Multi-objective discrete optimization of laminated structures

Heuristic-based multi-objective optimization design for viscous dampers in RC frame structures considering soil-structure interaction

In order to solve the problem of the multi-objective spectrum allocation on the joint optimization of maximal network utility and fairness of users in cognitive radio network, based on quantum bee colony theory and membrane computing, a novel multi-objective discrete combinatorial optimization algorithm, named membrane-inspired quantum bee optimization, is proposed. The global optimal solution of single objective can be searched in the elementary membranes, and Pareto front solutions which take account of network utility and fairness, can be obtained from skin membrane with the proposed method. The multi-objective optimization algorithm, which can solve both single objective and multi-objective optimization problems at the same time, is designed by the communication rules between membranes, the cooperative evolution of foraging behavior based on quantum state, and non-dominated sorting. Compared with classical color-sensitive graph coloring algorithm, genetic algorithm, quantum genetic algorithm, and particle swarm optimization under different objective functions, the proposed spectrum allocation method can search the global optimal solution of single objective as shown by the simulation results, and it is superior to classical spectrum allocation algorithms and existing intelligence spectrum allocation methods. The optimal Pareto front solutions of multi-objective spectrum allocation are also obtained.

ABSTRACTTo address multiobjective, multi constraint and time-consuming structural optimization problems in a vehicle axle system, a multiobjective cooperative optimization model of a vehicle axle structure is established. In light of the difficulty in the nondominated sorting of the NSGA-II algorithm caused by inconsistent effects of the uniformity objective function and physical objective function, this paper combines a multiobjective genetic algorithm with cooperative optimization and presents a strategy for handling the optimization of a vehicle axle structure. The uniformity objective function of the sub discipline is transformed to its self-constraint. Taking the multiobjective optimization of a vehicle axle system as an example, a multiobjective cooperative optimization design for the system is carried out in ISIGHT. The results show that the multiobjective cooperative optimization strategy can simplify the complexity of optimization problems and that the multiobjective cooperative optimization method based on an approximate model is favorable for accuracy and efficiency, thereby providing a theoretical basis for the optimization of similar complex structures in practical engineering.

Carbon capture, utilization and sequestration (CCUS) is one of the most promising and effective technologies to reduce carbon emissions, with significant potential for development in the coming decades. The CCUS coupling project utilizing CO2 Enhanced Oil Recovery (CO2-EOR) can pave the way to economically viable CO2 sequestration processes. However, the non-linear and non-convex relationships among CO2 sequestration capacity, CO2-EOR effectiveness, and economic benefits significantly increase uncertainty in technological policy formulation. This paper establishes a comprehensive optimization framework utilizing the Particle Swarm Optimization (PSO) algorithm, encompassing the formulation of objective functions, the development of numerical simulation models for typical reservoirs, and single-objective and multi-objective optimization method. In single-objective optimization, two objective functions—oil recovery factor and CO2 sequestration efficiency—are accomplished through weight assignment. This approach optimizes the combined objective function across different injection modes, including Continuous gas injection (CGI), Water-alternating-gas (WAG), and Gas injection after water flooding (GIW), while also considering diverse preferences for development strategies. In multi-objective optimization, the optimal well control parameter sets for CO2 sequestration and Net Present Value (NPV) are derived under different injection modes using Multi-Objective Particle Swarm Optimization (MOPSO) algorithm and the Pareto front. The results indicate that the multi-objective optimization method constructed in this paper can effectively address the problem of Co-Optimization of CO2-EOR projects. Compared to the base scenario and the single-objective optimization, the MOPSO optimization solutions on the Pareto front demonstrate superior overall performance in terms of oil recovery, sequestration capacity, and economic benefits through reasonable injection and production parameters that control the CO2 flooding front and expand the sweep efficiency. Water-Alternating-Gas (WAG) exhibits advantages in recovery rate and NPV while demonstrating lower sensitivity to carbon subsidies and CO2 injection costs. The comprehensive optimization method proposed in this paper will significantly enhance the technical decision-making capabilities related to CCUS coupling projects in carbonate reservoirs, providing guidance for safe and efficient CCUS technologies.

Multiobjective clustering techniques have been used to simultaneously consider several complementary aspects of clustering quality. They optimize two or more cluster validity indices simultaneously, they lead to high-quality results, and have emerged as attractive and robust alternatives for solving clustering problems. This paper provides a brief review of bio-Inspired multiobjective clustering, and proposes a bee-inspired multiobjective optimization (MOO) algorithm, named cOptBees-MO, to solve multiobjective data clustering problems. In its survey part, a brief tutorial on MOO and multiobjective clustering optimization (MOCO) is presented, followed by a review of the main works in the area. Particular attention is given to the many objective functions used in MOCO. To evaluate the performance of the algorithm it was executed for various datasets and the results presented high quality clusters, diverse solutions an the automatic determination of a suitable number of clusters.

