The paper deals with the multi-objective optimization problems of laminated composite beam structures. The objective function is to minimize the weight of the whole laminated composite beam and ...maximize the natural frequency. In particular, the simultaneous use of all the design variables such as fiber volume fractions, thickness and fiber orientation angles of layers is conducted, in which the fiber volume fractions are taken as continuous design variables with the constraint on manufacturing process while the thickness and fiber orientation angles are considered as discrete variables. The beam structure is subjected to the constraint in the natural frequency which must be greater than or equal to a predetermined frequency. For free vibration analysis of the structure, the finite element method is used with the two-node Bernoulli-Euler beam element. For solving the multi-objective optimization problem, the nondominated sorting genetic algorithm II (NSGA-II) is employed. The reliability and effectiveness of the proposed approach are demonstrated through three numerical examples by comparing the current results with those of previous studies in the literature.
To increase the energy-absorbing capability of frontal collision management systems and raise vehicle crash safety, foam-filled crash boxes should be optimized. On the basis of a double tubular ...construction, a novel foam-filled crash box with different design is developed. The energy absorption capacity, initial peak force and deformation modes of the original and improved crash boxes were examined using impact models. As opposed to the full-filling design, it is demonstrated that the filling design may utilize less foam while increasing specific energy absorption. The stability of the continuing deformation after the first buckling is determined by the foam filled crash-box combined. For the foam-filled crash box, a better optimized design technique is suggested using Radial Basic Function and Non-dominated Sorting GA II. Compression tests are used to validate the design concept. Therefore, the optimal design technique of the crash box is suitable and practical for the crashworthiness design of crash boxes, taking consideration the combined effect of significant indicators for electric vehicle.
•A fast and systematic multi-objective optimization framework is proposed.•Power density, efficiency and cathode O2 uniformity are optimized simultaneously.•Decision variables used for optimization ...are determined by variance analysis method.•Data-driven surrogate model and multi-optimization algorithm are combined together.
This paper aims to present a fast and systematic optimization approach for proton exchange membrane fuel cell (PEMFC) by combining variance analysis, surrogate models and non-dominated sorting genetic algorithm (NSGA-II). First, a three-dimensional steady-state PEMFC computational fluid dynamics (CFD) model is developed as the base model for optimization. Second, six variables that have significant effect on PEMFC performance are selected from numerious common parameters using variance analysis, reducing the number of decision variables from 11 to 6. Then, three data-driven ensemble learning models are trained as surrogate models to accelerate the fitness values evaluation of the optimization algorithm. Finally, three PEMFC performance indexes, including power density, system efficiency and oxygen distribution uniformity on cathode catalyst layer are optimized simultaneously based on NSGA-II. Using the NSGA-II combined with surrogate models, a set of Pareto solutions is obtained in a short time. The results indicate that PEMFCs with optimized parameters perform better than the base model in terms of all three performance indexes, demonstrating the success of this approach in solving time-consuming multi-optimization problems. This study provides a fast and systematic approach for PEMFC multi-objective optimization and can be a guide for engineering applications.
In order to balance the maximum mixing efficiency and minimum energy consumption of stirred tanks, this study proposes a four-stage optimization framework, integrating all: CFD model, ANN ...data-prediction model, multi-objective optimization model, and multi-criteria decision making model. With the stirred tank reactor as the modeling and simulation context, a data-prediction model, GA-GABP, is developed firstly. Second, an optimization model is established targeting energy consumption, fluid mixing degree, and suspension uniformity: the predictions of GA-GABP are optimized using the NSGA II, and finally, based on the Pareto front, weights are determined using the entropy weighting method, and the TOPSIS algorithm is employed to decide the final optimization scheme. Compared to the base case, the optimized scheme Opt1 shows a 52.49% reduction in energy consumption, a 1.35% increase in fluid mixing degree, and a 72.31% improvement in suspension uniformity. This demonstrates the framework's effectiveness in balancing multiple conflicting objectives.
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•A four-stage optimization framework was proposed.•The key parameters affecting the performance were studied and optimized.•Two-layer genetic algorithm was used to obtain the best hidden layer combination.•Influence weights of key parameters were studied with stochastic forest algorithm.•The optimal structural parameters were obtained.
As the most promising future generation green ship, hybrid energy ship power systems (HESPS) have gradually attracted attention. However, the integration of new energy including wind energy and solar ...energy, raises key challenges in designing a suit optimal dispatch for HESPS under different navigation conditions. Based on this, an optimal dispatch strategy using the improved Non-dominated Sorting Genetic (NSGA-II) algorithm is proposed. Firstly, an integrated HESPS model consisting of diesel power generation system, energy storage system (ESS), wind power generation system (WPGS) and photovoltaic power generation system is established. Based on this, a multi-objective optimization strategy is proposed to reduce the cost and greenhouse gas emissions. Through the design of crossover operator and mutation operator, an improved NSGA-II is developed to find optimal solutions. Finally, three cases are presented to test the performance of proposed optimal dispatch strategy. Compared with traditional NSGA-II and multi-objective particle swarm optimization (MOPSO), the indicator of Hypervolume, Proportion of independent solutions, Generational Distance (GD) and Inverted Generational Distance can be improved at least 0.39%, 0.18%, 1.85% and 15.87%. At the same time, the corresponding cost and energy efficiency operational index (EEOI) of HESPS can be reduced by 13.17% and 17.53%.
