Adaptive Multi-objective Optimization Research Articles

Efficient slicing scheme and cache optimization strategy for structured dependent tasks in intelligent transportation scenarios

The challenges posed by structured large-scale tasks to resource-sensitive intelligent transportation systems have been acknowledged, particularly regarding the need to reduce delay and energy consumption during the caching and offloading processes. To address these challenges and improve the quality of service for vehicular users, a cloud–edge-end collaboration caching strategy (CACCSC) based on structured task content awareness was proposed in this paper. The dependencies among task fragments were modeled through fuzzy judgment criteria. In addition, a system delay model, an energy consumption model, and an edge server load balancing model were developed, along with a multi-objective optimization model that integrates system delay, energy consumption, and edge server load balancing variance. To solve this multi-objective optimization problem, an adaptive multi-objective optimization algorithm (MDE-NSGA-III) was developed, which combines an enhanced version of the Differential Evolution algorithm with improvements to the NSGA-III algorithm. Finally, it has been demonstrated through simulation experiments that when the number of users in the system reaches 35, the system delay, energy consumption, and load balancing variance of the MDE-NSGA-III optimization scheme proposed in this paper are 6.1%, 6.6%, and 25% lower than those of the NSGA-III scheme, 15.8%, 10%, and 41.7% lower than those of the NSGA-II scheme, and 62.7%, 20.7%, and 8.3% lower than those of the PeEA scheme.

Inverse parameter estimation for the white–metzner equation using extrudate diameter measurements

Abstract This study presents a method to determine the elastic modulus of the White–Metzner (WM) viscoelastic model, which is used to describe polymer melt flow. In processes involving polymeric liquids, elastic effects are essential for accurate simulations. In particular, phenomena such as melt swelling after extrusion and upon gate passage in injection molding are critical to process simulation. However, systematic procedures for material characterization remain underdeveloped since implementation of an appropriate material model is challenging due difficulties inherent in parameter determination. In this work, to account for the shear thinning behavior, which is essential when handling polymeric liquids in current processes, while also incorporating elastic properties, the WM equation was employed. The method employs the post-extrusion swelling phenomenon where the diameter of the extruded material expands after exiting the capillary die. It integrates melt swell measurements with numerical simulations based on the WM equation and highlights the importance of considering gravity in the simulation. A laser scanning microscope is used to measure the extruded diameter while adaptive multi-objective optimization identifies unknown parameters in the model to align the simulation results with the measured diameter. This approach successfully determined the elastic modulus of the PET-PEN copolymer, establishing a system for determining the viscoelastic properties of polymer melts.

An adaptive multi-objective optimization method for structure-connection-performance design of commercial vehicle cab

A variable screening-approximate model-adaptive multi-optimization algorithm integrated method is proposed to solve the high-dimensional nonlinear problems for lightweight optimization of cabs. Firstly, a full-parametric body-in-white model of the cab is established using implicit parameterization technology, and the simulation analysis of the bending and torsional stiffness, natural modal characteristics, and passive safety are established. Then, considering the structure and connection process parameters, the design variables are screened based on a gray relational analysis-TOPSIS (GRA-TOPSIS). Finally, integrated multi-objective optimization of cab structure-connection-performance is carried out by an adaptive RBF neural network optimization method, and the optimal solution is determined by the GRA-TOPSIS method. Compared with the initial model, the mass of cab is reduced by 3.28% after optimization design. It provides guidance for the lightweight design of body.

Adaptive surrogate assisted multi-objective optimization approach for highly nonlinear and complex engineering design problems