Performance evaluation and multi-objective optimization of an innovative double-stage thermoelectric heat storage system for electricity generation

The performance of a plate heat exchanger (PHE) using water as the working fluid with zigzag flow channels was optimized in the present study. The optimal operating conditions of the PHE are explored experimentally by the Taguchi method, with effectiveness as the objective function. The results are further verified by the analysis of variance (ANOVA). In addition, the zigzag flow channel geometry is optimized by the non-dominated sorting genetic algorithm-II (NSGA-II), in which the effectiveness and pressure drop of the PHE are considered the two objective functions in the multi-objective optimization process. The experimental results show that the ratio of flow rates is the most important factor affecting the effectiveness of the PHE. The optimal operating conditions are the temperatures of 95 °C and 10 °C at the inlets of hot and cold water flows, respectively, with a cold/hot flow rate ratio of 0.25. The resultant effectiveness is 0.945. Three geometric parameters of the zigzag flow channel are considered, including the entrance length, the bending angle, and the fillet radius. The sensitivity analysis of the parameters reveals that a conflict exists between the two objective functions, and multi-objective optimization is necessary for the zigzag flow channel geometry. The numerical simulations successfully obtain the Pareto optimal front for the two objective functions, which benefits the determination of the geometric design for the zigzag flow channel.

Many real-world design problems involve simultaneous optimisation of multiple objectives. The considered grillage design is a multi-objective optimisation problem since there are two objective functions, namely: the volume design and the cost design. Thus, the solution of this optimisation problem requires specialised method suitable for multi-objective problems. In this article, a multi-objective design optimisation using real-coded genetic algorithm is proposed for the Pareto-optimality of grillage system. The objective functions in the optimisation problem measure the design sensitivities in grillage system. The non-linear constrained multi-objective optimisation is very important from the point of view of practical problem solving. Therefore, the real-coded genetic algorithm with multiple genetic operators is proposed to find the optimum grillage system without handling any of the penalty functions. Direct strength calculation defined from the class rules of DNV was applied for structural design of grillage system. The hybrid method (real-coded genetic algorithm including the non-dominated sorting and sharing approaches) performs a marvellous explorability in finding a diverse set of solutions and in converging near the true Pareto-optimal set. The results obtained are very encouraging since they show that we can produce an important portion of the Pareto-front at a very low computational time frame.

A surrogate-assisted evolution strategy for constrained multi-objective optimization

In the multi-objective optimization design of automotive seats based on Approximation-Based Design Optimization, a single approximation model may not adequately address the requirement of accurately fitting highly nonlinear feature data. For this reason, the Hybrid Approximation Models based on the Multi-Species Approximation Model (HAM-MSAM) is proposed to meet the requirement for high fitting accuracy. Subsequently, this study introduces a HAM-MSAM-based Approximation-Based Global Multi-Objective Optimization Design (ABGMOOD) strategy. This strategy is employed in the multi-objective optimization of the rear seat of a passenger car. HAM-MSAM was constructed from an experimentally validated finite element model and a training set generated through experimental design. The advantages of HAM-MSAM in capturing the highly nonlinear response under seat crash conditions were validated through comparison with hybrid model construction methods reported in existing literature. Finally, the optimization results obtained by the ABGMOOD strategy were compared to those of the classical local multi-objective optimization strategy, demonstrating the substantial advantages of the ABGMOOD optimization scheme in economy and weight reduction. In addition, the safety of the rear seats is slightly lower than that of the local optimization scheme but remains in compliance with regulatory requirements. The final optimized rear seat demonstrates notable improvements in safety, economy, and weight reduction, validating the feasibility of the ABGMOOD strategy and providing valuable insights for similar engineering optimization challenges.

Conventional electromagnetic linear actuators(ELA) have difficulty in balancing efficiency, response and accuracy, so a novel compound ELA is researched, featuring a coaxial integrated design of moving coil and moving iron actuators that switch between co-drive mode and separate drive mode based on service environment. In order to further enhance the comprehensive performance of the actuator and improve its adaptability to extreme working conditions, a multi-objective hierarchical optimization strategy was proposed based on NSGA-II algorithm by analyzing its structural principle, magnetic circuit characteristics and electromagnetic force characteristics. The moving coil actuator, as the main driving component, and the moving iron actuator, as the auxiliary driving component, are selected as the first and second layer optimization objects for multi-objective optimization design. Results indicate that the actuator achieves starting forces of 587.6N and 191.4N under cooperative drive mode and separate drive mode, respectively. Following optimization, these forces increased by approximately 2.2% and 22.4%, while the mover mass was reduced by about 7.2%. The actuator's thrust density, response speed, and structural compactness were effectively enhanced. The proposed approach can be used to solve the complex multi-objective optimization tasks in ELA designing.

