Combined Multi-objective Optimization Research Articles

A comprehensive study on physics-based simulation combined multi-objective optimization of capacity decay and voltage loss of Vanadium redox flow battery

This paper proposes physics-based simulation combined multi-objective optimization approach for reduction of both capacity decay and voltage loss of Vanadium redox flow battery. In this approach, firstly, a physics-based-electrochemical model for a single-cell VRFB is developed based on the dynamic plug flow reactor model and is used to obtain capacity decay and voltage loss under various conditions. Simulation studies were then conducted to investigate the effects of electrolyte flow rate and electrode fiber diameter on the VRFB performance. The capacity decay in VRFB relies mainly on the vanadium ions’ variation between two half-cells. The variation in the long-term cycle is fundamentally due to the electrolyte transfer across the membrane. The lower electrolyte flow rate, as well as electrode fiber diameter, can reduce the capacity decay as the electrolyte’s velocity across the membrane decreases. However, the lower electrolyte flow rate and electrode fiber diameter increase the voltage loss considering open circuit voltage loss, activation overpotential, and concentration overpotential. Finally, a novel optimization framework combined with simulation and the meta-heuristic algorithm is introduced to reduce both capacity decay and voltage loss in VRFB simultaneously. The proposed multi-objective method shows a significant reduction of both capacity decay and voltage loss.

Intelligent Planning Modeling and Optimization of UAV Cluster Based on Multi-Objective Optimization Algorithm

As a flight tool integrating carrier and reconnaissance, unmanned aerial vehicles (UAVs) are applied in various fields. In recent years, mission planning and path optimization have become the most important research focuses in the field of UAVs. With the continuous maturity of artificial intelligence technology, various search algorithms have been applied in the field of unmanned aerial vehicles. However, these algorithms have certain defects, which lead to problems, such as large search volume and low efficiency in task planning, and cannot meet the requirements of path planning. The objective optimization algorithm has a good performance in solving optimization problems. In this paper, the intelligent planning model of UAV cluster was established based on multi-objective optimization algorithm, and its path is optimized. In the aspect of modeling, this paper studied and analyzed online task planning, search rules and cluster formation control using an agent-based intelligent modeling method. For mission planning and optimization, it combined multi-objective optimization algorithm to build the model from three aspects of mission allocation, route planning and planning evaluation. The final simulation results showed that the UAV cluster intelligent planning modeling method and path optimization method based on multi-objective optimization algorithm met the requirements of route design and improved the path search efficiency with 2.26% task completion satisfaction.

A combined multi-objective intelligent optimization approach considering techno-economic and reliability factors for hybrid-renewable microgrid systems

This paper proposes a novel optimization strategy for hybrid-renewable energy systems in microgrids. The multi-objective optimization approach is formulated for a PV-wind-diesel-battery hybrid system. Its main objectives are to minimize the levelized cost of energy (LCOE) and loss of power supply probability (LPSP) whilst maximizing the use of renewable energy sources (RES). The proposed optimization strategy combines the Taguchi method with a novel fuzzy decision-maker-based multi-objective optimization algorithm. It implements, a) an energy management strategy to optimize the use of the energy sources, b) a Taguchi method to determine the upper bounds of the model's decision variables, c) a multi-objective moth flame optimization algorithm to optimize the size of the renewable energy sources, and d) a fuzzy decision-making approach to obtain the best Pareto front. The proposed strategy was implemented to optimize the design of a hybrid renewable energy system based on three different scenarios consisting of 10, 15, and 20 residential houses located in Sønderborg, a town in the Region of Southern Denmark. The results of the proposed model in terms of loss of power supply probability and levelized cost of energy for the scenarios I, II, and III are [0.224, 0.754], [0.313, 0.612], and [0.368, 0.547] respectively. In addition, the optimal design of the hybrid renewable-based microgrid system for scenarios I, II, and III are [PV: 32 kW, AD: 4.86, WT: 6], [PV: 36 kW, AD: 4.73, WT: 7], and [PV: 47 kW, AD: 5, WT: 7], respectively. The effectiveness of the proposed multi-objective optimization algorithm (MOMFO) in solving the optimization problem was examined and the results were further compared with those of the Non-Dominated Sorting Algorithm II(NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Social Engineering Optimizer (MOSEO).

