Mixed Integer Genetic Algorithm Research Articles

Floating wind farm design using social and environmental constraints

This work proposes a wind farm design methodology, integrating several design variables from different design aspects into an optimization problem, such as turbine power, number of turbines, cable types, wind farm layout and site location. A mixed integer genetic algorithm is employed to combine discrete and continuous design variables to find the design with minimum Levelized Cost of Energy (LCoE). The wind farm LCoE is calculated using a wind farm model, which combines detailed cost functions for different cost elements and engineering wake models to estimate Annual Energy Production. The design is constrained not only by technical aspects such as total power, farm area, but also by environmental and social constraints. These include the wind farm visibility, which is a popular concern among the people living along the coast of nearby wind farms, and availability, implying the compliance with the designated exclusion zones. To demonstrate the sensitivity of design constraints, two floating wind farms are sited and optimized as a case study, using different visibility constraints. The developed design tool within this study aims to bridge the gap between wind farm developers and local authorities who are responsible for permitting and zoning of development areas.

Design of a Compact UWB Filter with Low Insertion Loss and High Selectivity

The design procedure of a novel compact ultra-wideband (UWB) filter with high selectivity and low insertion loss is presented in this paper. The proposed filter consists of a simple planar combline filter at the top of a substrate and a non-intuitive shape of the defected ground structure (DGS) on the other side. The UWB filter is obtained by optimizing the shape of DGS and the combline parameters with a mixed integer genetic algorithm. The mixed integer genetic algorithm optimizes both continuous and binary values simultaneously. The continuous values define the combline parameters while the binary values define the shape of DGS. The detail of the design procedure is presented and validated by fabricating a prototype of the proposed filter. The measured insertion loss of the proposed filter in the mid-band frequency is less than 0.5 dB. The measurement results also show that the proposed UWB filter with a very compact structure has five transmission zeroes at the upper and lower stop bands.

Mixed-integer GA optimization for the tracking control of a formation of small satellites equipped with multi-constrained electric thrusters

A formation mission scenario is considered, consisting of small satellites (Chasers) that must keep a reference trajectory with respect to a larger and non-cooperating Target satellite. The Chaser must perform its formation guidance and control tasks within the framework of its thruster constraints: the case of a low thrust electrical engine is here considered. The constraints of such a thruster are relevant to number and orientation of thrusters, the admissible thrust levels and minimum and maximum ON time. The control algorithm developed to satisfy all these constraints is a model predictive control based on a mixed-integer genetic algorithm optimization, which provides a fast estimate of the necessary scheduled actions. The algorithm takes J2 and drag perturbations into account; at the scope a differential orbital parameters linear relative dynamics model has been used, while the real-world dynamics is represented by a high precision orbital propagator. The developed orbital controller is particularly effective when trajectory keeping is required; different relative configurations, like Helix or Train formations, are taken as benchmark trajectories. The results show promising and robust performance in counteracting differential perturbations, even in the case in which the Target satellite performs scheduled orbital maneuvers which are not timely communicated to the Chaser satellite.

Optimal techno-enviro-economic analysis of a hybrid grid connected tidal-wind-hydrogen energy system

The study deals with the techno-enviro-economic aspects of hydrogen production using polymer electrolyte membrane electrolysers powered by a hybrid grid-connected tidal-wind energy system. System modelling is presented initially. The energy management strategies for hydrogen production are then analysed as rule-based approach and as optimised approach. An objective function to maximise the operating profit under optimal system operation is formulated considering the variable energy costs, capital and maintenance expenditure, and real system constraints. A comprehensive cost analysis of the system is obtained by comparing two different optimisation approaches based on fixed cost and levelised cost factors, respectively. Towards reaching this goal, the use of mixed integer genetic algorithm optimisation is investigated. The operation of electrolyser in terms of its different operating modes, namely stop, running, and stand-by is presented. The dynamic optimisation of an electrolyser capable of working at up to twice its nominal rating for a limited duration is also analysed in the study. The results of the optimisation approach are 41.5% and 47% higher than the rule-based approach in terms of the annualised profit and carbon emission savings, respectively. In addition, the recurrent switching of electrolyser unit operating modes is avoided with the optimisation approach, reducing the associated energy consumption of about 27.2 MWh annually. The proposed model can be used as a generic tool for hydrogen production analysis under different contexts and it is especially applicable in high green energy potential sites with constrained grid facilities.

