Efficient Multi-objective Optimization Algorithm Research Articles

Heuristic algorithms for design of integrated monitoring of geologic carbon storage sites

Designs for Risk Evaluation and Management (DREAM) is a tool developed under the National Risk Assessment Partnership (NRAP) to enhance geologic carbon storage safety and efficiency. Using potential leakage scenarios generated externally by the users preferred history-matching approach, DREAM constructs ideal combinations of sensor locations in the right place at the right time to detect as many leaks as possible, detect them as early as possible, and minimize cost. This user-friendly tool, developed in Java, features a window-based GUI for input and a 3D visualization tool for viewing the domain space and optimized monitoring plans. DREAM's latest version accommodates real-world usage by allowing for joint optimization of wellbore point sensor placements and surface geophysics survey geometries, and by using more efficient multi-objective optimization algorithms. In an example shown here, these two improvements combined allow us to support containment assurance and go from detecting 80–90 % of the potential CO2 leakage to +99.7 %, a step-change improvement that can make the deciding difference in whether a site is suitable for geologic carbon storage. Though developed for geologic carbon storage, this tool would be equally applicable in many surface or offshore environmental monitoring projects.

Dynamic optimization of die-less spinning process through autonomous modeling of time-varying forming law

Die-less spinning is a typical incremental forming technology with flexible processing parameters, including the roller path and loading parameters, whose forming law changes dynamically and easy-to-produce defects of excessive thinning and flange wrinkling. This brings a great challenge to the process design of die-less spinning. In this study, we proposed a novel dynamic optimization approach and system considering the time-varying forming law for the processing parameters in die-less spinning. The idea of proposed approach is that simulating the spinning process step by step through autonomous finite element modeling, real-time extracting and modeling the simulated spinning status at each forming step to capture the time-varying forming law, then gradually optimizing the processing parameters through efficient multi-objective optimization algorithm. By conducting the proposed dynamic optimization approach to the first spinning pass of die-less spinning, series of polynomial response surface models (PRSM) of wall thickness and flange wrinkling at various forming steps were developed to describe the time-varying forming law. On this basis, the roller path and matching loading parameters in each step were gradually optimized through a differential evolution algorithm with the multi-objective of minimizing wall thickness reduction, reducing flange wrinkling and maximizing forming efficiency. The optimized process produced better spun part than the traditional spinning process, presenting the wall thickness reduction ≤5 % and the flange wrinkling ≤2.5 mm. Furthermore, the time-varying influence of processing parameters on the wall thickness and flange wrinkling were elucidated based on the PRSM of spinning status at various forming steps.

Directional multiobjective optimization of metal complexes at the billion-system scale.

The discovery of transition metal complexes (TMCs) with optimal properties requires large ligand libraries and efficient multiobjective optimization algorithms. Here we provide the tmQMg-L library, containing 30k diverse and synthesizable ligands with robustly assigned charges and metal coordination modes. tmQMg-L enabled the generation of 1.37 million palladium TMCs, which were used to develop and benchmark the Pareto-Lighthouse multiobjective genetic algorithm (PL-MOGA). With fine control over aim and scope, this algorithm maximized both the polarizability and highest occupied molecular orbital-lowest unoccupied molecular orbital gap of the TMCs within selected regions of the Pareto front, without requiring prior knowledge on the objective limits. Instead of genetic operations on small ligand fragments, the PL-MOGA did whole-ligand mutation and crossover operations, which in chemical spaces containing billions of systems, yielded thousands of highly diverse TMCs in an interpretable manner.

Performance prediction of the centrifugal air compressor for fuel cells considering degradation characteristics based on the hierarchical evolutionary model

The performance of the centrifugal air compressor is closely related to the efficiency and energy density of the fuel cell system, and extensive work has been done in this study to achieve precise performance predictions of the compressor considering degradation characteristics. The durability test of the compressor was carried out for 5000 h, and degradation characteristics with break-in time were discussed in detail. On this basis, the hierarchical evolutionary model was constructed to perform the performance predictions based on the Light Gradient Boosting Machine (LightGBM) and Multi-objective Whale Optimization Algorithm (MOWOA). The results demonstrate that the outlet pressure of the compressor decreases by 9.4 kPa and the power consumption increases by 0.71 kW at rated volume flow rate after the break-in of 5000 h, with attenuation ratios of 4.7 % and 12.2 %, respectively. The final non-dominated solution sets have excellent uniformity, diversity and convergence, proving that the developed MOWOA has high adaptability and strong optimization capability. The hierarchical evolutionary model achieves exact predictions for the outlet temperature, flow rate and pressure of the compressor, with absolute relative errors (AREs) below 1 % in outlet temperature and pressure while 5 % in outlet flow rate. All of these provide an efficient multi-objective optimization algorithm and an auto-calibrated hierarchical evolutionary model for further performance optimization of the air compressor.

