Multi-objective Optimization System Research Articles

Multiobjective Optimization System of Extrusion Process Parameters for Targeted Microtubes Based on RSM and NSGA-II

An optimization system of extrusion process parameters was designed to rapidly extrude a microtube with a target diameter and wall thickness for forming medical balloons with personalized size characteristics. Firstly, the double convected pom-pom (DCPP) model was selected for the numerical simulation of the tube extrusion process, and different parameter combinations and their corresponding simulation results were extracted based on orthogonal experiments. Secondly, the nonlinear functional relationships between microtube diameter and wall thickness with extrusion parameters were established based on the response surface model (RSM). Finally, the nondominated sorting genetic algorithm II (NSGA-II) and response surface model (RSM) were mixed to find the optimal parameters of the tube extrusion. The extrusion experiment results, taking three microtubes with different target size characteristics (1, 0.15), (1, 0.2), and (1.5, 0.2) as examples, show that the average error between the actual and predicted values of diameter and wall thickness of three microtubes is 6.53%, where the average diameter error is 7.3%, the wall thickness error is 5.77%, the maximum value is 9%, and the minimum value is only 1.3%.

Multipath Routing in Cloud Computing using Fuzzy based Multi-Objective Optimization System in Autonomous Networks

Intelligent houses and buildings, autonomous automobiles, drones, robots, and other items that are successfully incorporated into daily life are examples of autonomous systems and the Internet of Things (IoT) that have advanced as research areas. Secured data transfer in untrusted cloud applications has been one of the most significant requirements in the cloud in recent times. In order to safeguard user data from unauthorised users, encrypted data is stored on cloud servers. Existing techniques offer either security or efficiency for data transformation. They fail to retain complete security while undergoing significant changes. This research proposes novel technique in multipath routing based energy optimization of autonomous networks. The main goal of this research is to enhance the secure data transmission in cloud computing with network energy optimization. The secure data transmission is carried out using multi-authentication attribute based encryption with multipath routing protocol. Then the network energy has been optimized using multi-objective fuzzy based reinforcement learning. The experimental analysis has been carried out based on secure data transmission and energy optimization of the network. The parameters analysed in terms of scalability of 79%, QoS of 75%, encryption time of 42%, latency of 96%, energy efficiency of 98%, end-end delay of 45%.

Modeling and Characteristic Analysis of Magnetic Coupling Mechanism in Seawater Environment

The electrical conductivity and dielectric constant of seawater are quite different from those of air, so it is necessary to explore the differences between wireless power transmission characteristics in the air and seawater. The equivalent circuit model of the magnetic coupling mechanism is of great significance to the analysis of the transmission characteristics. In this article, a more accurate model analyzing the wireless power transmission in seawater environment is proposed, in which all parameters of the model can be obtained by specific expressions. First, by establishing the electromagnetic field analysis model in the seawater environment, the distribution laws of underwater electric field intensity and magnetic induction intensity are obtained. Then, the equivalent self- and mutual resistances of eddy current loss are introduced based on electromagnetic field model, and the analytical solutions of them are given. The lumped parameter equivalent circuit model of the underwater magnetic coupling mechanism is further derived, and the influencing factors of eddy current loss equivalent resistances are explored, which can provide theoretical guidance for multi-objective system optimization. Finally, an underwater platform was built to verify the accuracy of the equivalent circuit model.

Performance assessment and multi-objective optimization of a geothermal-based tri-generation system in hot Summer Continental Climate: Tianjin case study