Optimized fractional-order Butterworth filter design in complex F-plane

This paper introduces a new technique to optimally design the fractional-order Butterworth low-pass filter in the complex F-plane. Design stability is assured by incorporating the critical phase angle as an inequality constraint. The poles of the proposed approximants reside on the unit circle in the stable region of the F-plane. The improved accuracy of the suggested scheme as compared to the recently published literature is demonstrated. A mixed-integer genetic algorithm which considers the parallel combinations of resistors and capacitors for the Valsa network is used to optimize the frequency responses of the fractional-order capacitor emulators as part of the experimental verification using the Sallen–Key filter topology. The total harmonic distortion and spurious-free dynamic range of the practical 1.5th-order Butterwoth filter are measured as 0.13% and 62.18 dBc, respectively; the maximum and mean absolute relative magnitude errors are 0.03929 and 0.02051, respectively.

Optimal DG allocation and sizing in distribution systems with Thevenin based impedance stability index

The applications of Distributed Generation (DG) play a significant role to provide the benefit to conventional distribution systems. However, the sizing and location of these DG units should be taken into consideration to get maximum gain and benefit. If DGs are not located and sized properly, the distribution system can be adversely affected. So, optimal allocation and sizing are needed to avoid instability problems and more costs to increase the efficiency and quality of energy in distribution systems. This paper addresses the optimal sizing and allocation of DGs for power losses, voltage profile and stability improvement, introducing a proposed stability index based on Thevenin impedance in a distribution network. The stability index is calculated by the proposed approach based on Thevenin. Thevenin is obtained based on a two-bus system by reduction of more than two bus systems. The optimal sizing and location of DGs are also obtained by operating DGs under different power factors. The reactive capability of DGs is also shown by simulations with DGs operated under different power factors. The problem of optimal sizing and location of DGs, which is a nonlinear optimization problem with discrete and continuous variables, is solved based on the mixed integer Genetic Algorithm (GA). Also, the proposed approach is verified by using Grey Wolf Optimization (GWO) method. The problem contains multi-objectives with three objective functions: system power losses, voltage deviation and the Thevenin based stability index. The proposed approach has been tested on the IEEE-69 and 118-bus test system to demonstrate its effectiveness. The comparison with some other methods shows that the proposed approach gives better results than other methods while reducing the power losses and improving voltage and system stability in all suggested scenarios by finding the optimal placement and sizing of DG units. It is also shown that the reduction in power loss, voltage and stability improvements are increased more by using the reactive power capability of DGs. The results show that optimum located and sized DGs not only decreases the power loss, but also improve the voltage profile and stability of the system.

Real Coded Mixed Integer Genetic Algorithm for Geometry Optimization of Flight Simulator Mechanism Based on Rotary Stewart Platform

Designing the motion platform for the flight simulator is closely coupled with the particular aircraft’s flight envelope. While in training, the pilot on the motion platform has to experience the same feeling as in the aircraft. That means that flight simulators need to simulate all flight cases and forces acting upon the pilot during flight. Among many existing mechanisms, parallel mechanisms based on the Stewart platform are suitable because they have six degrees of freedom. In this paper, a real coded mixed integer genetic algorithm (RCMIGA) is applied for geometry optimization of the Stewart platform with rotary actuators (6-RUS) to design a mechanism with appropriate physical limitations of workspace and motion performances. The chosen algorithm proved that it can find the best global solution with all imposed constraints. At the same time, the obtained geometry can be manufactured because integer solutions can be mapped to available discrete values. Geometry is defined with a minimum number of parameters that fully define the mechanism with all constraints. These geometric parameters are then optimized to obtain custom-tailored geometry for aircraft flight simulation.

Inventory scaling, life cycle impact assessment and design optimization of distributed energy plants

