workers' utility, so as to maximize social welfare. The algorithm combines the traditional Linear Weighted Summation (LWS) algorithm and Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to find pareto optimal solutions of multi-objective optimization task assignment problem as much as possible for crowdsourcing platform to choose. Through comparison experiments on real data sets, the effectiveness and feasibility of the proposed method are evaluated.

Multi-objective optimization of the internal diamond burnishing process

ABSTRACT The paper presents a new single-operation two-transition technology for processing holes in sliding bearing bushings made of CuAl8Fe3 bronze. The technology was realized on a vertical machine center Haas MiniMill. During the first transition, the hole was formed by a new combined cutting tool and was then diamond burnished via a new device with an elastic beam providing the burning force. The deforming element was a spherical-ended polycrystalline diamond. The 2D and 3D surface texture parameters of the processed holes obtained after the first and second transitions were studied via experiments and regression analyses. Through one-objective optimization of the first transition, the optimal feed and cutting velocity values were found under a minimum roughness criterion. The optimal governing factors (diamond insert radius, burnishing force and feed rate) of the diamond burnishing process were determined through multi-objective optimization. The objective functions – micro-hardness and height and shape parameters of the surface texture – were selected in terms of processed hole requirements: improved lubrication conditions and increased micro-hardness. The formulated multi-objective optimization task was solved via non-dominated sorting genetic algorithm (NSGA-II). Experimental verifications confirmed the results. The processing series of holes showed that the new technology ensures good repeatability of the surface integrity characteristics.

Zoning Search With Adaptive Resource Allocating Method for Balanced and Imbalanced Multimodal Multi-Objective Optimization

Maintaining population diversity is an important task in the multimodal multi-objective optimization. Although the zoning search (ZS) can improve the diversity in the decision space, assigning the same computational costs to each search subspace may be wasteful when computational resources are limited, especially on imbalanced problems. To alleviate the above-mentioned issue, a zoning search with adaptive resource allocating (ZS-ARA) method is proposed in the current study. In the proposed ZS-ARA, the entire search space is divided into many subspaces to preserve the diversity in the decision space and to reduce the problem complexity. Moreover, the computational resources can be automatically allocated among all the subspaces. The ZS-ARA is compared with seven algorithms on two different types of multimodal multi-objective problems (MMOPs), namely, balanced and imbalanced MMOPs. The results indicate that, similarly to the ZS, the ZS-ARA achieves high performance with the balanced MMOPs. Also, it can greatly assist a “regular” algorithm in improving its performance on the imbalanced MMOPs, and is capable of allocating the limited computational resources dynamically.

Competition-Congestion-Aware Stable Worker-Task Matching in Mobile Crowd Sensing

Mobile Crowd Sensing is an emerging sensing paradigm that employs massive number of workers' mobile devices to realize data collection. Unlike most task allocation mechanisms that aim at optimizing the global system performance, stable matching considers workers are selfish and rational individuals, which has become a hotspot in MCS. However, existing stable matching mechanisms lack deep consideration regarding the effects of workers' competition phenomena and complex behaviors. To address the above issues, this paper investigates the competition-congestion-aware stable matching problem as a multi-objective optimization task allocation problem considering the competition of workers for tasks. First, a worker decision game based on congestion game theory is designed to assist workers in making decisions, which avoids fierce competition and improves worker satisfaction. On this basis, a stable matching algorithm based on extended deferred acceptance algorithm is designed to make workers and tasks mapping stable, and to construct a shortest task execution route for each worker. Simulation results show that the designed model and algorithm are effective in terms of worker satisfaction and platform benefit.

Reference point based evolutionary multi-objective optimization algorithms with convergence properties using KKTPM and ASF metrics

In a preference-based multi-objective optimization task, the goal is to find a subset of the Pareto-optimal set close to a supplied set of aspiration points. The reference point based non-dominated sorting genetic algorithm (R-NSGA-II) was proposed for such problem-solving tasks. R-NSGA-II aims to finding Pareto-optimal points close, in the sense of Euclidean distance in the objective space, to the supplied aspiration points, instead of finding the entire Pareto-optimal set. In this paper, R-NSGA-II method is modified using recently proposed Karush–Kuhn–Tucker proximity measure (KKTPM) and achievement scalarization function (ASF) metrics, instead of Euclidean distance metric. While a distance measure may not produce desired solutions, KKTPM-based distance measure allows a theoretically-convergent local or global Pareto solutions satisfying KKT optimality conditions and the ASF measure allows Pareto-compliant solutions to be found. A new technique for calculating KKTPM measure of a solution in the presence of an aspiration point is developed in this paper. The proposed modified R-NSGA-II methods are able to solve as many as 10-objective problems as effectively or better than the existing R-NSGA-II algorithm.

Višekriterijumski optimalni izbor hidrauličkog cilindra za pogonske mehanizme

The paper reports the optimization synthesis of a hydraulically actuated drive mechanism. A mathematical model of the mechanism using vector closure equations is developed. Based on the functional purpose of the mechanism, a set of geometric and force/moment requirements are defined which must be met by a proper selection of a standardized hydraulic cylinder and its points of attachment. A multiobjective design optimization task is defined with three objective functions whose minimum is searched - the mass of the hydraulic cylinder, the squared total deviation of the developed by the hydraulic cylinder moments from the predefined values of the external moments and the force in the hydraulic cylinder. The defined multiobjective optimization task is considered as a mixed variable nonlinear constrained optimization problem containing 5 continuous and 2 discrete variables and the multistage Monte Carlo method is used for its solution. Using different weighting schemes several Pareto-optimal compromise solutions are obtained.