A novel efficient multi-objective optimization algorithm for expensive building simulation models

A novel efficient multi-objective optimization algorithm for expensive building simulation models

Adaptive Basis Function Selection Enhanced Multisurrogate-Assisted Evolutionary Algorithm for Production Optimization

Summary Surrogate-assisted evolutionary algorithms (SAEAs) have become a popular approach for solving reservoir production optimization problems. The radial-basis-function network (RBFN) is a robust surrogate model technology suitable for reservoir development with numerous wells and a long production lifetime. There are several types of basis functions for constructing RBFN models. However, existing research shows that selecting the basis function with competitive performance for the current optimization problem is challenging without prior knowledge. In conventional SAEAs, the basis function is often predetermined, but its prediction accuracy for the problem at hand cannot be guaranteed. Furthermore, canonical SAEAs usually employ only one surrogate model for the entire optimization process. However, relying on a single surrogate model for optimization increases the probability of search direction misdirection due to prediction deviations. In this paper, a novel method named adaptive basis function selection enhanced multisurrogate-assisted evolutionary algorithm (ABMSEA) is introduced for production optimization. This method mainly includes two innovations. First, by training and testing different types of basis functions, the one with the best prediction performance is adaptively selected. Second, the ensemble model is constructed using the bootstrap sampling method, comprising multiple global surrogate models based on the selected best basis function. To search for a set of solutions that perform well on multiple surrogates, we employ an efficient multiobjective optimization (MOO) algorithm called nondominated sorting genetic algorithm II (NSGA-II). This algorithm uses the surrogates themselves as objective functions, aiming to find solutions that yield favorable results across multiple surrogates. The proposed method improves the efficiency of production optimization while enhancing global search capabilities. To evaluate the effectiveness of ABMSEA, we conduct tests on four 100D benchmark functions, a three-channel model, and an egg model. The obtained results are compared with those obtained from differential evolution (DE) and three other surrogate-model-based methods. The experimental results demonstrate that ABMSEA exhibits an accurate selection of competitive basis functions for the current optimization period while maintaining high optimization efficiency and avoiding local optima. Consequently, our method enables optimal well control, leading to the attainment of the highest net present value (NPV).

Dependent tasks offloading in mobile edge computing: A multi-objective evolutionary optimization strategy

Due to the proliferation of applications such as virtual reality and online games with high real-time requirements, Mobile Edge Computing (MEC) has become a promising computing paradigm that can improve user experience and reduce the task offloading latency. The cloud–edge-end collaborative offloading further addresses the problem of insufficient computing resources of edge servers owing to large-scale computing-intensive applications in MEC. However, existing offloading solutions often ignore the important factor of economic cost, making it hard for these solutions to achieve a sustainable cloud–edge-end collaborative computation. To this end, this paper considers a multi-user multi-server, cloud–edge-end collaborative offloading scenario in the presence of dependent offloading tasks for the sake of maximizing rewards and minimizing latency. Each user issues a computing-intensive application consisting of multiple dependent tasks, which are offloaded collaboratively by various computational resources. With the goal of maximizing the yield of offloading for users and server providers, a multi-objective optimization problem of joint task offloading and execution rewards is studied. Technically, a multivariate multi-objective optimization problem with three objectives is modeled. An efficient multi-objective evolutionary optimization algorithm based on MOEA/D is then developed to solve the latency minimization and reward maximization problems. Extensive simulation results verify the effectiveness of the algorithm and illustrate that the proposed algorithm can significantly improve user offloading benefits. In addition, a scalability evaluations of our proposed algorithm is conducted for demonstrating its feasibility in large-scale task offloading scenarios.

