Multi-objective Genetic Algorithm Optimization Research Articles

Investigation of Structural Design Optimization of Loaded Cable-Supported Pipe Systems Using Response Surface Method

A Cable-supported pipe system (CSPS) is a composite system of structure involving truss modules and cable to support a pipeline over a large space. The structure is crucial in the transportation of fluid. This paper proposes structural design optimization method using response surface method (RSM) and investigates the results of Multi-Objective Genetic Algorithm and Screening design optimization of the structure. The RSM is implemented using deterministic inputs of the verified experimental results with the aid of genetic aggregation response surface in ANSYS Design Exploration platform. The results of performance response of the loaded CSPS structure considering the uncertainty of the design parameters, including the cross-sectional area of the cable truss, and pipe members were subsequently mined. This revealed that the cable sections and truss modules can effectively and efficiently improve the performance of the structure. The outcome also shows that performance the structure is robust. A set of "Pareto optimum solutions' ' were thus obtained based on the constraints of the design optimization, the yield stress of material properties and limit displacement of the structure. The results illustrate that the proposed optimization method provides a set of Pareto optimum solutions and guide for the robust design of the structure.

Hybrid multi-objective node deployment for energy-coverage problem in mobile underwater wireless sensor networks

Underwater wireless sensor networks have grown considerably in recent years and now contribute substantially to ocean surveillance applications, marine monitoring and target detection. However, the existing deployment solutions struggle to address the deployment of mobile underwater sensor nodes as a stochastic system. The system faces internal and external environment problems that must be addressed for maximum coverage in the deployment region while minimizing energy consumption. In addition, the existing traditional approaches have limitations of improving simultaneously the objective function of network coverage and the dissipated energy in mobility, sensing and redundant coverage. The proposed solution introduced a hybrid adaptive multi-parent crossover genetic algorithm and fuzzy dominance-based decomposition approach by adapting the original non-dominated sorting genetic algorithm II. This study evaluated the solution to substantiate its efficacy, particularly regarding the nodes’ coverage rate, energy consumption and the system’s Pareto optimal metrics and execution time. The results and comparative analysis indicate that the Multi-Objective Optimisation Genetic Algorithm based on Adaptive Multi-Parent Crossover and Fuzzy Dominance (MOGA-AMPazy) is a better solution to the multi-objective sensor node deployment problem, outperforming the non-dominated sorting genetic algorithm II, SPEA2 and MOEA/D algorithms. Moreover, MOGA-AMPazy ensures maximum global convergence and has less computational complexity. Ultimately, the proposed solution enables the decision-maker or mission planners to monitor effectively the region of interest.

Economic, environmental, exergy (3E) analysis and multi-objective genetic algorithm optimization of isopropyl acetate production with hybrid reactive-extractive distillation

Isopropyl acetate (IPAC) is an important fine chemical intermediate with a great demand for industrial production. In view of the high energy consumption in the traditional reactive distillation (RD) process of IPAC production, this study further proposed three enhanced processes of reactive-extractive distillation (RED), reactive-extractive dividing wall column (REDWC) and reactive-extractive distillation coupled pervaporation (REDPV) process. Firstly, the surface charge density distribution between components is calculated by COSMO-SAC model to screen a variety of potential solvents. Combined with the empirical screening method, relative volatility is used to determine the optimal solvent. Then, the conceptual design of RD, RED and REDWC three processes are completed based on reaction kinetics, and the operating conditions of processes are optimized by multi-objective genetic algorithm. Furthermore, REDPV process using pervaporation module instead of solvent recovery column is proposed. Finally, the total annual cost (TAC), gas emissions and exergy efficiency are used to evaluate the proposed process. The results show that compared with RD process, the TAC and gas emission of RED process are reduced by 42.69% and 49.82%. While those of REDWC process are reduced by 45.06% and 52.93%. The thermodynamic efficiency of these two processes is increased by 63.28% and 62.67%. Compared with RED process, REDPV process further reduces the cost by 14.45% and the gas emissions by 34.55%, but obtain the lowest thermodynamic efficiency.