Despite enormous advances in computer power, computationally costly models impede the use of traditional optimization approaches that must be invoked repeatedly during the optimization process in practical engineering applications. Surrogate models have been found to be a promising endeavor in multi-objective optimization problems involving expensive analysis and simulation processes such as multi-physics modeling and simulation, finite element analysis (FEA), and computational fluid dynamics (CFD. Developing an optimization algorithm that can easily identify the Pareto frontier of highly nonlinear multi-objective optimization problems with less computation cost is the aim of this work. In this paper, an Adaptive Multi-Objective Optimization approach based Surrogate models (AMOS) is developed to reduce computation cost of fitness evaluations and discover the Pareto optima for multi-objective optimization problems with comparable high accuracy. AMOS explores the design space by sampling using LHD to identify promising regions. Then, AMOS exploits the identified promising region by adaptively constructing the most suitable surrogate model, which could be response surface, radial basis, or Kriging surrogates, in the feasible design space based on root mean square error values (RMSE). AMOS stops iterating when a termination criterion is met, and a Pareto frontier is identified based on developed guidance and fitness functions. AMOS has successfully identified the pareto frontier of practical engineering optimization problems with expensive black box functions and significantly reduced the computation cost. The novel method was put to the test utilizing real-world challenges and engineering design examples such vehicle magnetorheological design and wind turbine airfoil geometry.

Dynamic adaptive multi-objective optimization algorithm based on type detection

Dynamic multi-objective optimization problems (DMOPs) are multi-objective problems that are influenced by dynamically changing environmental parameters. Most current algorithms for solving DMOPs only respond to dynamic changes in the decision space or objective space and also ignore the impact of the type of DMOPs on the algorithm. The changes in the Pareto-optimal solution (POS) and Pareto-optimal front (POF) may affect the type of change in DMOPs. Therefore, this paper proposed an adaptive dynamic multi-objective evolutionary algorithm for type detection (TDA-DMOEA). First, the dynamic detection operator is designed to identify the types of dynamic problems. The Wilcoxon signed-rank test and Hyper Volume (HV) are used to detect the difference of POS and POF in two adjacent environments respectively. Then, different response strategies are designed to cope with different types of changes in DMOP. In particular, a multi-angle-based transfer learning method (MA-TL) with a closed kernel function is derived when faced with simultaneous changes in POS and POF. Finally, a comprehensive study of the commonly used benchmark set of DMOPs is presented, and the proposed algorithm achieves better performance in optimizing DMOPs.

Adaptive multi-objective competitive swarm optimization algorithm based on kinematic analysis for municipal solid waste incineration

Multi-objective optimization for the municipal solid waste incineration process is considered as a valuable technique to improve energy recovery and reduce pollutant emission. However, the complex mechanism analysis and multimodal problem of the municipal solid waste incineration process set challenges for both the modeling and optimization studies. To overcome this problem, an adaptive multi-objective optimization for the municipal solid waste incineration process is proposed in this paper. First, a bi-objective model of the municipal solid waste incineration, the basis for optimization, is established based on mass balance and energy balance, which takes furnace temperature and flue gas oxygen content as decision variables to mathematically deduce the generated heat and exhaust gases. Second, an adaptive multi-objective competitive swarm optimization algorithm is proposed for the optimization of the municipal solid waste incineration process. Two-step competition and multi-strategy learning are designed to provide a clear division of labor for particles and a novel idea for detecting evolutionary environment is proposed based on kinematic analysis of particles. Finally, relevant experiments are conducted on benchmark instances and the municipal solid waste incineration optimization model. The proposed algorithm shows promising convergence, diversity, fastness by comparing with several representative and state-of-the-art algorithms. The proposed algorithm achieves the optimization effects with 4.36% improvement of the available heat for power generation and 4.13% reduction of the exhaust gas assessment in the optimization of the municipal solid waste incineration process.

An adaptive multi-objective joint optimization framework for marine hybrid energy storage system design considering energy management strategy

The electric propulsion ship with the hybrid energy storage system (HESS) has environmental friendliness and significant advantages in terms of low fuel consumption. Due to the high maneuverability and load fluctuation of vessels, marine HESS design problem is nonlinear and multi-objective. Aiming at HESS design problem with complex working conditions during vessel practical operation process, and since there is a strong coupling between HESS and power allocation, in this paper an adaptive multi-objective joint optimization framework (AMJOF) for HESS design considering energy management strategy (EMS) is proposed. A multi-objective joint optimization method is introduced into the framework to achieve strong working condition adaptability and reach the most suitable energy management for marine HESS design with the lowest investment cost and minimum battery degradation. In addition, based on adaptive segmentation and frequency control, a novel EMS is proposed, which achieves efficient power allocation, and the evaluation indicator is introduced to quantify the performance of different optimization. The results indicate that the AMJOF can achieve excellent performance and find the optimal solution for operation of the ship.