Distributed energy plants can be relevant to mitigate global energy consumption and carbon emissions. The energy and environmental advantages of these systems can be achieved through an optimal design by considering their life cycle energy consumption and environmental impacts. However, the high technical complexity and the scarcity of life cycle inventory data about distributed energy plants makes their design optimization very challenging. This paper proposes a design optimization methodology for distributed energy plants by accounting for life cycle impacts. The plant comprises renewable energy systems, fossil fuel energy systems and energy storage technologies. The life cycle inventory data are scaled at different sizes by using scaling laws which are obtained by gathering data of commercially available systems of various sizes and from different manufacturers. In this manner, impact scaling curves for the quantification of energy and environmental impacts like fossil cumulative energy demand, global warming potential and abiotic resource depletion are obtained. The validity of the proposed methodology is demonstrated by considering the campus of the University of Parma (Italy) as a case study. A distributed energy plant is optimally designed by using a mixed-integer genetic algorithm to minimize life cycle fossil cumulative energy demand. Moreover, an economic assessment of the optimal configurations is also performed. Compared to a conventional plant, the configuration with combined heat and power systems allows a primary energy saving of about 15% and a reduction of total costs of about 12%, while the configuration with reversible heat pumps is the most expensive.

Optimal Placement of Sensors for Controlling Smart Base Isolation Systems

Smart base isolation composed of a low-damping base isolation and a supplemental magneto-rheological (MR) damper is one of the most effective semi-active control systems to protect the structures against earthquake. In this study, optimal placement of sensors is determined by using a mixed-integer genetic algorithm for best possible performance. The results show significant effect of sensor configuration on the control system performance, revealing that the sensor configuration should be taken as an important factor in design process of smart base isolation. Besides, although optimal determination of sensor placement improves the control system performance, a high number of sensors is needed to measure structure responses. A method using the least number of sensors is proposed in order to reduce the control cost. The results show the effectiveness of this method in achieving near-optimal response and reducing the control cost.

Optimization of a discrete multi-tone visible light communication system using a mixed-integer genetic algorithm

Visible light communications (VLC) are generally considered an important pillar of 5G and beyond 5G networks. In this paper, we show how a VLC system using multiple sub-carrier modulation and in particular, discrete multitone, can be designed using a mixed-integer genetic algorithm. We propose a suitable figure-of-merit that can serve as a fitness function for each candidate design. The estimation of the fitness function is carried out using a rigorous system model incorporating both the linear and the non-linear aspects of the transceiver. The algorithm can be used to identify a suitable set of values for various parameters of the system, including the power allocation at each sub-carrier, the modulation order, the sub-carrier spacing and the clipping level. Our Python implementation is publicly available on the web under an open-source license.

Optimization and performance analysis of a multilayer structure for daytime radiative cooling

Daytime radiative cooling has drawn much attention recently because a target surface can be passively maintained at sub-ambient temperature. In order to implement a daytime radiative cooling device (simply ‘radiative cooler’), strong thermal emission should be focused in the mid-infrared regime (8–13 µm), called ‘atmospheric transparent window’. At the same time, absorption of the solar irradiation should be minimized. In the present study, for optimal performance of daytime radiative cooling, a mixed-integer genetic algorithm was employed to achieve maximal infrared emission as well as minimal solar absorption. The combination of total number of layer, materials, and thickness of each layer in the multilayered radiative cooler were determined through optimization. The optimized multilayer structure exhibited the spectrally-averaged (in the 8–13 µm wavelength range) normal emissivity value of 0.96 and the solar-weighted absorptivity of 0.03. The corresponding net cooling power was found to be 101.0 W/m2, and a sub-ambient cooling temperature of 11.2∘C (i.e., below ambient temperature) was predicted in daytime at air-mas 1.5 condition. Besides, the mechanism of enhanced emission in the infrared region and suppressed absorption in the solar spectrum were thoroughly investigated. We also derived the expected performance of the optimized radiative cooler for various conditions of the environmental parameters, such as convection heat transfer coefficient, ambient temperature, and precipitable water level.

Joint optimization of safety barriers for enhancing business continuity of nuclear power plants against steam generator tube ruptures accidents

In nuclear power plants (NPPs), different types of safety barriers are designed to ensure the safe and continuous operation of the NPP against disruptive events. These safety barriers, although designed to operate in different phases of the accidents evolution, are often optimized separately, without considering their collective effects on preventing disruptions and quickly recovering from the disruptions. This paper develops a joint optimization model for synthetically optimizing safety barriers of different natures, including prevention, mitigation, emergency and recovery barriers to enhance the business continuity of the NPP, considering the threat of steam generator tube rupture (SGTR) accidents. The joint optimization is guided by a business continuity metric called expected business continuity value (EBCV). A physics-of-failure model is developed to describe the crack growth process of the steam generator tube and to model the effect of the prevention barriers, i.e., periodical inspection of the crack length. An event tree model is developed to describe the evolution of the SGTR-initiated accident and to model the effect of the mitigation and emergency barriers. Recovery measures are also considered via a widely-used logarithmic function model. A mixed-integer genetic algorithm (MIGA) is used to obtain optimal solutions of the joint optimization model. The results show that the developed joint optimization model can achieve better performance in terms of business continuity, compared to the conventional methods that optimize the safety barriers separately.