Fractional Order Controller Design for Wind Turbines

According to recent studies, it has been concluded that renewable electricity generation is being requested to replace all other fuels more often. In China and the USA, among renewable electricity sources, wind usage has increased significantly compared to 2020. Given these circumstances, the aim of this study was to develop a suitable speed control method for wind power systems in order to achieve maximum power generation while reducing mechanical loads. Several control strategies have been proposed in the literature, all of which offer a compromise between performance and robustness. The present research developed fractional order PID (FOPID) controllers and proved which would be the most suitable controller to address the challenges that wind turbine systems face. The parameters of the FOPID controllers (KP, KI, KD, λ and µ) were tuned with the help of the following optimization algorithms: a genetic algorithm (GA), a multi-objective genetic algorithm (MOGA) and particle swarm optimization (PSO). The results from these three turning methods were then compared to find the method that offered the best performance and system robustness.

Multi-objective optimization of angles-only navigation and closed-loop guidance control for spacecraft autonomous noncooperative rendezvous

In this paper, the angles-only navigation and closed-loop guidance control for spacecraft autonomous noncooperative rendezvous in geosynchronous Earth orbit (GEO) are studied, and a multi-objective optimization model is established considering various constraints. The optimization objectives include the total flight time, the total fuel cost, and the observability of angles-only navigation. First of all, the angles-only navigation model is established, including relative dynamics and Line-of-Sight (LoS) measurement models. Then, the closed-loop guidance control covariance is analyzed, and the navigation and control covariance analysis models are established. And then, a multi-objective optimization model is established, including the objective functions, optimization variables, constraints, and the mathematical model. After that, a crowding mechanism based on vector space model (VSM) is proposed to improve the fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II, and a VSM-NSGA-II is constructed to solve the proposed multi-objective optimization model. Finally, a set of Pareto optimal solutions are simulated and analyzed by using closed-loop guidance control covariance analysis and Monte Carlo simulation, thereby proving the effectiveness of the multi-objective optimization model proposed in this paper.

Simpson’s rule based accurate mathematical modelling of photovoltaic cell using multi-objective genetic algorithm for hardware verification

The behaviour and effective performance of solar cell is represented by Triple Diode Solar Cell Module (TDSCM) circuit with five parameters for different environmental conditions. The equations described the solar modules behaviour are usually implicit in nature and the parameter extraction was very complexity. From the Photovoltaic (PV) module data sheet, one can identify the four equations applying to single, double, and triple diode parameters. For getting fifth equation researchers have gone with several approximations, which concludes the computation complexity, convergence problem, and low accuracy issues. In the proposed work the fifth equation are framed under the area characteristics curve (V-I & P-V) concept using Simpson’s approximation. To find which PV module is less accuracy and non-linearity consideration for the performance level. Therefore, to overcome these issues the multi-objective Genetic Algorithm (GA) optimization method are prescribed to frame the fifth equation of the Simpson’s rules. This works improved non-linearity performance and gives the high accuracy modelling compare to other single, double diode methods.

Performance Optimization of Surface Plasmon Resonance Imaging Sensor Network Based on the Multi-Objective Optimization Algorithm.

In this work, we report performance optimization of a wireless sensor network (WSN) based on the plain silver surface plasmon resonance imaging (SPRi) sensor. At the sensor node level, we established the refractive index-thickness models for both gold and silver in the sensor and calculated the depth-width ratio (DWR) and penetration depth (PD) values of the sensor of different gold and silver thicknesses by the Jones transfer matrix and Kriging interpolation. We optimized the DWR and PD simultaneously by using the multi-objective optimization genetic algorithm (MOGA). In the following performance optimization of WSN, we simultaneously optimized the transmission success rate and information dimension with the number of nodes and transmission failure rate of the sensor node as variables by the same algorithm. By calculating the information dimension and the transmission success rate of each Pareto optimal solution, we obtained the number of nodes and transmission failure probability of the node available for practical deployment of WSN. The above results indicate that the Pareto optimal solution set obtained from MOGA can help to provide the best solution for the optimization of some certain performance parameters and also assist us in making the trade-off decision in the structure design and network deployment if optimal values of all the performance parameters can be obtained simultaneously.