Enhancing IAQ, thermal comfort, and energy efficiency through an adaptive multi-objective particle swarm optimizer-grey wolf optimization algorithm for smart environmental control

People in modern societies spend most of their time in buildings, and a considerable amount of energy is used to maintain indoor air quality (IAQ) and thermal comfort levels to ensure people's health and productivity. Thus, to ensure the energy efficiency of buildings, smart environmental control must be implemented through prediction and optimization based on artificial intelligence (AI) algorithms. However, rapid and accurate environmental control based on comprehensive consideration of IAQ, thermal comfort, and energy savings remains to be accomplished. Therefore, this paper describes the development and application of a multi-objective AI algorithm that can be deployed in practical environmental control to rapidly and accurately optimize the IAQ, thermal comfort levels, and energy efficiency of buildings. First, a database of indoor airflow and temperature distribution is established through computational fluid dynamics (CFD) simulations and chamber experiments. Then, with the database, the back-propagation neural network (BPNN) model is created to rapidly predict air pollution concentrations and thermal comfort levels. If these predictions using the BPNN model do not satisfy the setup requirements, an adaptive multi-objective particle swarm optimizer-grey wolf optimization (AMOPSO-GWO) algorithm is used to identify the optimal strategy for maximizing indoor air quality, thermal comfort levels, and energy saving. The results demonstrate that the BPNN-based AMOPSO-GWO algorithm has high predictive accuracy (>90%). The mean reduction in air pollution concentrations, increase in thermal comfort levels, and average energy savings are 31%, 45%, and 35%, respectively.

An Adaptive Multiobjective Multitask Service Composition Approach Considering Practical Constraints in Fog Manufacturing

Fog manufacturing (FMfg) is an emerging real-life cloud manufacturing (CMfg) paradigm based on digital twin models, which perceive finely dynamic processes and provide on-demand optimization services. Service composition as a core plays a vital role in achieving the optimization goal. Service composition issues in actual CMfg are mostly multitask problems that need considering dynamic factors. Otherwise, the planned production process will deviate significantly from actual situations. However, such multitask service composition (MTSC) models have scarcely been studied. Thus, this article formulates a multiobjective MTSC model considering the crucial resource competition process between multitasks under multiple practical constraints (MPC). Then, to solve the MTSC–MPC, this article develops an adaptive multiobjective whale optimization algorithm (AMOWOA) containing well-designed strategies. Numerical experiments and application cases verify that AMOWOA outperforms the comparison algorithms. AMOWOA continually can optimize and adaptively adjust the service composition process based on the crucial manufacturing resource available time under actual constraints in FMfg environment.

Reactive power optimization based on adaptive multi-objective optimization artificial immune algorithm

In this study, an adaptive multi-objective optimization artificial immune algorithm is presented for reactive power optimization. In the proposed algorithm, a non-inferior solution ranking method based on Pareto coefficient is proposed to rank antibodies. The fitness evaluation mechanism based on individual neighborhood selection and adaptive cloning operator ensure the convergence of the algorithm, and the chaotic random sequence is added to the mutation operator to improve the diversity of the antibody population. Considering the minimum active power loss, the maximum static voltage stability margin and the best voltage level, a multi-objective reactive power optimization model is established by introducing the static voltage stability index. IEEE-30 bus system is chosen as a research object. Combined with technique for order preference by similarity to ideal solution method, after the multi-attribute decision-making of the Pareto solution set, the optimal solution cannot only ensure the economic operation of the system, but also enhance the voltage stability of the power grid. The designed reactive power optimization algorithm is effective.