Meta-Heuristics Optimization of Mirrors for Gravitational Wave Detectors: Cryogenic Case

In this paper, we explore the behavior of several optimization methods for reducing coating Brownian noise in the mirrors of gravitational wave detectors. We will refer to cryogenic operating temperatures, where the low refractive index material has mechanical losses higher than those of the high refractive index material. This situation is the exact opposite of that which occurs at room temperature, which is already widely known. The optimal design of the dielectric mirror (without a priori assumptions on thicknesses) can be obtained through the combined multi-objective optimization of transmittance and thermal noise. In the following, we apply several multi-objective meta-heuristics to compute the Pareto front related to the optimization problem of dielectric mirror thicknesses made of two materials (binary coatings). This approach gives us more certainty about the structure of the final result. We find strong evidence that all meta-heuristics converge to the same solution. The final result can be interpreted with simple physical considerations, providing useful rules to simplify the thicknesses of the optimization algorithm.

Multi-Objective Decision-Making for Hybrid Renewable Energy Systems for Cities: A Case Study of Xiongan New District in China

The application of renewable energy has become increasingly widespread worldwide because of its advantages of resource abundance and environmental friendliness. However, the deployment of hybrid renewable energy systems (HRESs) varies greatly from city to city due to large differences in economic endurance, social acceptance and renewable energy endowment. Urban policymakers thus face great challenges in promoting local clean renewable energy utilization. To address these issues, this paper proposes a combined multi-objective optimization method, and the specific process of this method is described as follows. The Hybrid Optimization Model for electric energy was first used to examine five different scenarios of renewable energy systems. Then, the Technique for Order Preference by Similarity to an Ideal Solution was applied using eleven comprehensive indicators to determine the best option for the target area using three different weights. To verify the feasibility of this method, Xiongan New District (XND) was selected as an example to illustrate the process of selecting the optimal HRES. The empirical results of simulation tools and multi-objective decision-making show that the Photovoltaic-Diesel-Battery off-grid energy system (option III) and PV-Diesel-Hydrogen-Battery off-grid energy system (option V) are two highly feasible schemes for an HRES in XND. The cost of energy for these two options is 0.203 and 0.209 $/kWh, respectively, and the carbon dioxide emissions are 14,473 t/yr and 345 t/yr, respectively. Our results provide a reference for policymakers in deploying an HRES in the XND area.

A combined multi-objective optimization model for degradation trend prediction of pumped storage unit

Pumped storage unit (PSU) operated under the combination of different condition, which may cause equipment wear, fatigue and fault issues. It is difficult to achieve accurate and stable degradation trend prediction (DTP) considering only either the accuracy or stability. In this paper, a combined multi-objective optimization model is proposed for DTP of PSU. Firstly, the health model of PSU is constructed with back propagation neural network (BPNN) and condition parameters of active power, working head and guide vane opening. Subsequently, the performance degradation index (PDI) is developed to characterize degradation level of PSU. Then, considering high accuracy and strong stability in predicted model, kernel extreme learning machine (KELM) is optimized by multi-objective particle swarm optimization (MOPSO) to achieve DTP. To validate the effectiveness of the proposed model, the monitoring data collected from PSU in China is taken as case studies. The results demonstrate that the proposed model outperforms other comparison models in terms of predicting accuracy and stability.