Considerable interest in the investigation of energy supply systems that effectively utilize renewable energies is being aroused due to the shortage of fossil energy and serious environmental pollution. Geothermal energy is a considered as a promising alternative to fossil energy due to its advantages of large reserves, good stability, and strong sustainability. This paper developed a geothermal-driven tri-generation system to generate power and supply heating and cooling demand. The tri-generation system is composed of an absorption refrigeration cycle, a heat exchange cycle, and an organic Rankine cycle. The paper begins with an introduction to the thermodynamic, economic, and environment models of the presented system. The effects of the geothermal water parameters on the system performance were originally investigated based on the actual data of a sports center (zero energy building). This building is located in Jiefang South Road, Tianjin, which belong to Hot Summer Continental Climate. In addition, the multi-objective optimization of this system was carried out based on the TOPISIS decision-making method considering economic and environmental criteria. The minimum payback period and maximum carbon emission reduction were chosen as the optimization factors. Finally, the optimal temperature of geothermal water is obtained and the exergy analysis was performed under the optimal conditions. The results indicate that the presented system possesses an optimal geothermal water temperature of 83 °C. Under this temperature, the total thermal efficiency in cooling season and heating season were 41.17 % and 84.93 %, respectively. The exergy efficiency in cooling season and heating season were 47.28 % and 62.23 %, respectively. Our proposed system has equivalent annual operating cost of 12.9 k$/year, with a payback period (PBP) of 2.57 years and carbon emission reduction of 2379 ton/year. The condenser and evaporator need to be improved in terms of large exergy loss rate and low exergy efficiency.

Multiobjective optimization of multisource heating system based on improving diversification and implementation

The multiobjective optimization of the system usually focuses on the optimization of the objective functions while ignoring the influence of decision variables on the implementation of the solution. This paper proposes a new improved optimization method by embedding the decision variable diversification mechanism in the optimization process, adopting the discretization mechanism in the multisource complementary heating model, and improving the search space. The new improved optimization method and the original method have similar performance in obtaining the Pareto front, and the hypervolume of the two algorithms differs by only 1.37% on average. The standard deviations of the decision variables in the optimal solutions obtained by the improved algorithm are increased by 70%, and it has a higher diversity of solutions in the decision space. The equipment capacity obtained by the improved algorithm is discretized, and avoids equipment with lower capacity which is beneficial to construction. In this paper, the optimal implementation solution is obtained through the selection of the objective functions by the overall planners and the construction preference of the solution implementers. In this way, the overall planners' requirements for energy conservation, emission reduction and economy, as well as the solution implementers' choice of implementation solutions can be comprehensively considered. In addition, this paper also obtains another optimal solution for adopting the carbon pricing method.

Design of Multi-Renewable Energy Storage and Management System Using RL-ICSO Based MPPT Scheme for Electric Vehicles

Nowadays, traditional power systems are being developed as an emergence for the use of smart grids that cover the integration of multi-renewable energy sources with power electronics converters. Efforts were made to design power quality controllers for multi-renewable energy systems (photovoltaic (PV), Fuel Cell and Battery) to meet huge energy demands. Though there have been several techniques employed so far, the power quality issue is a major concern. In this paper, a multi-objective optimal energy management system for electric vehicles (EVs) is proposed using a reinforcement learning mechanism. Furthermore, the maximum power point tracking (MPPT)-based Reinforcement Learning-Iterative cuckoo search optimization algorithm (RL-ICSO) along with the Proportional Integral Derivative (PID) controller is incorporated. For this, a renewable energy source is considered as input for eliminating voltage and current harmonics. Similarly, a DC to AC inverter using a Model Predictive Control (MPC) controller-based pulse generation process was carried out to incorporate the power quality compensation of multi-renewable energy microgrid harmonics in three-phase systems. The generated energy is checked for any liabilities by adding a fault in the transmission line and thereby rectifying the fault by means of the Unified Power Quality Controller (UPQC) device. Thus, the fault-rectified power is stored in the grid, and the transmitting power can be used for EV charging purposes. Thus, the energy storage system is effective in charging and storing the needed power for EVs. The performance estimation is carried out by estimating the simulation outcome on Total Harmonic Distortion (THD) values, parameters, load current and voltage. In addition, the performance estimation is employed, and the outcomes attained are represented. The analysis depicts the effectiveness of the power and energy management ability of the proposed approach.