An application of adaptive normalization evolutionary optimization ANMOGA for missile fin design based on trajectory parameters

System design is a complicated and iterative process. Accordingly, a robust optimization procedure should be involved during the design process to minimize complications and time consumed for the whole process. The present research proposes a new methodology to design missile fins that can lessen the gap between the conceptual and final phases during the missile design process. The proposed methodology is based on a new dynamic adaptive multi-objective optimization algorithm accompanied by a selection scheme to choose a single optimum design amongst a Pareto set of solutions. The adaptive normalization multi-objective evolutionary algorithm can be used in any system design. This algorithm is capable of predicting Pareto fronts for multi-objective optimization problems at very good dispersion. In addition, a selection scheme is provided to help decision-makers choose a single optimum amongst Pareto front points. The proposed algorithm is tested against several multi-objective test functions as well as the application of a selection scheme to the obtained Pareto fronts. The results showed the robustness of the proposed algorithm in the prediction of convex fronts. However, it still needs more improvement to deal with non-convex ones. An application for the considered algorithm is also provided as a case study to design tail fins for a conventional missile configuration. The output configuration of the designed fins has very reasonable dimensions when compared to similar configurations.

Economic-effectiveness analysis of micro-fins helically coiled tube heat exchanger and optimization based on multi-objective differential evolution algorithm

In this study, the heat transfer characteristics and hydrodynamics of a new type of micro-fin helically coiled tube (MF-HCT) are modelled and compared with those of typical smooth helically coiled tubes (HCTs). Calculation models are developed and compared for both helically coiled tube heat exchangers (HCTHEXs) and micro-fin helically coiled tube heat exchangers (MF-HCTHEXs). The total cost and effectiveness are selected as the objective functions, and five independent structural parameters are selected for each type of HCTHEX. Subsequently, an adaptive multi-objective optimization differential evolution algorithm is implemented for both of them, and the results are compared. The results indicate that, for Reynolds numbers smaller than 22,000, the MF-HCT performs better than the HCT, and the total cost of the MF-HCTHEX is lower than that of the HCTHEX under the same effectiveness. The difference between their total cost increases with their effectiveness and can be up to 79,000 €. For both the HCTHEX and MF-HCTHEX, when the effectiveness is less than 0.9, it is more appropriate to select a larger winding angle, layer spacing, and tube spacing.

Research on Cancer Molecular Typing Based on High-Throughput Sequencing Technology.

This paper studies the role of high-throughput measurement technology in cancer molecular typing. Based on the Dendrix algorithm, the model proposed in this paper selects the gene replication time as an inherent attribute that affects the frequency of gene mutations and adds it to the model. After setting the size of the gene set, compared with the Dendrix algorithm, the model does not need to delete the gene set that has been found in the process of searching the pathway, and it can find more driving pathway gene sets. Based on the high coverage and high exclusivity of the driving gene set in the pathway and the influence of gene covariates, this paper constructs an adaptive multiobjective optimization model. In order to overcome the problem of gene mutation heterogeneity, this model introduces gene covariates as the weight of gene mutation frequency so that the model is adaptive to each gene. The analysis of the research results shows the reliability of high-throughput sequencing technology.

Adaptive multi-objective optimization for emergency evacuation at metro stations

Evacuation is a critical issue at metro stations, where damage, or even death, could be caused due to unexpected accidents if without proper evacuation. Multi-objectives are often desired in the evacuation management, and evacuation strategies should be tailored due to the dynamic features of overcrowded passengers and high uncertainty at the metro stations. A simulation-based approach integrating Light Gradient Boosting Machine (LightGBM) and Non-dominated Sorting Genetic Algorithm III (NSGA-III) is proposed to realize the automatic evacuation evaluation and adaptive optimization at metro stations. A framework consisting of 3 objectives and 9 influential factors is developed for the evacuation evaluation and adaptive optimization. A LightGBM based meta-model is used to construct the relationship between influential factors and objectives. A LightGBM and NSGA-III integrated optimization algorithm is employed to automatically evaluate the evacuation events and seek the adaptive strategies for the station renovation in order to achieve a safe evacuation under specific conditions. A station model simulating a metro station in Singapore is constructed to test the effectiveness and applicability of the proposed approach. It is found that (1) Among 50 target station based cases, 33 failing cases (at least one of the objectives fails) are identified, and the automatic evaluation results indicate the studied metro station could successfully evacuate the passengers when the passenger volume is no more than 1000, but it is very likely to fail when a higher volume exists; (2) Adaptive optimization strategies can be found for every different scenario, and an average 24.8% of the improvement degree can be achieved for all scenarios; (3) The adaptive multi-objective optimization is more cost-effective, presenting an average cost efficiency 2.04, which is significantly higher than the average of the non-adaptive optimization cost efficiency, 0.57. The novelty of this research lies in that (a) A LightGBM-based meta-model is built to construct the relationship between desired multi-objectives and the influential factors, which lays the foundation for the evacuation management from multi-objective optimization perspective; (b) The LightGBM and NSGA-III integrated model can realize the automatic evacuation evaluation and accordingly provide the adaptive strategies under specific conditions.