There are some problems in the multi-objective optimization: the optimal solution difficult to obtain, low optimization efficiency, etc. To solve the above problems, a new method to adopt the Collaborative Optimization (CO) strategy as well as introduce the Satisfactory Degree (SD) is proposed. In the system-level optimization constraints, the consistency constraints of the sub-system objective is added to make the system level achieve the maximum satisfactory degree while each sub-system can also get their own optimal solution. In the sub-system level optimization, the objective function is defined according to its optimized target. The Genetic Algorithm (GA) and the Sequential Quadratic Programming (NLPQL) are used in the system level optimization, while NLPQL is used in the sub-system optimization. The optimization example of Gear reducer shows that the proposed method is feasible and effective.

Regular monitoring is essential for understanding the ecological and hydrological processes at the basin scale. There are complex trade-offs in the layout and optimization of more than one ecological environment index monitoring network. This article introduces an optimization method that uses the universal cokriging (UCK) model to establish multiple monitoring target variables in the watershed. After establishing a multivariate linear model, the spatial trend of the target variable is eliminated, and the Linear Model of Coregionalization (LMC) is used to fit the cross-variation function and regression residual variance. The weighted average of the mean regression kriging variance (MRKV) of multiple target variables is used as the objective function. The spatial simulated annealing algorithm (SSA) is used for sampling optimization. The optimized sample is combined with Pareto’s method to make the final optimization sample. This paper takes the establishment of a multi-objective wireless monitoring network for surface temperature, soil moisture, and rainfall in the Jinsha River Basin as the research object. The results show that the multi-objective optimization design based on the UCK model can well show the spatial variability of the target variables. It can also better solve other multi-objective optimization trade-offs such as economic maintenance based on entropy other than non-target monitoring variables. The final result realizes the dual multi-objective site optimization of multi-objective variables and multi-objective benefits.KeywordsSpatial simulated annealingMulti-objectiveRegression krigingEntropySite optimization

Due to the adversarial limitations between the output solutions in the multi-objective optimization stage, the energy efficiency optimization of large pumping stations has the disadvantage of difficult coordination between power consumption and efficiency. Therefore, a multi-objective Bayesian hyperparameter optimization algorithm design for energy efficiency optimization of large pumping stations has been carried out. A multi-objective optimization model for energy efficiency of large pumping stations was constructed by combining the input power of the main motor, transmission energy loss, transformer loss, and energy consumption of auxiliary equipment, with efficiency as the constraint. We extract sequence features that affect pump water demand, and use feature labels as hyperparameters for the multi-objective optimization model of energy efficiency in large pumping stations. We transform the multi-objective optimization problem into a hyperparameter optimization problem, avoiding adversarial output solutions. We iterate the query points collected by EI, so the output model reaches a convergent optimal solution. The test results show that the designed algorithm can improve the pumping efficiency of the pump station while controlling the power consumption of the pump station, achieving collaborative optimization of efficiency.

This paper provides a novel framework for solving multiobjective discrete optimization problems with an arbitrary number of objectives. Our framework represents these problems as network models, in that enumerating the Pareto frontier amounts to solving a multicriteria shortest-path problem in an auxiliary network. We design techniques for exploiting network models in order to accelerate the identification of the Pareto frontier, most notably a number of operations to simplify the network by removing nodes and arcs while preserving the set of nondominated solutions. We show that the proposed framework yields orders-of-magnitude performance improvements over existing state-of-the-art algorithms on five problem classes containing both linear and nonlinear objective functions. Summary of Contribution: Multiobjective optimization has a long history of research with applications in several domains. Our paper provides an alternative modeling and solution approach for multiobjective discrete optimization problems by leveraging graphical structures. Specifically, we encode the decision space of a problem as a layered network and propose graph reduction operators to preserve only solutions whose image are part of the Pareto frontier. The nondominated solutions can then be extracted through shortest-path algorithms on such a network. Numerical results comparing our method with state-of-the-art approaches on several problem classes, including the knapsack, set covering, and the traveling salesperson problem (TSP), suggest orders-of-magnitude runtime speed-ups for exactly enumerating the Pareto frontier, especially when the number of objective functions grows.