Parallel Simulation and Cloud Computing Can Optimize Large-Scale Field Development

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 191728, “Large-Scale Field Development Optimization Using High-Performance Parallel Simulation and Cloud Computing Technology,” by Shusei Tanaka, SPE, Zhenzhen Wang, SPE, Kaveh Dehghani, SPE, Jincong He, SPE, Baskar Velusamy, and Xian-Huan Wen, SPE, Chevron, prepared for the 2018 SPE Annual Technology Conference and Exhibition, Dallas, 24–26 September. The paper has not been peer reviewed. Field-development optimization is challenging because of the large number of control parameters, model complexity, and subsurface uncertainties. The complete paper discusses a study in which the authors propose a joint field-development and well-control-optimization work flow using high-performance parallel simulation and commercial cloud computing, and demonstrate its application through an•offshore oilfield•development. Introduction Designing a field-development plan to maximize the profitability of the asset involves accounting for many factors. Commonly, a field-development plan follows a staged process in which several alternatives are appraised and the one with the best economics is selected. The selected plan is then optimized while considering schedule and cost. Earth modeling and reservoir simulation are often used to provide probabilistic ranges of oil in place or net recovery for a given design at each phase of the process. This requires effective cross-functional teamwork to achieve quality decisions by relying on knowledge and skills spanning different disciplines. Proposed Work Flow Closed-loop or real-time reservoir management are terms often used to describe field-development optimization. These involve rapid data assimilation to define or update subsurface uncertainties and follow optimization processes to find the optimal combination of control parameters. These processes are not single-step but are executed iteratively during reservoir life. The focus of this paper is to describe the optimization process for maximizing economical values by selecting the best development plan under given ranges of subsurface uncertainties. The work flow proposed by the authors controls topside facilities, the number of wells, their trajectories, the drilling sequence, and completion strategy simultaneously, while considering subsurface uncertainties and constraints. The authors use a next-generation reservoir simulator and commercial cloud computing to explore the possibility of achieving an optimized development scenario within reasonable time and cost constraints. The combination of high-performance simulators and virtually unlimited scalability in the cloud provides opportunities for joint field-development optimization without reducing the number of control parameters, enabling the developed work flow to be applicable to various asset-development scenarios. The proposed work flow was applied to the Olympus field case, which is an optimization benchmarking problem developed by the Netherlands Organization for Applied Scientific Research using a synthetic North Sea-type reservoir. The study objective was to improve the net present value (NPV) after 20 years of operation by controlling the number and location of platforms and the number of injectors and producers as well as their trajectories and drilling sequence. The large number of control parameters and subsurface uncertainties make the optimization process challenging. Three optimization techniques—genetic algorithm (GA), particle swarm optimization (PSO), and ensemble-based optimization (EnOpt)—were tested and their performances compared. Best results, in terms of NPV improvement, were obtained by using the mixed-integer genetic algorithm method.

Sensitivity analysis and optimization of building operations

Operator decisions regarding daily and seasonal scheduling of systems’ availability and setpoints play an important role in building performance. This paper reviews the key operational parameters of 14 office buildings in Ottawa, Canada. The results revealed that over 60% of the air handling units (AHUs) were not turned off outside of normal occupied hours, and most of them did not have an economizer cycle. The indoor temperature setpoints were between 20 and 24 °C. Based on these insights, a building performance simulation (BPS)-based sensitivity analysis was conducted to better understand the energy and comfort performance implications of common operator decisions. Eight operational parameters were studied: AHU start and stop times, seasonal switchover to heating and cooling times, heating and cooling season temperature setpoints, and ventilation rate and mode (i.e., constant or occupancy-based). The results revealed that the AHU start and stop times and the ventilation rate are the most critical operational parameters examined in terms of affecting the energy and comfort performance of the buildings investigated. Subsequently, a mixed-integer genetic algorithm was applied to identify the optimal operational parameters among the set of eight operational parameters investigated for four different heating dominated climate zones, nine different occupancy and three different envelope scenarios. The relationships between the variables of these scenarios and the optimal operational parameters were examined.