An agent-based blackboard system for multi-objective optimization

Abstract In the field of multi-objective optimization, there are a multitude of algorithms from which to choose. Each algorithm has strengths and weaknesses associated with the mechanics for finding the Pareto front. Recently, researchers have begun to examine how multi-agent environments can be used to help solve multi-objective optimization problems. In this work, we propose a multi-objective optimization algorithm based on a multi-agent blackboard system (MABS). The MABS framework allows for multiple agents to read and write pertinent optimization problem data to a central blackboard agent. Agents can stochastically search the design space, use previously discovered solutions to explore local optima, or update and prune the Pareto front. A centralized blackboard framework allows the optimization problem to be solved in a cohesive manner and permits stopping, restarting, or updating the optimization problem. The MABS framework is tested against three alternative optimization algorithms across a suite of engineering design problems and typically outperforms the other algorithms in discovering the Pareto front. A parallelizability study is performed where we find that the MABS is able to evaluate a set number of designs, which require an evaluation time ranging from 0 to 300 seconds, quicker than a traditional optimization algorithm: this fact becomes more apparent the longer it takes to evaluate a design. To provide context for the benefits provided by MABS, a real-world nuclear engineering design problem is examined. MABS is used to examine the placement of experiments in a nuclear reactor, where we are able to evaluate hundreds of configurations for experimental placement while maintaining a strict set of safety constraints.

Superior performances strategies of different hybrid renewable energy systems configurations with energy storage units

The essential function of a hybrid renewable energy system is to produce an adequate electrical supply to the load demand with low cost. This paper proposes the optimization and operation of the renewable energy power sources for electrification of isolated rural city in Algeria desert. For this purpose, a system composed by PV panel (PV), Wind Turbine (WT), Battery Bank (BB) for storage of the electrical energy and Fuel Cell (FC) with hydrogen tank (H2 Tank) and electrolyzer (Elect) system for storage of the chemical energy is used to fulfill the need of the load. In the present paper we are interested in evolutionary algorithms for solving optimization problem of hybrid configuration of power system. However, a new heuristic algorithm namely whale optimization algorithm (WOA) is used to obtained the best solution of multi-objective optimization system of cost of energy (COE), total net present cost (TNPC) and loss power supply probability (LPSP). Two recent algorithms, particle swarm optimization (PSO) and grey wolf optimizer (GWO) are also implemented in this work. Seven cases studies have been tested for examining the efficient of proposed optimization technique. The suggested whale optimization algorithm, as demonstrated by simulations and comparisons with existing methods, solves the problem of multi-objective optimization of hybrid power system configurations with high accuracy and validity.

Multi-objective risk-based optimal power system operation with renewable energy resources and battery energy storage system: A novel Hybrid Modified Grey Wolf Optimization–Sine Cosine Algorithm approach

This research work proposes a Hybrid Modified Grey Wolf Optimization–Sine Cosine Algorithm for the multi-objective optimal scheduling of hybrid power system taking into consideration the risk factor arising due to the intermittent/uncertain nature of the renewable power generation sources. The hybrid power system is modelled considering the thermal generation units, wind energy system, solar photo voltaic system, electric vehicle and battery energy storage system. The multi-objective optimization problem is proposed based on the simultaneous minimization of the total operating cost and system risk. The conditional value at risk is introduced as the risk index to analyse the system risk due to uncertainties in power deliveries by the renewable energy resources, electric vehicle and battery energy storage system during the scheduling process. The integral contribution of this research work focuses on the establishment an optimal generation schedule based on the combined optimization of the total operating cost and system risk. The simultaneous minimization of the operating cost and the risk index is performed with the multi-objective Hybrid Modified Grey Wolf Optimization–Sine Cosine Algorithm and has been used to develop a Pareto-optimal front. The implementation of the fuzzy min–max technique is opted to fetch the best compromised solution. The standard test systems of IEEE-30 bus and Indian-75 bus system are used to validate the potency of the proposed approach. Comparative analysis has been established to highlight the results obtained with the proposed approach is appreciable than other optimization techniques.