A decomposition-based multi-objective Jaya algorithm for lot-streaming job shop scheduling with variable sublots and intermingling setting

This study examined the lot-streaming job shop scheduling problem (LSJSP) with variable sublots and intermingling setting, which has rarely been considered in the literature and is beneficial to real industry settings. Upon variable sublots and intermingling setting, a multi-objective mixed-integer linear programming model (MILP) is formulated to achieve a tradeoff between the shortest tardiness and the minimum number of transferred sublots. Considering the NP-hard characteristic of the problem, the MILP model would get restricted in solving medium and large scale instances. Thus building an efficient multi-objective optimization algorithm is desirable. Motivated by the Jaya algorithm and decomposition idea, we propose a new algorithm framework called a decomposition based multi-objective Jaya algorithm (MOJA/D) to achieve a balance between exploration and exploitation. This MOJA/D framework can provide some guidance in algorithm construction for other multi-objective optimization problems. Following the MOJA/D framework, a specific MOJA/D metaheuristic is further designed for coping with the proposed problem. First, a novel forward and backward decoding strategy is designed to implement coevolution among different sub-problems and enhance the population diversity. Second, the Jaya updating mechanism is designed based on problem knowledge to guide the solutions moving towards the best solution and away from the worst solution. Moreover, considering the tradeoff between two conflicting objectives, the problem-specific local search strategies are designed and integrated with the neighborhood based local search strategies to enhance exploitation in the search process. The influence of parameter setting on MOJA/D is investigated by using design of experiments. The statistical results from extensive computational experiments demonstrate the effectiveness of the specific algorithm designs and show that the MOJA/D metaheuristic is superior to the existing algorithms in solving the proposed problem.

Process Parameter Selection for Production of Stainless Steel 316L Using Efficient Multi-Objective Bayesian Optimization Algorithm.

Additive manufacturing is a modern technique to produce parts with a complex geometry. However, the choice of the printing parameters is a time-consuming and costly process. In this study, the parameter optimization for the laser powder bed fusion process was investigated. Using state-of-the art multi-objective Bayesian optimization, the set of the most-promising process parameters (laser power, scanning speed, hatch distance, etc.), which would yield parts with the desired hardness and porosity, was established. The Gaussian process surrogate model was built on 57 empirical data points, and through efficient sampling in the design space, we were able to obtain three points in the Pareto front in just over six iterations. The produced parts had a hardness ranging from 224-235 HV and a porosity in the range of 0.2-0.37%. The trained model recommended using the following parameters for high-quality parts: 58 W, 257 mm/s, 45 µm, with a scan rotation angle of 131 degrees. The proposed methodology greatly reduces the number of experiments, thus saving time and resources. The candidate process parameters prescribed by the model were experimentally validated and tested.

Optimization of complex food formulations using robotics and active learning

The creation and optimization of formulated products represents a major challenge for science and industry in the food sector. Thereby, different raw materials are mixed and processed to meet predefined and often competing targets. During this procedure, applied experimental campaigns not only require expert knowledge, but, depending on the complexity, also cause a high consumption of resources and costs. In the present work, a fully automized milli-fluidic laboratory driven by the Thomsen sampling efficient multiobjective optimization (TSEMO) algorithm was designed. The methodology was successfully applied to optimize the aggregation process of a liquid formulation consisting of whey protein isolate, NaCl and CaCl2. Within 48 h 90 experiments could be performed without human intervention, resulting in a Pareto front formed by a set of 18 optimal recipes. It is thus a successful demonstration of an actively learning, self-driving food formulation process.

An Efficient Multi-objective Optimization Algorithm Exploiting Gradient Enhanced Kriging with Optimally Selected Basis Functions for Electromagnetic Design

An Efficient Multi-objective Optimization Algorithm Exploiting Gradient Enhanced Kriging with Optimally Selected Basis Functions for Electromagnetic Design

An efficient multi-objective optimization framework to maintain road networks based on Bayesian optimization and reliability theory

This study proposes an efficient asset management framework that enables decision makers to prioritize the maintenance of their roads, while focusing on the most critical road segments. In particular, this study first extends the application of reliability theory to estimate the overall network condition. Following that, this study proposes a new consequence of failure function for the whole road network based on road segments’ reliability, age, and road agency preferences. Finally, the study proposes an efficient multi-objective optimization algorithm, with the goal of maximizing overall network performance with the least maintenance and computational cost. The suggested framework was applied to three main roads in Jordan and validated statistically by comparing its performance to that of a typical multi-objective genetic algorithm (GA) under various scenarios and utilizing multiple performance metrics. The results show that the proposed algorithm outperforms the traditional GA in terms of objective function values, convergence, and solution spread.