A multi-objective residential load management based on self-adapting differential evolution

• An adaptive multi-objective optimization method for demand response is proposed. • Epsilon constrains method is used to get a single point lies within Pareto front. • A tiered incentive can encourage customers to participate in demand response program. • The results indicate significant cost reduction (53.34%) and peak load (43.04%). The current grid system is unable to meet the ever-increasing demands for energy due to annual population growth and the increasing number of energy-consuming devices that are being used in the modern era. One solution that helps to balance available generation capacity with required demand, and increases the grid reliability is the demand response (DR). For this purpose, this paper presents a residential load scheduling algorithm to manage the operation time of household appliances depending on the time of use (ToU) tariff. A self-adapting multi-objective differential evolution (SaMODE) algorithm was developed to find the optimal operation points of home energy appliances, with the aim of minimizing the customer electricity bill and peak load while maintaining the customer's convenience. The proposed tri-objective problem is solved by using the Epsilon-constraint optimization method. In addition, a prior approach is adopted to manage the tradeoff among objectives according to the customer's preferences. The desirable customer preferences are ensured through the prior approach by restricting two objectives to specific values, then finding an optimal corresponding solution for them. The findings and discussion demonstrate that the proposed SaMODE strategy benefits the customers by reducing their energy bill for up to 53.34% with an acceptable level of inconvenience. On the other hand, the power suppliers have also received benefits by decreasing the peak energy demand by up to 43.04%, which in turn increases the stability of the power system and lessen the burden on the utility company.

Adaptive selection multi-objective optimization method for hybrid flow shop green scheduling under finite variable parameter constraints: case study

The energy consumption loss is high particularly in manufacturing processes involving heating furnaces. Moreover, the mandatory constraints in continuous heating stage bring difficult challenges to production scheduling. To improve the production efficiency and reduce the energy consumption in a hybrid flow shop with continuous and discrete processing stages, this study developed an adaptive selection multi-objective optimization algorithm with preference (ASMOAP). The mandatory constraints of continuous processing stage are transformed into one of the optimization objectives, which is defined as maximum excess value of adjustment time in this paper. A multi-objective optimization scheduling model with the makespan, energy consumption, and maximum excess of adjustment time is established. The optimization preference is designed in the proposed multi-objective optimization algorithm. The maximum excess of adjustment time is set as the multi-objective optimization preference. Three adaptive selection strategies are designed for the algorithm based on the proportions of outstanding and preference individuals to eliminate constraint conflicts. Presented results prove that the proposed algorithm could effectively solve hybrid flow shop scheduling problem considering discrete and continuous processing stages with limited time. It can be applied to obtain a better feasible solution while improving the efficiency and reducing the energy consumed in practical production processes.

New adaptive intelligent grey wolf optimizer based multi-objective quantitative classification rules mining approaches

The classification rule mining problem is one of the most important tasks of data mining. A constructed classification model is needed to have high accuracy, comprehensiveness, interestingness, etc. Furthermore, when the data composed of quantitative, numerical, or mixed data types, automatically discovering the appropriate intervals at the time of the mining process is a hard problem. The standard classification algorithms in the literature do not find the intervals of the quantitative attributes in the rules and this is performed a priori that causes the modification of datasets. Automatically constructing a successful classification model consisting of an explainable rule set without changing or modifying the data in artificial intelligence and machine learning is a very hot topic. Due to the philosophy of constantly researching to discover more accurate, surprising, and comprehensible rule sets and the absence of the most effective algorithm for all kinds of data sets, new methods or new versions of existing methods are proposed. In this study, automatic mining of high-quality rule set is handled as a multi-objective optimization problem due to the nature of the necessities and novel adaptive multi-objective intelligent search and optimization algorithms based on Grey Wolf Optimizer are proposed for this task. The datasets are considered as search spaces and the proposed adaptive Pareto based multi-objective Grey Wolf Optimizer algorithms are designed and applied as search methods for automatically discovering the high-quality rules. The proposed intelligent methods and successful classification algorithms such as naive Bayes, k-NN, support vector machines, decision trees, and RIPPER are tested in five real-world data sets with different characteristics. The obtained results show the efficiency of the proposed intelligent search and optimization methods.