•Hybrid energy ship model including DGs, ESS, WPGS, PV etc., is explored.•A novel optimal dispatch strategy using the improved NSGA-II is proposed.•Compared with existing methods, optimization performance can be improved.•EEOI and reduce greenhouse gas emissions can be reduced by 13.17% and 17.53%.
•The Lion Pride Algorithm (LPA) for bi-objective problems was proposed.•LPA is based on the social structure of lion prides and the framework of NSGA-II.•LPA outperformed NSGA-II in the convergence, ...diversity, and speed.•Various optimization problems were used to verify the LPA.
Reservoir operation optimization is very important in water resource development and management. This paper focuses on the bi-objective optimization problems via proposing a novel bi-objective algorithm, called lion pride algorithm (LPA), based on the social structure of lion prides and the framework of NSGA-II. Specifically, LPA first divides the population into lion prides, then classifies the individuals into lion kings and ordinary lions based on the prides, and finally assign better fitness values to these lion kings compared to ordinary lions. Its performance in bi-objective optimization is then tested by the benchmark problems and the reservoir operation problems. Results indicate that: (1) LPA outperforms NSGA-II in convergence and diversity and runs about 2 to 4 times faster than NSGA-II for bi-objective optimization; (2) LPA has the good optimization ability for the complex problems with sharp-peak and long-tail POFs; and (3) in the reservoir operation problems of this paper, the optimization results of LPA weakly dominate 29% to 70% of those of NSGA-II, while the optimization results of NSGA-II only weakly dominate 1% to 22% of those of LPA. This study sheds a new idea for bi-objective optimization.
In this investigation, the hydrothermal performance of Microchannel Heat Sink (MCHS) augmented with microfin is studied under the condition of forced convection. Microfins are used as flow disruptive ...structures which lead to redevelopment of thermal boundary layer and improved mixing of fluid flow. A comparative analysis of MCHS with different fin configurations (conical, circular, square and triangular) is conducted to find the best thermal performing fin shape. The comparison revealed that the conical fin configuration exhibits significantly higher thermal performance (TP). The present study also obtained the optimum design parameters of conical fins using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), as it significantly impacts the thermal performance. An ANN framework is developed as a surrogate model to accelerate optimization procedure. A Pareto front is obtained with 50 populations of 5 design variables (C1, C2, p, h1 and h2). The optimization results show that a slight offset position from an inline fin arrangement, with maximum fin height, offers the highest overall TP value of 1.88 at Reynolds number 100.
•Micro Channel Heat Sink (MCHS) with different microfin structures under forced convection is investigated numerically.•Conical micro fins create transverse and longitudinal disturbance leading to superlative heat transfer.•An ANN assisted NSGA II optimization technique is used to minimize the Po number and maximize the Nu number.•A Pareto front is obtained with 50 populations of 5 design variables.•Significant improvement is observed for MCHS with maximum fin height and slight offset in the inline fin arrangement.
This paper provides an extensive review of the popular multi-objective optimization algorithm NSGA-II for selected combinatorial optimization problems viz. assignment problem, allocation problem, ...travelling salesman problem, vehicle routing problem, scheduling problem, and knapsack problem. It is identified that based on the manner in which NSGA-II has been implemented for solving the aforementioned group of problems, there can be three categories: Conventional NSGA-II, where the authors have implemented the basic version of NSGA-II, without making any changes in the operators; the second one is Modified NSGA-II, where the researchers have implemented NSGA-II after making some changes into it and finally, Hybrid NSGA-II variants, where the researchers have hybridized the conventional and modified NSGA-II with some other technique. The article analyses the modifications in NSGA-II and also discusses the various performance assessment techniques used by the researchers, i.e., test instances, performance metrics, statistical tests, case studies, benchmarking with other state-of-the-art algorithms. Additionally, the paper also provides a brief bibliometric analysis based on the work done in this study.
•Exergy efficiency with consideration for the quantity and quality of energy is taken as an index of energy utilization.•A collaborative planning model is proposed on energy structure selection and ...equipment capacity optimization configuration.•The solution methods used in this model include Matter-element information theory, Frequent Pattern-growth algorithm, and the tabu search algorithm embedded in the multi-objective genetic algorithm.•The comparison between the three schemes verifies the effectiveness of the model.
This paper introduces the exergy efficiency that takes into account both the quantity and quality of energy, and constructs a bi-level planning optimization model of the regional integrated energy system. Factors such as equipment exergy efficiency are considered by the upper-level planning model. Through matter-element information theory and Frequent Patterm-growth algorithm, the energy structure of the regional integrated energy system planning is determined by quantitative means. The lower-level planning model is intended for economy and exergy efficiency, and the tabu search algorithm is embedded in the solving algorithm of the multi-objective genetic algorithm for solving, determining the capacity of each equipment in the energy structure. In the case study, three comparison schemes are used to verify the proposed model and method through the regional integrated energy system in a resort town in northern China. Among them, Scheme 1 is devised for economy, Scheme 2 is proposed for economy and energy efficiency, and Scheme 3 is intended for economy and exergy efficiency. The results show that compared with the Scheme 1 and Scheme 2, the system exergy efficiency of Scheme 3 corresponding to this model has increased by 17.34% and 11.17%, respectively. Despite an increase of 10.61% in total annualized cost in Scheme 3 compared to Scheme 1, there is a decrease of 6.37% compared to Scheme 2. Despite the conflict in improving exergy efficiency and economy, there is still a balance to draw in the proposed model in this paper.
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