Optimizing The Transportation of Petroleum Products in A Possible Multi-Level Supply Chain

The goal of many supply chain optimization problems is to minimize the costs of the entire supply chain network. However, since environmental protection is one of the main concerns, the green supply chain network has been seriously considered as a solution to this concern in order to minimize its effects on nature. This article refers to the modeling and solution of a green supply chain network for the transportation of petroleum products in order to reduce the annual costs, considering the environmental effects. In this article, the cost elements of the supply chain such as the transportation costs of each petroleum product, operating costs, the cost of purchasing crude oil products and the fixed costs of building oil centers as well as the components of the environmental effects of the supply chain such as the amount of gas emissions and volatile organic particles produced by transportation options in the supply chain. considered green. Considering these two components (cost and environmental impact), we have proposed a multi-objective supply chain model. In this facility model, oil centers have limited capacity and at each level of the chain, there are several types of transportation options with different costs. To solve the problem, we have used two multi-objective particle swarm optimization algorithms and genetic multi-objective optimization algorithm with non-dominant sorting II with a priority-based decoding to encode the chromosome. Finally, we have used TOPSIS method to compare these two algorithms.

Experimental and numerical investigation on the heat transfer enhancement for Mini-channel heat sinks with tessellated fins

Thermal management continues to be a constructive procedure applied to extend the life span in electronic equipment. In this paper, a novel mini-channel heat sink with tessellated fins is proposed with different arrangements of oblique channels width and slant angle of secondary channels and the heat cooling capacity of the mini-channel heat sink is investigated numerically and experimentally at the Reynolds number (Re) in the range of 573 to 2295. The results indicated that the new mini-channel heat sink with tessellated fins is more conductive to strengthen the heat transfer effect. The simulation results show that with the increase of volume flow rate, the temperature difference and pressure drop of the model bottom plate with tessellated fins decrease obviously. When the oblique channel slant angle is 70°and the inclined flow channel width is 1.8 mm, the heat transfer effect of better quality can be achieved basically. By processing the single-objective and multi-objective genetic algorithm optimization, optimal designs is predicted and analyzed based on the two variables associated to the geometry of the channels. It is found that the optimized model promotes better thermal performance and uniform substrate temperature, i.e., the pressure drop can be reduced by up to 73%, the average Nusselt number can be raised by up to 17%, and the minimal temperature difference of 17 K at Qv = 30 ml/s. To the end, the error calculation proves that the multi-objective optimization results are in good agreement with the simulation results.

Optimal design and analysis of a combined freshwater-power generation system based on integrated solid oxide fuel cell-gas turbine-organic Rankine cycle-multi effect distillation system

Gas turbine output has a very good capability for heat recovery and increases production capacity by heat recovery steam generator and heat recovery vapor generator. Also, gas turbines have good potential for coupling with a solid oxide fuel cell to increase power generation. The present research proposes and evaluates a novel combination of a solid oxide fuel cell and gas turbine system with an organic Rankine cycle and a multi-effect thermal desalination system. Conventional and advanced exergetic, exergoenvironmental and exergoeconomic analyses are performed to better understand the proposed system in view of performance, economic, and environmental impacts. To find the optimal design values, minimize the total exergetic environmental impacts and total exergetic cost rate, and maximize exergetic efficiency, as objective functions, multi-target optimization using the multi-target water cycle algorithm and the multi-target genetic algorithm is used. The analyses are conducted using MATLAB software. Results determine the optimal hybrid system could produce 5000 m3/day of freshwater, with five effects on the MED-TVC. The energy and exergy efficiencies of the suggested hybrid system reached 47.85% and 41.94%, respectively, an increase of 11.6% and 3.6% compared to the coupled gas turbine system and solid oxide fuel cell. Furthermore, by applying the Multi-objective Genetic Algorithm and Multi-objective Water Cycle Algorithm optimization, the overall efficiency of cogeneration is increased by 28% and 27.5%. The total exergetic cost is reduced by 23.12% and 22.46%, and the total exergetic environmental impact is reduced by 20.15% and 19.65%, respectively.