Sustainable energy planning for cost minimization of autonomous hybrid microgrid using combined multi-objective optimization algorithm

With the development of scattered energy resources in the rural areas of Sarawak (Malaysia), various operational problems due to the unplanned installation of autonomous microgrids become gradually remarkable. To address this concern, the paper proposes an optimal strategy to evaluate the performance of different hybrid microgrid configurations for the Long San Village in Sarawak. A mathematical model is presented for sizing the component of the system to meet the maximum load demand under changing weather conditions and at the lowest possible cost. The developed approach simulates different microgrid models using deterministic and stochastic optimization methods to find the exact dynamic energy price of the selected optimal configuration in the context of system uncertainties. Furthermore, the operational feasibility of the system in terms of reliability and voltage security is studied in addition to economic feasibility with a comparative analysis of the environmental impact. The results show that the optimal configuration with the lowest cost of energy and net present cost can be achieved if the installed solar PV is less than 61 kW with 85 kW h of energy storage and 11 kW of hydro generation, where such system has 55,725 (kg/year) Carbon Dioxide and 330 (kg/year) Nitrogen Oxides. The findings also indicate that the dynamic energy pricing increases to 0.71 $/kWh when the power generation from renewable resources drops to zero. Further, the dynamic analysis shows that in order to reduce the voltage drop during disturbances, it is crucial to carefully install the sources in the buses connected to high energy demand.

Combined multi-objective optimization and robustness analysis framework for building integrated energy system under uncertainty

The optimal design of building integrated energy system is sensitive to the variation of uncertain parameters. For addressing the tradeoff of uncertainty and optimality-robustness, this study proposes a combined multi-objective optimization and robustness analysis framework for optimal design of building integrated energy system. The proposed framework includes two parts. In the optimization part, on the basis of scenario generation for capturing the uncertainties of renewable energy sources and energy demands, two-stage multi-objective stochastic mixed-integer nonlinear programming is conducted to optimize the system‘s economic and environmental objectives. Two decision-making methods are introduced to identify the final optimum solution from the obtained Pareto frontier. In the robustness-analysis part, a combined Monte Carlo simulation and optimization method is implemented to verify the robustness of the optimal solutions. The two parts of the framework are integrated to investigate the case of a hotel in Beijing, China. The results indicate that compared with the stochastic model, the deterministic model underestimates the annual total cost. Achieving economic and environmental optimum is conflicting and needs a trade-off through decision making. Moreover, in the robustness analysis, an acceptable robustness value is identified, considering both the selected objectives and the operation constraints’ probability of failure. The Shannon-entropy-based final optimum solution exhibits the best comprehensive performance, with an annual total cost of $695 × 103/year, an annual carbon emissions of 2100 tons/year, and an 8.81% probability of failure.

V2B/V2G on Energy Cost and Battery Degradation under Different Driving Scenarios, Peak Shaving, and Frequency Regulations

The energy stored in electric vehicles (EVs) would be made available to commercial buildings to actively manage energy consumption and costs in the near future. These concepts known as vehicle-to-building (V2B) and vehicle-to-grid (V2G) technologies have the potential to provide storage capacity to benefit both EV and building owners respectively, by reducing some of the high cost of EVs, buildings’ energy cost, and providing reliable emergency backup services. In this study, we considered a vehicle-to-buildings/grid (V2B/V2G) system simultaneously for peak shaving and frequency regulation via a combined multi-objective optimization strategy which captures battery state of charge (SoC), EV battery degradation, EV driving scenarios, and operational constraints. Under these assumptions, we showed that the electricity usage/bill can be reduced by a difference of 0.1 on a scale of 0 to 1 (with 1 the normalized original electricity cost), and that EV batteries can also achieve superior economic benefits under controlled SoC limits (e.g., when kept between the SoC range of SoCmin > 30% and SoCmax < 90%) and subjected to very restricted charge-discharge battery cycling.

Posture prediction and optimization for a manual assembly operation involving lifting of weights

The present work combines ergonomics with the posture prediction in the assembly process to avoid musculoskeletal issues of human operator. For improved productivity the operator should be in a better work environment and in sound health. The purpose of this paper is to provide a different perspective to avoid ergonomic risk factors in manual assembly. Here, a human is modeled as 20-DOF as modeled in robotic analysis and simulated in a virtual environment. In the present study, two objective cost functions i.e. joint discomfort function and energy expenditure function have been employed for evaluating the optimized posture. For posture prediction, a combined multi-objective optimization (MOO) method is used and the objective cost functions are minimized i.e. less joint discomfort and less energy in MOO method required to do the manual assembly operation and consequently, the results are compared and finally the movements are tested using REBA technique.

