Multi-objective Discrete Moth-Flame Optimization for Complex Network Clustering

For multi-objective optimization under condition of complicated objective function, the data processing in the evaluation is sometime tediously long, special algorithm is needed to be adopted. Since the remarkable features of uniform distribution of test points within the test domain and the small number of tests, fully representative of each point, and easy to perform regression analysis, the uniform test design method is hybrid with the “intersection” method for multi-objective optimization to simplify the complicated data process in evaluation first. Furthermore, the "intersection" multi-objective optimization methodology is combined with sequential uniform design so as to get a more precise approximation for solving multi-objective optimization problem, the procedure for searching optimum of the “intersection“ multi-objective optimization methodology with sequential uniform design algorithm is put forward. A multi-objective optimization of linear programming problem with three variables is taken as our example, which involves a maximum for one objective and a minimum for another objective. The result for applying the novel approach to the example indicates the effectiveness of current hybrids.

In this study, we developed an efficient computer-aided design tool for scaling combustor designs. From a limited number of fluid simulations, an original design is scaled while preserving most of the flow properties, using a multi-objective optimization method and deep neural network or kriging surrogate models. The accuracy and robustness of the method were first tested with a simple geometry. Investigations have shown a strong sensitivity of the surrogate models to the sample distribution, which can be reduced using a strategic sampling method. Subsequently, the geometry was scaled down to a factor of two using both surrogate models while preserving most of the flow features.

This study proposes the application of combinatorial multi‐objective optimisation (MOO) in an electrical power distribution system. Conventional electrical power systems do not consider reverse power flow, in which the power flows toward the feeder in the distribution system. However, reverse power flow toward the substation transformer is caused by voltage deviation with high penetration of distributed generators into a distribution system. Consequently, this causes faults in electric devices and may even lead to a massive blackout. To resolve voltage deviation problems, it is necessary to consider some trade‐offs. With this background, this study reveals three points. The first and second contributions regard general engineering research issues such as the definition of a new optimisation problem framework. To solve the problems discussed in this study, a new method of MOO was required. This method of MOO is applied to the power system to minimise voltage deviation while simultaneously minimising the number of required voltage control devices and operation. In addition, a new MOO method to determine the optimal placement of control devices while retaining operation diversity is proposed. Finally, each optimisation method is compared with numerical simulation and the advantages are summarised from the simulation results.

Surrogate-based method can dramatically reduce the number of expensive function evaluations in real-world multi- objective optimization problems (MOP). When the number of objectives is small, using surrogate models combined with expected hypervolume improvement (EHVI) infill sampling criterion (ISC) has been proved to be efficient to provide a set of solutions with good diversity and good proximity to the Pareto front (PF) in aerodynamic shape optimization. However, traditional hypervolume-based infilling strategies use only one kind of ISC to generate one or multiple sample points, the advantages of various kinds of ISC cannot be comprehensively utilized and the parallelization is not easy to implement. This paper proposes a combined multi-point infilling strategy based on Kriging models and develops an efficient global multi-objective constrained optimization method (EGMOCO) to solve multi- objective aerodynamic shape optimization with complex constraints. Multiple sample points are generated by using four ISC considering hypervolume at each iteration and then evaluated in parallel. Firstly, the performance of EGMOCO is compared with that of single criterion EHVI strategy on six benchmarks within the same computational budget to prove its effectiveness, and then EGMOCO is implemented in an aerodynamic shape optimization problem with complex constraints. The result shows that EGMOCO has good performance in balancing local exploitation and global exploration with faster convergence rate and high robustness, the whole PF can be fully explored in limited evaluations and the constraint handling is effective especially for real-world problems with complex and nonlinear constraints, the comprehensive aerodynamic performance of the airfoil is greatly improved. It can be confirmed that Kriging-based multi-objective optimization method combined with multi-point infilling strategy performs better than single infilling criterion EHVI, since different sample infilling criteria can complement with each other, both local exploitation and global exploration can be considered and well balanced.