On adaptive multi-objective optimization for greener wired networks

In this study, we develop a novel adaptive Multi-Objective Optimization (MOO) algorithm to jointly minimize the Power Consumption (PC) and Maximum Link Utilization (MLU) of wired computer networks. This novel algorithm, based on Non-dominated Sorting Genetic Algorithm II (NSGA-II), is able to discover the optimal link weights configuration for wired networks based on live network traffic data. The goal is to minimize both PC and MLU when this optimal link weights configuration is applied to the network. The impact of the Internet and wired networks’ growing power consumption on costs and environment continues to be a major concern of network operators. Our adaptive MOO scheme can be applied to any wired network to reduce its power consumption with acceptable load balancing to help achieve greener energy-efficient networks. In order to validate our solution, we run experiments on two different network topologies with various network traffic data to compare the performance of three different methods of updating the link weights: random, delta-weight, and hybrid. The experiment results show that all three methods can find optimal solutions to achieve both reduced PC and MLU. Compared to the default link weights configuration, the optimal solution found by hybrid approach is the best since it can reduce the PC by 35.24%, while reducing MLU by 42.86% for specific traffic pattern on Abilene network topology.

Intelligent and Adaptive Multi-Objective Optimization in WANET Using Bio Inspired Algorithms

A WANET is a specific type of Adhoc network with a community of specialized, self-organizing mobile devices. Because of the complex existence of mobile devices and wireless networking, the adhoc network communicating in wireless manner faces many problems related to the topological organizations , battery capacity limitations that results in energy restrictions, transmission of information, etc. The significant attributes of an adhoc network communicating in wireless manner are the quality of service and the percentage of energy used. Nowadays as the bio-inspired computing plays a major role in the research field. Specifically bio-inspired algorithms motivated by nature's actions are used immensely to accomplish optimization in issues faced in communication. Massive works have been reported in the recent years to enhance the quality of service utilizing the Bio-inspired computing due to its intelligent and adaptive nature. The paper proposes the intelligent and an adaptive routing employing the bio inspired genetic bee colony algorithm and the ant colony optimization to have optimized energy utilization maximizing the longevity of the network. The proposed method is evaluated suing the network simulator-2 on the basis of the energy utilization, longevity and the service quality (Throughput) of the network, the results observed for the method put forth were better compared to the existing methods.

A Multi-Objective Task Scheduling Method for Fog Computing in Cyber-Physical-Social Services

Fog computing provides users with data storage, computing, and other services by using fog layer devices close to edge devices. Tasks and resource scheduling in fog computing has become a research hotspot. For the multi-objective task-scheduling problem in fog computing, an adaptive multi-objective optimization task scheduling method for fog computing (FOG-AMOSM) is proposed in this paper. In this method, the total execution time and the task resource cost in the fog network are taken as the optimization target of resource allocation, and a multi-objective task scheduling model is designed. Since the objective model is a Pareto optimal solution problem, the global optimal solution can be obtained by using multi-objective optimization theory and the improved multi-objective evolutionary heuristic algorithm. Moreover, to obtain a better distribution of the current task scheduling group, the neighborhood is adaptively changed according to the current situation of the task scheduling group in fog computing, which avoids the problem that the neighborhood value caused by the neighborhood policy in the multi-objective algorithm affects the distribution of the task scheduling population. This algorithm is used to solve the non-inferior solution set of the utility function index of fog computing task scheduling to try to solve the multi-objective cooperative optimization problem in fog computing task scheduling. The results show that the proposed method has better performance than other methods in terms of total task execution time, resource cost and load dimensions.