Reduced FV modelling based on CFD database and experimental validation for the thermo-fluid dynamic simulation of flue gases in horizontal fire-tubes

The present work aims at proposing an optimized physical heat transfer correlation in smooth horizontal fire-tubes (applying a one-dimensional reduced Finite Volume Method - FVM), validated over a wide range of operating conditions. An experimental activity is first carried out, in which the temperature and pressure profiles of the flue gases, streaming through pipes while applying hot-water-boiler conditions, are measured. A Computational Fluid Dynamics (CFD) set-up is developed for simulating the thermo-physical behaviour of the flue gases undergoing a wide range of operating conditions (replicating the conditions that these streams experience in the most common industrial boiler applications). The acquired experimental data with a detailed uncertainty analysis are employed for validating the developed CFD results. Subsequently, the reduced one-dimensional FVM physical models for estimating the heat transfer (taking into account the convective and radiative terms) and pressure drops are implemented. The most accurate models are calibrated using the results of the fluid-dynamics simulations. In this procedure, the coefficients of the standard models are optimized, employing a Multi Objective Genetic Algorithm optimization procedure (MOGA). This work demonstrates that the CFD simulations can reproduce the experimental data accurately. Furthermore, the estimations of the FVM model based on the calibrated physical correlations are in good agreement with numerical results, but with a reduced computational cost. Results indicate an improved estimation of tubes outlet temperature with respect to standard correlation (42.4% error reduction), while the estimated outlet pressure shows only a slight increase of the error with respect to the Fang correlation.

New Solar MPPT Control Technique Based on Incremental Conductance and Multi-Objective Genetic Algorithm Optimization

Solar energy is highly dependent on climatic conditions and swings sporadically, making photovoltaic integration into power systems difficult. In order to improve the design of power infrastructure and the successful deployment of decentralized and renewable energy sources, a new paradigm for the energy supply chain is established, leading to the creation of microgrids. Intelligence should be introduced at all levels of the grid, and it should act over a long period. This article introduces new research tools and methodologies to help utility engineers better understand the grid's potential impact from these new power sources. Therefore, this research proposes a novel PV solar system control method. The strategy is an innovative Maximum Power Point Tracking (MPPT) technology for solar systems. Designing an effective controller capable of lowering Power Stress inside the PV System is critical in this respect. In this research, the Proportional Integral (PI) controller has been utilized in conjunction with a heuristic technique termed Genetic Algorithm (GA) in order to regulate and stabilize the MPP of the power supplied by the PV system. Then the GA technique has been utilized to identify the ideal settings, based on the four performance indices of Integrated Squared Error (ISE), Integrated Absolute Error (IAE), Integrated Time Absolute Error (ITAE), and Integrated Time Squared Error (ITSE) (Kp, Ki). The simulation results show that the PV system can successfully monitor the required MPP.

Single and Multi Objective Optimization for Injection Molding Using Numerical Simulation with Surrogate Models and Genetic Algorithms

Abstract The objective of this study is to develop an integrated computer-aided engineering (CAE) optimization system that can quickly and intelligently determine the optimal process conditions for injection molding. This study employs support vector regression (SVR) to establish the surrogate model based on executions of three-dimensional (3D) simulation for a selected dataset using the latin hypercube sampling (LHS) technique. Once the surrogate model can satisfactorily capture the characteristics of simulations with much less computing resources, a hybrid optimization genetic algorithm (GA) or a multi-objective optimization GA is then used to evaluate the surrogate model to search the global optimal solutions for the single or multiple objectives, respectively. The performance and capabilities of other surrogate modeling approaches, such as polynomial regression (PR) and artificial neural network (ANN), are also investigated in terms of accuracy, robustness, efficiency, and requirements for training samples. Experimental validations and applications of this work for process optimization of a special box mold and a precision optical lens are presented.