Thermodynamic, Economic Analysis, and Multiobjective Optimization of a Novel Transcritical CO2 Rankine Cycle with a Vortex Tube

The transcritical CO2 (TCO2) Rankine cycle has been receiving more and more attention for the lower irreversible losses and the excellent thermophysical characters of CO2. However, it is hard to condense CO2 to liquid due to its low critical temperature (about 31°C) under ambient conditions. In this paper, a novel transcritical CO2 Rankine cycle called TCO2 is proposed, in which a vortex tube is added to condense CO2 under ambient conditions. A mathematical model is established to analyze the parametric effects on thermodynamic and economic performance based on specific equipment investment cost; the model is verified with experimental data. Nondominated sorting genetic algorithm II is used to achieve multiobjective system optimization for obtaining optimum cycle performance. Parametric analysis results show that an increase turbine inlet temperature and vortex tube outlet pressure can increase exergy efficiency. A decrease in turbine inlet pressure and turbine inlet temperature can reduce the TCO2 cycle equipment investment cost. In addition, multiobjective optimization results indicate that a conflict exists between TCO2 cycle thermodynamic and economic performance based on specific equipment investment cost.

Design Optimization of Turbomachinery by Artificial Neural Networks as a Meta-model

Artificial Neural Networks (ANN) has a potential to be used for making decisions instead of human designers. Well-trained ANN is helpful tools to eliminate the work for making decisions and can be effective tools for putting order the complicated parametric study. The genetic algorithms are one of global design search systems. It is important to provide enough generations for the design search, however the evaluation of each individual costs a lot. ANN is one of the candidates of an alternative model (meta-model) which drastically reduce the number of actual CFD calculations, and a highly reliable database can be constructed by CFD validations. Each time the database is reconstructed, the prediction accuracy of the meta-model increases, enabling efficient design search for the optimal one. This paper introduces the multi-objective optimization system using the ANN assisted genetic algorithms, and optimization of turbomachinery design is performed with reducing the simulation costs.

A multivariable hybrid prediction system of wind power based on outlier test and innovative multi-objective optimization

Wind power forecasting is critical to the safe running of the power grid. However, due to the strong intermittence and instability of wind, reliable forecast of wind power remains a significant difficulty. In this study, a novel multivariable machine learning hybrid prediction system that incorporates data preprocessing, prediction, and multi-objective system optimization is designed to quantify the certainty and uncertainty of wind power. To increase the quality of data input, the data preparation module performs outlier tests based on the correlation between wind power and wind speed, as well as feature extraction, on the original data. In the prediction process, this paper offers an incremental kernel extreme learning machine (IK-elm), the parameters of which are set synchronously by an enhanced multi-objective optimization technique (MOCEHHO) developed in this paper. It overcomes the restrictions of duplicated hidden layer nodes and low learning efficiency caused by classic ELM and successfully maximizes the model's prediction capabilities. The simulation results on four datasets from Turkish wind farms show that the hybrid forecasting system outperforms the benchmark and may be utilized as a useful tool for wind power forecasting.

Multi-Objective Optimization for Industrial Ecology: Design and Optimize Exchange Flows in an Industrial Park

The aim of this project is to find an appropriate mode for green sustainable manufacturing and production. Thus, the concept of this model encourages the development of synergy and leverage of resource networks in order to reduce waste and pollution, and to share resource efficiently. To the best of our knowledge, there is currently no other general mathematical model for designing and optimizing exchange material/energy flows in an industrial park. The purpose of this work is to propose a relative advanced dynamic multi-objective model. Simulations have been performed by using the NIMBUS (Nondifferentiable Interactive Multi-objective Bundle-based optimization System) method. This model can assure a win-win situation for industries and environment.

Multi-objective optimization design of green building envelopes and air conditioning systems for energy conservation and CO2 emission reduction

Configurations of the building envelope and the air conditioning system have a considerable effect on annual building energy efficiency. Taiwan government proposed two green building indices, namely the Envelope Energy Load (ENVLOAD) and the Performance of Air Conditioning System (PACS), as energy performance models of building envelopes and air conditioning systems. Relatively few studies, however, have been mainly conducted, using multiobjective optimization design for building envelope and air condition systems, with a goal to treat both the building energy systems and the envelope. Consequently, this study is to make contribution to this related filed by proposing a multi-objective optimal building envelope and air conditioning system energy performance decision model (MOBELM).This model was used to design the optimal configurations of building envelopes and air conditioning systems based on the ENVLOAD and PACS constrictions and the two conflicting objectives. The first refers to the minimal construction cost when the building envelope and air condition system are built. The second is concerned with the minimal CO2 emission when the envelope materials are manufactured and used to form the building envelope. In the case study, the optimized configurations that dominated the original unoptimized design could validate the MOBELM.