Optimization Design of Planar Circle Coil for Limited-Size Wireless Power Transfer System

Power Transfer Efficiency (PTE) and Transferred Power (TP) are two crucial indicators of the wireless power transfer (WPT) system. However, in most compensatory topologies, the ideal coils design parameters of PTE and TP are inconsistent, implying that optimizing for one indicator would make another indicator less effective. As a result, in this article, the Thompson sampling efficient multi-objective optimization (TSEMO) algorithm is used to simultaneously optimize PTE and TP in the S-S compensation topology. Through the co-calculation of MATLAB and COMSOL, the coils can be optimized in the environment of magnetic material (ferrite) and conductive material (aluminum). Coils larger than the underwater vehicle’s size are deleted during the data interaction between MATLAB and COMSOL to guarantee that the optimal PTE and TP can be attained within the constrained area. Due to the open standard external software packages between MATLAB and COMSOL, the entire optimization process may be automated. The results show that for WPT systems with restricted input voltage, such as the battery, this design strategy can achieve better TP at the expense of lower PTE, which is highly beneficial in improving the system’s overall performance.

A Computationally Efficient Evolutionary Algorithm for Multiobjective Network Robustness Optimization

The robustness of complex networks is of great significance. Great achievements have been made in robustness optimization based on single measures, however, such networks may still be vulnerable to multiple attack scenarios. Therefore, recently, multiobjective robustness optimization of networks has received increasing attention. Nevertheless, several challenges remain to be addressed, including the different computational complexities in evaluating the objectives, insufficient diversity in the obtained networks, and high computational costs of the search process. In this article, we address the aforementioned challenges by developing a computationally efficient multiobjective optimization algorithm. Based on the unique features of complex networks, a new parallel fitness evaluation method guided by a network property parameter is designed and embedded in a reference vector-guided multiobjective evolutionary algorithm. In addition, a surrogate ensemble with heterogeneous inputs is constructed based on graph embedding information to efficiently estimate multiple robustness of networks. The proposed algorithm is validated on synthetic and real-world network data and our results show that the designed algorithm outperforms the state-of-the-art method with a marked improvement on the computational efficiency. Compared with other single-objective optimization methods, the proposed algorithm demonstrates a considerable exploitation ability.

Optimization of Formulations Using Robotic Experiments Driven by Machine Learning DoE

Formulated products are complex mixtures of ingredients whose time to market can be difficult to speed due to the lack of general predictable physical models for the desired properties. Here, we report the coupling of a machine learning classification algorithm with the Thompson sampling efficient multiobjective optimization (TSEMO) algorithm for the simultaneous optimization of continuous and discrete outputs. The methodology is successfully applied to the design of a formulated liquid product of commercial interest for which no physical models are available. Experiments are carried out in a semiautomated fashion using robotic platforms triggered by the machine learning algorithms. The procedure allows one to find nine suitable recipes meeting the customer-defined criteria within 15 working days, outperforming human intuition in the target performance of the formulations.

A Thompson Sampling Efficient Multi-Objective Optimization Algorithm (TSEMO) for Lithium-Ion Battery Liquid-Cooled Thermal Management System: Study of Hydrodynamic, Thermodynamic, and Structural Performance

Abstract The efficient design of battery thermal management systems (BTMSs) plays an important role in enhancing the performance, life, and safety of electric vehicles (EVs). This paper aims at designing and optimizing cold plate-based liquid cooling BTMS. Pitch sizes of channels, inlet velocity, and inlet temperature of the outermost channel are considered as design parameters. Evaluating the influence and optimization of design parameters by repeated computational fluid dynamics calculations is time consuming. To tackle this, the effect of design parameters is studied by using surrogate modeling. Optimized design variables should ensure a perfect balance between certain conflicting goals, namely, cooling efficiency, BTMS power consumption (parasitic power), and size of the battery. Therefore, the optimization problem is decoupled into hydrodynamic performance, thermodynamic performance, and mechanical structure performance. The optimal design involving multiple conflicting objectives in BTMS is solved by adopting the Thompson sampling efficient multi-objective optimization algorithm. The results obtained are as follows. The optimized average battery temperature after optimization decreased from 319.86 K to 319.2759 K by 0.18%. The standard deviation of battery temperature decreased from 5.3347 K to 5.2618 K by 1.37%. The system pressure drop decreased from 7.3211 Pa to 3.3838 Pa by 53.78%. The performance of the optimized battery cooling system has been significantly improved.

An efficient multi-objective optimization algorithm based on level swarm optimizer

In the past few decades, evolutionary multi-objective optimization has become a research hotspot in the field of evolutionary computing, and a large number of multi-objective evolutionary algorithms (MOEAs) have been proposed. However, MOEA is still faced with the problem that the diversity and convergence of non-dominated solutions are difficult to balance. To address these problems, an efficient multi-objective optimization algorithm based on level swarm optimizer (EMOSO) is proposed in this paper. In EMOSO, a sorting method is introduced to balance the diversity and convergence of non-dominated solutions in the whole population, which is based on non-dominated relationship and density estimation. Meanwhile, a level-based learning strategy is introduced to maintain the search for non-dominated solutions. Finally, DTLZ, ZDT and WFG series problems are utilized to verify the performance of the proposed EMOSO. Experimental results and statistical analysis indicate that EMOSO has competitive performance compared with 6 popular MOEAs. The source code of EMOSO is provided at: https://github.com/xuweizhang163/EMOSO.