An enhance multimodal multiobjective optimization genetic algorithm with special crowding distance for pulmonary hypertension feature selection

Multiobjective optimization assumes a one-to-one mapping between decisions and objective space, however, this is not always the case. When many variables have the same or equivalent objective value, a multimodal multiobjective issue develops in which more than one Pareto Set (PS) maps to the same Pareto Front (PF). Evolutionary computing research into multimodal multiobjective optimization issues has increased (MMOPs). This paper proposed an enhanced multimodal multiobjective genetic algorithm to crack MMOPs using a special crowding distance calculation (ESNSGA-II). This special crowding distance calculation can consider the diversity of the decision space while paying attention to the diversity of the object space. Then, a unique crossover mechanism is established by combining the simulated binary crossover (SBX) method with the capacity of Pareto solutions to generate offspring solutions. The balance between convergence and diversity in both decision space and object space can be guaranteed synchronously, and PS distribution and PF accuracy may both be enhanced. The proposed ESNSGA-II uses the CEC2020 benchmarks MMF1-MMF8 to assess its properties. Comparing the ESNSGA-II to other recently established multimodal multiobjective evolutionary techniques demonstrates that it is capable of efficiently searching numerous PSs of MMOPs. Finally, the suggested ESNSGA-II is used to address a real MMOP problem of pulmonary hypertension detection via arterial blood gas analysis. The statistical analysis reveals that the suggested ESNSGA-II algorithm outperforms other algorithms on this MMOP, and so may be considered a possible tool for pulmonary hypertension.

Optimal Design of Periodic Honeycomb Plate with Unit Cell Structure Based on Genetic Algorithm

Composite honeycomb sandwich plates have been widely used in aerospace, ships, construction bridges, machinery, stationery, and other industries. In order to improve the performance and configuration, taking the structural dynamic characteristics of periodic honeycomb plate as the research object, a structural optimization design method based on natural frequency and stiffness was proposed with the goal of weight reduction. The geometric parameters of the structure system were trained by the multiobjective optimization genetic algorithm (MOGA), and the Pareto optimal solution of variable combination was obtained. This paper presented a decoupling method for complex system optimization design based on dynamic performance from the perspective of basic unit, which could solve the coordination problem of vibration stability and weight reduction in periodic honeycomb plate structure optimization design. It has reference significance for the similar composite material frame base structure design.

Multiobjective Optimization of Laser Polishing of Additively Manufactured Ti-6Al-4V Parts for Minimum Surface Roughness and Heat-Affected Zone.

Metal parts produced by additive manufacturing often require postprocessing to meet the specifications of the final product, which can make the process chain long and complex. Laser post-processes can be a valuable addition to conventional finishing methods. Laser polishing, specifically, is proving to be a great asset in improving the surface quality of parts in a relatively short time. For process development, experimental analysis can be extensive and expensive regarding the time requirement and laboratory facilities, while computational simulations demand the development of numerical models that, once validated, provide valuable tools for parameter optimization. In this work, experiments and simulations are performed based on the design of experiments to assess the effects of the parametric inputs on the resulting surface roughness and heat-affected zone depths. The data obtained are used to create both linear regression and artificial neural network models for each variable. The models with the best performance are then used in a multiobjective genetic algorithm optimization to establish combinations of parameters. The proposed approach successfully identifies an acceptable range of values for the given input parameters (laser power, focal offset, axial feed rate, number of repetitions, and scanning speed) to produce satisfactory values of Ra and HAZ simultaneously.

A simulation-based framework to optimize occupant-centric controls given stochastic occupant behaviour

Occupant-centric control (OCC) strategies represent a novel approach for indoor climate control in which occupancy patterns and occupant preferences are embedded within control sequences. They aim to improve occupant comfort and energy efficiency by learning and predicting occupant behaviour (OB), then optimizing building operations accordingly. Previous studies estimate that OCC can increase energy savings by up to 60% while improving occupant comfort. However, their performance is subject to several factors, including uncertainty due to OB, OCC configurational settings, as well as building design parameters. To this end, testing OCCs and adjusting their configurational settings prior to implementation are critical to ensure optimal performance. Furthermore, identifying building design alternatives that optimize such performance given different occupant preferences is an important step that faces many logistical constraints during field implementations. This paper presents a framework to optimize OCC performance in a simulation environment, which entails coupling synthetic OB with OCCs that learn preferences. The genetic algorithm for multi-objective optimization is then used to identify the configurational settings and design parameters that minimize energy consumption and maximize occupant comfort under various occupant scenarios. To demonstrate the proposed framework, three OCCs were implemented in the building simulation program, EnergyPlus, and executed through Python packages to optimize OCC configurations and design parameters. Results revealed significant improvement of OCC performance when they were customized with the identified optimal configurational settings for different occupant scenarios. It was found that these optimal points could reduce energy consumption by up to 33% while improving occupant comfort by up to 28%, relative to a baseline scenario with non-optimized OCC implementation. The proposed framework aims to improve OCC performance in actual buildings and avoid discomfort issues that may arise during its initial implementation phases.