A new synthetic metamodel methodology for liquid‐propellant engine's cooling system optimization

AbstractThe present paper strives for optimization of the cooling system of a liquid‐propellant engine (LPE). To this end, the new synthetic metamodel methodology utilizing the design of experiment method and the response surface method was developed and implemented as two effective means of designing, analyzing, and optimizing. The input variables, constraints, objective functions, and their surfaces were identified. Hence, the design and development strategy of combustion chamber and nozzle was clarified, and 64 different experiments were carried out on the RD‐161 propulsion system, of which 47 experiments were approved and compatible with the problem constraints. This engine used all three modes of cooling: the radiation cooling, the regenerative cooling, and the film cooling. The response surface curves were drawn and the related objective function equations were obtained. The analysis of variance results indicate that the developed synthetic model is capable to predict the responses adequately within the limits of input parameters. The three‐dimensional response surface curves and contour plots have been developed to find out the combined effects of input parameters on responses. In addition, the precision of the models was assessed and the output was interpreted and analyzed, which showed high accuracy. Therefore, the desirability function analysis has been applied to LPE's cooling system for multiobjective optimization to maximize the total heat transfer and minimize the cooling system pressure loss simultaneously. Finally, confirmatory tests have been conducted with the optimum parametric conditions to validate the optimization techniques. In conclusion, this methodology optimizes the LPE's cooling system, a 2% increase in the total heat transfer, and a 38% decrease in the pressure loss of the cooling system. These values are considerably large for the LPE design.

Multi-objective system optimization method and experimental validation of a centralized squeeze film damper using a cell mapping method considering dynamic constraints

This article presents a multi-objective optimal design method for a squeeze film damper (SFD) with centring spring. The proposed method is able to obtain the optimal design parameters for a flexible rotor system. The objectives are minimizing the amplitude of the disk and transmitted force, and maximizing the SFD effectiveness simultaneously. These objectives generally conflict with each other, and the optimal values of the design parameters, or Pareto set, are obtained using the cell mapping method for different rotor imbalances with dynamic constraints. Three SFDs are designed and manufactured based on the obtained Pareto set, and experiments are conducted. The experimental results correspond with numerical simulation results, and the conflicting nature between objectives is validated in the case study. The proposed multi-objective optimization algorithm offers a novel and effective tool for designing SFDs. Engineers can refer to an optimal solution set with strong robustness against different demands, rather than a single design.

The Spatial Optimization and Evaluation of the Economic, Ecological, and Social Value of Urban Green Space in Shenzhen

Urban green space (UGS) is important in urban systems, as it benefits economic development, ecological conservation, and living conditions. Many studies have evaluated the economic, ecological, and social value of UGS worldwide, and spatial optimization for UGS has been carried out to maximize its value. However, few studies have simultaneously examined these three values of UGS in one optimization system. To fill this gap, this study evaluated the economic value of UGS in terms of promoting housing prices, its ecological value through the relief of high land surface temperature (LST), and its social value through the provision of recreation spaces for residents within a 255 m distance. Subsequently, these three values were set as objectives in a genetic algorithm (GA)-based multi-objective optimization (MOP) system. Shenzhen was taken as the case study area. The results showed that the influencing distance of UGS in Shenzhen for house prices was 345 m, and the influencing distance of UGS for LST was 135 m. Using MOP, the Pareto solutions for increasing UGS were identified and presented. The results indicate that MOP can simultaneously optimize UGS’s economic, ecological, and social value.