Inverse kinematics solution and posture optimization of a new redundant hybrid automatic fastening system for aircraft assembly

PurposeThe purpose of this paper is to use the redundancy of a new hybrid automatic fastening system (HAFS) for aircraft assembly in the best way.Design/methodology/approachFirst, the kinematic model of HAFS is divided into three sub-models, which are the upper/lower tool and parallel robot. With the geometric coordination relationship, a comprehensive kinematic model of the HAFS is built by mathematically assembling the sub-models based on the DH method. Then, a novel master-slave decoupling strategy for inverse kinematics solution is proposed. With the combination of the minimum energy consumption and the comfortable configuration, a multi-objective redundancy resolution method is developed to optimize the fastening configuration of the HAFS, which keep the HAFS away from the joint-limits and collision avoiding in the aircraft panel assembly process.FindingsAn efficient multi-objective posture optimization algorithm to use the redundancy in the best way is obtained. Simulation and an experiment are used to demonstrate the correctness of the proposed method. Moreover, the position and orientation errors of the drilling holes are within 0.222 mm and 0.356°, which are accurate enough for the automatic fastening in aircraft manufacturing.Practical implicationsThis method has been used in the HAFS control system, and the practical results show the aircraft components can be fastened automatically through this method with high efficiency and high quality.Originality/valueThis paper proposes a comprehensive kinematic model and a novel decoupling strategy for inverse kinematic solution of the HAFS, which provides a reference to utilize the redundancy in the best way for a hybrid machine with redundant function.

Energy-Oriented Scheduling for Hybrid Flow Shop With Limited Buffers Through Efficient Multi-Objective Optimization

Efficient scheduling benefits productivity promotion, cost savings, and customer satisfaction. In recent years, with a growing concern about the energy price and environmental impact, energy-oriented scheduling is going to be a key issue for sustainable manufacturing. In this paper, we investigate an energy-oriented scheduling problem deriving from the hybrid flow shop with limited buffers. First, we formulate the scheduling problem with a mixed integer linear programming (MILP) model, which considers two objectives including minimizing the total weighted tardiness (TWT) and non-processing energy (NPE). To solve the NP-hard problem in the strong sense, we develop an efficient multi-objective optimization algorithm under the framework of the multi-objective objective evolutionary algorithm based on decomposition (MOEA/D). We devise a job-permutation vector to represent the scheduling solution and cover its search space. Since NPE is a non-regular function, we develop a two-pass decoding procedure composed of a discrete-event system (DES) simulation procedure and a greedily post-shift procedure. Besides, we apply an external archive population (EAP) to guide the algorithm to converge on a Pareto frontier and a local search procedure to enhance the diversity of the population. Finally, we conduct extensive computational experiments to verify the effectiveness of the proposed energy-oriented multi-objective optimization (EOMO) algorithm. The results presented in this paper may be useful for future research on energy-oriented scheduling problems in realistic production systems.

Multi-objective hydropower station operation using an improved cuckoo search algorithm

Efficient utilization of water resources in hydropower station operation is an important part of mitigating water and energy scarcity. Exploring efficient multi-objective optimization algorithms and studying the trade-off between water and energy have become the primary goal of multi-objective hydropower station optimal operation (MOHSOO). In this paper, a new improved multi-objective cuckoo search (IMOCS) algorithm is proposed to overcome the shortcomings of MOCS. Specifically, a population initialization strategy based on constraint transformation and the individual constraints and group constraints technique (ICGC) and a dynamic adaptive probability (DAP) are used to improve the search efficiency and the quality of solutions, respectively. A flock search strategy (FSS) is proposed to greatly speed up the convergence and improve the quality of the non-dominated solutions. In addition, the MOCS and NSGA-II are presented as a comparison to test the performance of IMOCS as well as three hybrids of MOCS combined with these strategies. An MOHSOO model of Xiaolangdi and Xixiayuan cascade hydropower stations in the lower Yellow River is built to verify the effectiveness of these algorithms together with five benchmark problems. The results show that IMOCS performs better than other algorithms in convergence speed, convergence property, and diversity of solutions. For the Xiaolangdi hydropower station, there is a strong competitive relationship between power generation and water supply from September to next February, which severely restricts the power generation of the hydropower station.