A neural network approach to the combined multi-objective optimization of the thermodynamic cycle and the radial inflow turbine for Organic Rankine cycle applications

An optimization model based on the use of Neural Network surrogate models for the multi-objective optimization of small scale Organic Rankine Cycles is presented, which couples the optimal selection of the thermodynamic parameters of the cycle with the main design parameters of In-Flow Radial turbines. The proposed approach proved well suited in the resolution of the highly non-linear constrained optimization problems, typical of the design of energy systems. Indeed the use of a surrogate model allows to adopt gradient based methods that are computationally more efficient and accurate than conventional derivative-free optimization algorithms.The intensive numerical experiments demonstrate that assuming a constant efficiency for the In-Flow Radial turbine leads to an error in the evaluation of the performance of the system of up to 50% and that the optimization approach proposed improves the accuracy of the solution and it reduces the computational time required to reach it by two orders of magnitude. An holistic approach in which the turbine and the thermodynamic cycle are designed simultaneously and the use of multi-objective optimization proved to be essential for the design of Organic Rankine cycles that satisfy both size and performance criteria.

Multi-Objective Cluster Intelligent Algorithms for Railway Door-to-Door Transportation Routing Design

In this study, we aim to develop a system optimization model of Railway Freight Transportation Routing Design (RFTRD) and conduct solution analysis which is based on the improved multi-objective swarm intelligence algorithm. The proposed improved multi-objective swarm intelligence algorithm is applied to solve the combinatorial optimization problem of railway door-to-door freight transportation through design, and provide decision support for railway vehicle door-to-door freight transportation through design. The optimization results shows that, the random multi-neighborhood based multi-objective shuffled frog-leaping algorithm with path relinking (RMN-MOSFLA-PR) can be better applied to solve the combined multi-objective optimization problem, and this proposed improved algorithm can find Pareto frontier through the comparative analysis in the design example of railway door-to-door freight transportation. The frontier can provide support for railway transportation enterprises, arrange the decision-making of the starting and ending stations for multiple shippers, and optimize the use of existing transportation resources, so as to reduce the transportation cost and time of the system.

Portfolio Optimization Using Value-at-Risk: Assessment Using Historical Simulation Method for Energy Stocks

Value at Risk is an important measure of market risk. It gives the maximum amount of loss in a portfolio of assets. Conditional Value-at-Risk covers the amount of losses exceeding Value-at-Risk and does not suffer from the functional problems (Non-Convexity and non-additivity. See Rockafellar and Uryasev (2000)) of VaR. Here VaR is calculated using Historical Simulation and portfolio optimization is done with the help of Genetic Algorithm. This methodology is applied to energy stocks of different countries. Optimization is done to maximize returns and minimize risk separately as well as for combined multi-objective optimization

MULTI-OBJECTIVE OPTIMIZATION OF TURNING PARAMETERS USING THE COMBINED MOORA AND ENTROPY METHOD

Selection of optimum machining parameters in machining operations leads to good functional attributes for the machined components and increased productivity. In this work, machining parameters and nose radius are optimized in turning of EN25 steel with coated carbide tool by the application of combined Multi-Objective Optimization by Ratio Analysis (MOORA) and entropy measurement method. The selected machining parameters are cutting speed, feed rate, depth of cut and nose radius for minimization of surface roughness, micro-hardness and maximization of Material Removal Rate (MRR). Entropy concept has been used to assign the weight criteria of each objective being considered. The optimum combination of machining parameters and nose radius are obtained using normalized assessment values. The results obtained in the analysis are validated and the results based on turning process responses can be effectively improved.