A cost-effective optimization approach for improving the fatigue strength of diamond-burnished steel components

Diamond burnishing is a surface modification method aimed at improvements in the surface integrity (SI) and operating behavior of metal components. A cost-effective optimization approach for increasing the fatigue strength of diamond-burnished steel components has been developed. The basic idea is that the fatigue strength can be controlled by controlling some of the SI characteristics (surface micro-hardness, hardened-layer depth and roughness) whose measurements are not time-consuming and expensive. Thus, a multi-objective optimization task was set and solved using the weight vector method. The governing factors were the diamond radius and burnishing force. The resulting fatigue limit differed from the maximum fatigue limit by a mere 0.44%, which proves the effectiveness of the proposed approach. The results obtained for the fatigue limit are explained by means of an X-ray analysis of the introduced residual stresses and an analysis of the microstructures of the surface and subsurface layers. It has been established that a greater depth of the affected zone coupled with a smaller gradient in the alteration of the microstructure in depth provides larger fatigue strength.

Solution Representation Learning in Multi-Objective Transfer Evolutionary Optimization

This paper presents a first study on solution representation learning for inducing greater alignment and hence positive transfers between distinct multi-objective optimization tasks that bear discrepancies in their original search spaces. We first establish a novel probabilistic model-based multi-objective transfer evolutionary optimization (TrEO) framework with solution representation learning, capable of activating positive transfers while simultaneously curbing the threat of negative transfers. In particular, well-aligned solution representations are learned via spatial transformations to handle mismatches in search space dimensionalities between distinct multi-objective problems, as well as to increase the overlap between their optimized search distributions. We then showcase different algorithmic instantiations and case studies of the proposed framework in applications spanning continuous as well as discrete optimization; illustrative examples include multi-objective engineering design and route planning of unmanned aerial vehicles. The experimental results show that our framework helps induce positive transfers by unveiling useful but hidden inter-task relationships, thus bringing about faster search convergence to solutions of high quality in multi-objective TrEO.

Improved genetic algorithm for 3D printing multi-objective optimization task scheduling

For the 3D printing multi-objective optimization task scheduling problem, the problem model is established from the three aspects of related problem definition, constraint conditions, and objective function, which lays the foundation for subsequent research and solution.

Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning.

The fundamental goal of generative drug design is to propose optimized molecules that meet predefined activity, selectivity, and pharmacokinetic criteria. Despite recent progress, we argue that existing generative methods are limited in their ability to favorably shift the distributions of molecular properties during optimization. We instead propose a novel Reinforcement Learning framework for molecular design in which an agent learns to directly optimize through a space of synthetically accessible drug-like molecules. This becomes possible by defining transitions in our Markov decision process as chemical reactions and allows us to leverage synthetic routes as an inductive bias. We validate our method by demonstrating that it outperforms existing state-of-the-art approaches in the optimization of pharmacologically relevant objectives, while results on multi-objective optimization tasks suggest increased scalability to realistic pharmaceutical design problems.

Text Summarization as a Multi-objective Optimization Task: Applying Harmony Search to Extractive Multi-Document Summarization

Abstract Today, automated extractive text summarization is one of the most common techniques for organizing information. In extractive summarization, the most appropriate sentences are selected from the text and build a representative summary. Therefore, probing for the best sentences is a fundamental task. This paper has coped with extractive summarization as a multi-objective optimization problem and proposed a language-independent, semantic-aware approach that applies the harmony search algorithm to generate appropriate multi-document summaries. It learns the objective function from an extra set of reference summaries and then generates the best summaries according to the trained function. The system also performs some supplementary activities for better achievements. It expands the sentences by using an inventive approach that aims at tuning conceptual densities in the sentences towards important topics. Furthermore, we introduced an innovative clustering method for identifying important topics and reducing redundancies. A sentence placement policy based on the Hamiltonian shortest path was introduced for producing readable summaries. The experiments were conducted on DUC2002, DUC2006 and DUC2007 datasets. Experimental results showed that the proposed framework could assist the summarization process and yield better performance. Also, it was able to generally outperform other cited summarizer systems.

Selective laser sintering of carbon fiber–reinforced PA12: Gaussian process modeling and stochastic optimization of process variables

This work presents an in-depth investigation of the influence of the individual laser sintering parameters on density, mechanical, and dimensional properties of carbon fiber–reinforced PA12 parts manufactured by selective laser sintering (SLS) . A space-filling design of experiments method was used to plan the experiments and SLS trials were conducted to manufacture test samples that were characterized in terms of dimensional accuracy, density, and mechanical properties. Gaussian process–supervised learning was used to model the interaction between laser sintering parameters and quality properties. Stochastic optimization via evolutionary algorithm was employed to obtain trade-off solutions for several multi-objective optimization tasks. The Gaussian process presented excellent model quality for the majority of response variables evaluated. Laser sintering parameters had a significant influence on physical and mechanical properties, exhibiting complex and non-linear behavior. Multi-objective optimization showed a wide range of optimized laser sintering parameters available, depending on the trade-off objective desired.