A new approach in multi-objective evolutionary optimization is decomposition. Decomposition is a basic method in old multi-objective optimization which in evolutionary multiobjective optimization, has not been widely used. Using this method, a multi-objective optimization problem is converted into a number of scalar subproblems, and all the subproblems are simultaneously optimized. In this paper, the performance and efficiency of the algorithm MOEA/D (multi-objective evolutionary algorithm based on decomposition) with the performance of two algorithms NSGA-II and MOPSO (evolution optimization methods based on dominance) for solving constrained portfolio optimization in Tehran Stock Exchange, has been compared. Portfolio return and its risk, has been considered as the optimization objectives and CvaR has been considered as a risk measure. The results indicate the high potential of these algorithms for constrained portfolio optimization. Also, the results indicate that the optimization algorithm based on decomposition has lower computational complexity, and Pareto front is more extensive than the other two methods. (Abstract)

In Continuous Stirred Tank Reactor (CSTR) have Fractional order PID with the nominal order PID controller has been used to Multi-Criteria Decision Making (MCDM) and EMO (Evolutionary Multi-objective Optimization) by adjustment of control parameters like Hybrid methods in Multi objective optimization. But, this Fractional order PID with the nominal PID controller has maximum performance estimation. Proposed research work focused the Flower Pollination Algorithm based on Multi objective optimization with Genetic evaluation and Fractional order PID with the nominal PID controller is provides CSTR results. When a flower is displayed to maximum variations in this practical state, the Genetic evaluation has been used to identify the variations. The FPID (Flower Pollination Integral Derivative) is used for tuning the parameters of a Fractional order PID with the nominal PID controller for each region to improve the multi-criteria decision making. FPID also denoted as Flower Optimization Integral Derivative (FOID). The Genetic evaluation scheduler has been combined with multiple local linear Fractional order PID with the nominal PID controller to check the stability of loop for entire regions with various levels of temperatures. MATLAB results demonstrate that the feasibility of using the proposed Fractional order PID with the nominal PID controller compared than the existing PID controller, and it shows the FOID attained better results.

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.

This paper designs and optimizes a new structure hybrid material B‐pillar assembly. Establish a finite element analysis model for the side impact test of an electric vehicle to verify the model's accuracy. Construct a finite element model of the hybrid material B‐pillar assembly. Combined with the static performance analysis of the B‐pillar, the laminate design of the carbon fiber composite B‐pillar reinforcement plate and optimization. For the outer and inner panels of the B‐pillar, the surrogate model method and the multiobjective optimization method are combined to set 11 design variables and 13 responses. The optimal Latin hypercube design method was used to randomly sample each design variable and fit the radial basis function (RBF) approximate model. The modified second‐generation non‐dominated sorting genetic algorithm (MNSGA‐II) was used to carry out multiobjective optimization of the mixed material B‐pillar assembly, and the multicriteria decision‐making method was used to obtain the optimal solution for the Pareto solution set. A drop weight impact test was performed on the hybrid material B‐pillar assembly. The results showed that the peak collision force of the hybrid material B‐pillar was reduced by 43.7% compared with the original steel B‐pillar, the specific energy absorption was increased by 17.5%, and the side impact safety performance was better. After optimization, the weight of the mixed material B‐column was reduced by 26.44%. The improvement rates of the intrusion amount at P2 and the intrusion speed at P1 were 23.99% and 0.63%, respectively, and the lightweight effect and side impact safety were significantly improved.Highlights The finite element analysis parameters of carbon fiber composite materials were constructed through experiments, and the basis for simulation analysis under various working conditions of the B‐pillar based on performance‐driven optimization was established. A hybrid material B‐pillar design method is proposed, which consists of a variable‐thickness high‐strength steel B‐pillar outer plate, a variable‐thickness high‐strength steel B‐pillar inner plate, and a carbon fiber composite B‐pillar reinforcement plate. The super layer of the carbon fiber composite B‐pillar reinforcement plate was optimized and the laying plan of the carbon fiber composite B‐pillar reinforcement plate was determined. Performance‐driven optimization is adopted to realize the collaboration of multilevel design and optimization methods such as B‐pillar conceptual design, detailed design, and multiobjective optimization. A modified NSGA‐II algorithm was proposed and combined with the multicriteria decision‐making method and the entropy‐weighted gray relation analysis method to determine the Pareto optimal solution and the optimal design scheme for the mixed material B‐pillar.