The Combined Multi-objective Optimization Design for a Light Guide Rod

The light guide rod (LGR) has been popularly used for the vehicles, and the automobile lamp industries need mass production to match this trend. This paper aims to develop a systemic way to find the best parameters' combination for the LGR, and the parameters are usually restricted to some levels and random values. In this paper, the LGR example with two optical performances of illuminance flux and uniformity is to be optimized by use of the real-coded genetic algorithm (RGA) and grey relational analysis (GRA). The illuminance flux and uniformity of the best parameters' combination are obtained and compared with the initial set. Comparisons with Taguchi-Grey can improve 5% of gain and comparisons with Pareto genetic algorithm (PaGA) can improve 1.7% of gain. The combined multi-objective optimization can saving 7% time and it is found that the new proposed method has positive gains in performances.

Optimization of smart adhesively bonded single-strap composite joint

In order to reduce the stress concentration and improve the failure strength of adhesively bonded joint structures, a smart adhesively bonded joint structure has been developed by integrating piezoelectric layers into the adherends. For better design guide, a combined theoretical model and multi-objective optimization approach is employed to optimize the smart joint in this study. Firstly, the first-order shear deformation theory is utilized to model and analyze the detailed peel and shear stresses distribution in the adhesive layer. Further, considering the characteristics of the stress distribution, the interface debonding failure criterion is used to set up the objective; optimization variables are considered as the stacking sequence, geometries and applied electric fields of the integrated piezoelectric layer. Thus, with the aid of the Mathematica software, the optimal smart adhesively bonded joint system can be determined. Finally, some detailed examples are analytically solved to show the considerable difference between the preoptimized and optimized smart joint systems, which validate that the developed theoretical model and multi-objective optimization approach can be used to enhance adhesively bonded joint failure strength.

Combining multiobjective optimization and Bayesian model averaging to calibrate forecast ensembles of soil hydraulic models

Most studies in vadose zone hydrology use a single conceptual model for predictive inference and analysis. Focusing on the outcome of a single model is prone to statistical bias and underestimation of uncertainty. In this study, we combine multiobjective optimization and Bayesian model averaging (BMA) to generate forecast ensembles of soil hydraulic models. To illustrate our method, we use observed tensiometric pressure head data at three different depths in a layered vadose zone of volcanic origin in New Zealand. A set of seven different soil hydraulic models is calibrated using a multiobjective formulation with three different objective functions that each measure the mismatch between observed and predicted soil water pressure head at one specific depth. The Pareto solution space corresponding to these three objectives is estimated with AMALGAM and used to generate four different model ensembles. These ensembles are postprocessed with BMA and used for predictive analysis and uncertainty estimation. Our most important conclusions for the vadose zone under consideration are (1) the mean BMA forecast exhibits similar predictive capabilities as the best individual performing soil hydraulic model, (2) the size of the BMA uncertainty ranges increase with increasing depth and dryness in the soil profile, (3) the best performing ensemble corresponds to the compromise (or balanced) solution of the three‐objective Pareto surface, and (4) the combined multiobjective optimization and BMA framework proposed in this paper is very useful to generate forecast ensembles of soil hydraulic models.

Optimization of transverse shear moduli for composite honeycomb cores

Effective transverse shear moduli of composite honeycomb cores are important material properties in analysis and design of sandwich structures. Based on a combined multi-objective optimization and homogenization approach, effective transverse shear moduli for composite honeycomb cores are optimized in this paper. An analytical approach using a two-scale homogenization technique is adopted to predict the effective transverse shear stiffnesses of thin-walled composite honeycomb cores with general configurations, and explicit formulas are given for three typical honeycomb cores consisting of sinusoidal, tubular, and hexagonal geometries. To maximize the performance of transverse shear behavior, a multi-objective method is employed to obtain optimal designs of periodic cellular cores. By assigning equal weighting (or penalty) factors to the transverse shear stiffnesses, the overall shear stiffness parameter is optimized. The optimization problem is solved using a sequential quadratic programming algorithm. The geometric and weighting factor effects on the optimal designs for the three cellular cores considered in this study are evaluated and discussed. An optimization procedure using finite element analysis and a response surface algorithm is also conducted to verify the proposed approach. The present combined homogenization and multi-objective optimization approach can be used to maximize transverse shear material properties of composite honeycomb structures.