Load Distribution Optimization of Multi-Source District Heating System Based on Fuzzy Analytic Hierarchy Process

As part of the energy structure transition, a key focus in district heating systems is the load distribution optimization of multiple heat sources under the specific heating network. A multi-objective optimization approach is discussed in this paper with a goal of achieving complementary advantages among heat sources, and improving the performance of the system in terms of economic cost, energy structure, and environmental benefits. This paper firstly establishes the mechanism model for multi-source district heating systems (MSDHS). Secondly, it proposes a multi-objective optimization system to account for the operation economy, energy structure, and environmental impact for MSDHS. The selected objectives are such selected that they could be justified and used in real sites. The weights of the objective functions are obtained via the fuzzy analytic hierarchy process (FAHP). Finally, this paper solves the optimization problem via particle swarm optimization (PSO) to obtain the optimal load distribution and tests its validity in a real heating system covering an area of 15 million m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The optimized load distribution scheme achieves a coal saving of 1.14%, a natural gas saving of 0.53%, and a cost-saving of $3,270 during a 24-hour pilot operation. This study provides the basis for future optimization enhancement and algorithm development.

Using big data computing framework and parallelized PSO algorithm to construct the reservoir dispatching rule optimization

The paper aims to study how to realize the rational allocation and efficient utilization of water resources among reservoirs and coordinate the balanced optimization of benefit among dispatching objectives under the premise of ensuring flood control safety. A multi-objective optimal dispatching system for reservoirs in Jinsha River basin based on the Spark big data computing framework and parallelized particle swarm optimization (PSO) is proposed. The characteristics of multiple objectives of water resources optimal dispatching system in Jinsha River basin are analyzed. The multiple objectives have been transformed into single objectives, and the solving model of the problem is obtained. Secondly, the parallel algorithm programming model, the PSO algorithm and its parallel strategy for solving optimization problems, and the parallel method of PSO based on Spark big data computing framework are studied. The results show that the research work provides a scientific theoretical basis and a feasible optimization method for the management and dispatch of cascade hydropower stations. Therefore, this study plays a decisive role in promoting the efficient operation of water resources optimal dispatching system and has good reference value for the development and application of big data parallel programming based on Spark platform.

Multi-objective optimization of the flush-type intake duct for a waterjet propulsion system

The intake duct is an important component of waterjet propulsion system and its hydraulic performance is directly connected with the capability of the waterjet pump and even the propulsion performance of the system. In order to optimize the flush-type intake duct, the present paper proposes a multi-objective optimization system including Design of Experiments (DOE), Computational Fluid Dynamics (CFD), three-dimensional parametric design, approximate model, the modified Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and a Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS). The nonuniformity and perpendicularity of outflow, and hydraulic efficiency for the intake duct are treated as the optimization objectives with four geometrical parameters as the design variables. Local sensitivity analyses indicate that the optimization objectives are significantly affected by the inclination angle and slightly affected by the lip vertical distance of the intake duct. During IVR = 0.3–0.8, the outflow quality and hydraulic efficiency of the optimized intake duct are greatly improved. The nonuniformity after optimization decreases by 27.8% and perpendicularity increases by 3.07% at the design condition of IVR = 0.7 and V s = 19.49 m/s. Based on the flow at the outlet plane of intake duct, the pressure distribution after optimization is very uniform and the tangential velocity is small without obvious secondary flow. During the application to a mixed-flow waterjet propulsion system at various navigation speeds, the nonuniformity of the optimized intake duct decreases by 20.4% and perpendicularity increases by 4.11% on average, demonstrating that the optimized intake duct performs a better outflow quality. It is also noted that the non-uniform flow from the intake duct mainly affects pressure distribution on the suction surface of impeller blade and causes different shaft power. • A multi-objective optimization system is proposed by incorporating DOE, CFD, three-dimensional parametric design, approximate model, the modified NSGA-II and TOPSIS. • The three optimization objectives are significantly affected by the inclination angle and slightly affected by the lip vertical distance. • Non-uniform suction flows mainly affect the flow field on the impeller suction surfaces and cause the difference in the shaft power.