Renewable energy system optimization of low/zero energy buildings using single-objective and multi-objective optimization methods

As for the elastic layout of the direct-driven electric wheel system, the electromechanical coupling between the electromagnetic excitation of in-wheel driving motor and the weak damping system gives rise to negative vibration issues, which further deteriorate the dynamic performance of the electric vehicle. This paper presents a multi-objective optimization method for active suspension system to solve these issues by developing an integrated in-wheel motor electrical vehicles model. The unbalanced electromagnetic excitation from in-wheel driving motor is investigated by means of analytical and finite element methods. The Pareto solution set of optimal parameters are generated by the multi-objective particle swarm optimization method, and a comparison in vehicle dynamic performances is made to verify the targeted optimization method. The simulation results indicate that the optimized active suspension system attenuates the sensitivity of the vehicle system to electromagnetic excitation with a satisfactory balancing between vehicle ride comfort and stability as well as active suspension utilization.

This paper presents a new multi-objective optimization method, which is inspired from the idea of non-dominated sorting genetic algorithm (NSGA) and genetic quantum algorithm (GQA). The GQA has been tested on well known test beds in single objective optimization and compared with the genetic algorithm (GA) in the lead author’s previous work [22]. This paper aims to apply the idea of GQA to multi-objective optimization (MOO). The developed method is called non-dominated sorting genetic quantum algorithm (NSGQA). The developed method is tested with benchmark problems collected from literature, which have characteristics representing various aspects of a MOO problem. Test results show that NSGQA has better performance on most benchmark problems than currently popular MOO methods such as the NSGA. The integration of GQA with MOO, and the systematic comparison with other MOO methods on benchmark problems, should be of general interest to researchers on MOO and to practitioners using MOO methods in design.

This study presents a new method for aerodynamic optimization of a civil aircraft fuselage. Typically, the fuselage is designed for minimum drag force at cruise conditions, which is essential for determining the flight cost. Evolutionary optimization methods, as a powerful tool, require the accurate computations of flows around several hundreds of candidate geometries, which in turn are too costly and time-consuming. One strategy is to use two-dimensional simulations that reduce the computational cost by at least one order of magnitude. This approach was applied in the current work, and the results were obtained with the aim of minimizing the drag coefficient and maximizing the lift coefficient. A genetic algorithm (GA) was used for shape optimization, and the objective function evaluation was carried out using computational fluid dynamics (CFD). A fuselage shape parameterization method is presented that accounts for important flow features. The 150-seat aircraft model designed in Amirkabir University of Technology was considered as a reference body. In the first step, the optimization is carried out for the single objective of minimizing the drag coefficient. Then, the multiobjective optimization is performed with the simultaneous drag coefficient minimization and lift coefficient maximization. Finally, the two-dimensional (2D) side view of the optimum fuselages is used for generation of the corresponding three-dimensional (3D) geometry. The results show that such 3D optimum fuselage reduced the drag coefficient by about 11% while increasing the lift coefficient by 29% in comparison with the original geometry.

Multi-objective optimization problems can be divided into continuous multi-objective optimization problems and discrete multi-objective optimization problems, and discrete multi-objective optimization is not universal. In practical applications, there are many discrete multi-objective optimization problems. The solution of different problems needs to design different evolutionary multi-objective algorithms according to their specific conditions. The threshold in the traditional multi-objective optimization of wireless network is a preset constant. It has relatively poor performance in the synthesis of images with different noise levels. An adaptive wireless network multi-objective optimization algorithm based on image synthesis is proposed. Based on the maximum inter class variance and maximum peak signal to noise ratio (SNR), an adaptive wireless network multi-objective optimization algorithm is established. The accuracy and noise immunity of image synthesis are also considered. In order to avoid the effect of threshold increase on algorithm efficiency, multi-objective optimization algorithm is introduced into the algorithm. Experiments show that the method proposed in this paper is accurate and robust and has good universality for the synthesis of different noise images.

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

The Robogymnast is a triple link underactuated pendulum that mimics a human gymnast hanging from a horizontal bar. In this paper, two multi-objective optimization methods are developed using invasive weed optimization (IWO). The first method is the weighted criteria method IWO (WCMIWO) and the second method is the fuzzy logic IWO hybrid (FLIWOH). The two optimization methods were used to investigate the optimum diagonal values for the Q matrix of the linear quadratic regulator (LQR) controller that can balance the Robogymnast in an upright configuration. Two LQR controllers were first developed using the parameters obtained from the two optimization methods. The same process was then repeated, but this time with disturbance applied to the Robogymnast states to develop another set of two LQR controllers. The response of the controllers was then tested in different scenarios using simulation and their performance evaluated. The results show that all four controllers are able to balance the Robogymnast with varying accuracies. It has also been observed that the controllers trained with disturbance achieve faster settling time.