Snowflake Bionic Flow Channel Design to Optimize the Pressure Drop and Flow Uniform of Proton Exchange Membrane Fuel Cells.

The flow channel design of bipolar plates plays a significant role in the proton exchange membrane fuel cells operation, particularly in thermal and water management. The pursuit of low-pressure drop supply and flow field uniformity in PEM fuel cells has not stopped, resulting in numerous new bipolar plate flow channel designs. The biomimetic leaf vein shape-based flow channel and lung flow channel designs can significantly improve gas supply uniformity and reduce pressure drop. Therefore, we propose a snowflake-shaped bionic channel design by integrating the advantages of the leaf vein shape and lung shape channel. A 3D multi-physics fuel cell model is used to verify the feasibility and superiority of the bionic snowflake design in improving fuel cell performance, especially in reducing the pumping work. The local pressure distribution, oxygen distribution, water distribution, and current density distribution are used to reveal the enhancement mechanism of the new snowflake flow channel. The flow uniformity is further enhanced by using multi-objective (13 target parameters) and multi-parameter (18 independent variables) genetic algorithm optimization. The general goal of this work is to provide a new strategy for the thermal and water management of PEM fuel cells.

A novel multi-objective optimization approach to guarantee quality of service and energy efficiency in a heterogeneous bus fleet system

An efficient public transport system is essential for sustainable city development, as it directly affects people’s welfare. This article addresses the urban public transport timetabling problem with multi-objective evolutionary algorithms, considering multiple vehicle types and respecting the public transport restrictions of local authorities. The conflicting objectives are the minimization of fuel consumption and unsatisfied user demand, which are essential to make transit buses an attractive alternative for users, thus promoting environmentally friendly mobility. The problem was solved with two well-known metaheuristics, namely the non-dominated sorting genetic algorithm-II (NSGA-II) and cellular genetic algorithm for multi-objective optimization (MOCell), and their performance was compared using several metrics. Their parameters were tuned with a thorough study, and several evolutionary operators designed for the problem were considered. The outcomes suggest that a solution using various types of buses can produce diverse dispatching strategies, reducing pollutant emissions and maintaining tolerable ridership losses.

Potential for optimization of energy consumption and costs in saffron production in central Iran through data envelopment analysis and multi‐objective genetic algorithm

AbstractTechnical management of agricultural units plays an important role in increasing the yield, energy efficiency, and decreasing the production costs. Based on that, the present study aimed to evaluate and optimize the technical and economic efficiency in Saffron farms in the 2019–20 cropping season in Iran. Required data were collected from 70 Saffron farms through interviews and questionnaires and were analyzed and compared using two optimization methods including data envelopment analysis (DEA), and multi‐objective genetic algorithm (MOGA). Based on the results related to the energy section, the total energy input was obtained as 43,578 MJ ha−1 before any optimization, while it was determined as 36,033 and 36,910 MJ ha−1 after optimization using DEA, and MOGA, respectively. Also DEA and MOGA methods improved the energy ratio index (ER) (0.002) by 50, and 159%, respectively. Results related to the economic section showed that the total production costs were mitigated from 1260 $ ha−1 to 863.5 and 1069 $ ha−1 after optimization by DEA, and MOGA, respectively. After optimization of revenue (using MOGA method), and total costs (using MOGA, and DEA), the benefit cost ratio index was improved from 1.43 to 2.09 (using DEA), and 3.3 (using MOGA). Consequently, MOGA optimization method showed better results compared to DEA in both energy and economic sections.