Selection of earthquake ground motions for multiple objectives using genetic algorithms

Existing earthquake ground motion (GM) selection methods for the seismic assessment of structural systems focus on spectral compatibility in terms of either only central values or both central values and variability. In this way, important selection criteria related to the seismology of the region, local soil conditions, strong GM intensity and duration as well as the magnitude of scale factors are considered only indirectly by setting them as constraints in the pre-processing phase in the form of permissible ranges. In this study, a novel framework for the optimum selection of earthquake GMs is presented, where the aforementioned criteria are treated explicitly as selection objectives. The framework is based on the principles of multi-objective optimization that is addressed with the aid of the Weighted Sum Method, which supports decision making both in the pre-processing and post-processing phase of the GM selection procedure. The solution of the derived equivalent single-objective optimization problem is performed by the application of a mixed-integer Genetic Algorithm and the effects of its parameters on the efficiency of the selection procedure are investigated. Application of the proposed framework shows that it is able to track GM sets that not only provide excellent spectral matching but they are also able to simultaneously consider more explicitly a set of additional criteria.

Just-in-time learning based soft sensor with variable selection and weighting optimized by evolutionary optimization for quality prediction of nonlinear processes

Just-in-time (JIT) learning based soft sensors have been widely used for predicting product quality variables of nonlinear processes. They dynamically build online local models by selecting the samples most relevant to the query data from a historical database whenever an estimate is requested. However, building high-performance JIT soft sensors remains challenging due to difficulties defining similarity measures and the selection of input variables for facilitating efficient relevant sample selection and model building. In this study, we propose a novel soft sensing framework, referred to as JIT learning with variable selection and weighting (JIT-VSW). In this framework, a mixture weighted similarity (MWS) measure is defined by combining multiple weighted Euclidean distance (WED) based similarity measures. The MWS measure enables variable weighting embedded in WED measures to account for the relevance between input and output variables and facilitates the handling of highly complex process characteristics through the mixture-type similarity measure. Meanwhile, a wrapper optimization approach using evolutional algorithms is proposed for input variable selection. Further, the selection of input variables and the determination of MWS parameters, i.e., weights assigned to input variables and mixture coefficients of WED similarity measures, are formulated as a mixed integer optimization problem and solved simultaneously by using the mixed integer genetic algorithms (MIGA). The effectiveness and superiority of JIT-VSW are verified through three real-world applications.

A Novel Bi-Level Framework for Fitting the Parameters in Cardiac Cellular Models.

Cardiac models constructed from sets of differential equations provide invaluable information about heart mechanism and disorder of both human and animals. As tuning the parameters is a profoundly important step of modeling, this paper presents a novel parametrization technique based on a bilevel framework that benefits from two solution approaches, namely mixed integer genetic algorithm (MIGA) and linear least squares (LLS). In the upper-level optimization step, the action potential (AP) of the model is fitted to the reference AP using MIGA. In the lower-level optimization step, the mismatch between the total current of the model and reference is minimized via a clamp concept-based linearization and LLS solution approach. Notably, the clamp concept can diminish the nonlinearity of the parameter fitting problem. The issue of dependency on initial parameters in the lower-level problem, as well as the sensitivity of model parameters to linearization, are circumvented by MIGA in the upper-level optimization. For evaluation of MIGA-LLS performance, two complex human ventricular models are employed. The results demonstrate that in comparison to the genetic algorithm (GA)-based approach, the proposed framework significantly reduces the average and variation of normalized root-mean-squared error (NRMSE) in terms of the AP and total current in different trials. Variability in the resulting parameter values is considerably decreased as well.

Technical and Economical Assessment of using Static Compensators for Improving Power Quality in Distribution Power Systems

Nowadays, various issues regarding the power quality have been widely considered. Regarding to the progress made in power electronics in recent years, the best way to improve the reliability of reducing voltage deviations, reducing losses, and generally providing high quality to consumers is to use custom power devices (CPDs). Series, parallel, or hybrid devices come from a subset of CPDs such as a dynamic voltage restorer, distribution static compensator, and unified power quality conditioner. In this work, the purpose of place these devices are to achieve various goals of improving power quality and reducing system costs. To achieve these goals, at first, the problem of single-objective optimization for each of the objective functions was solved separately. After determining the optimal value of each of the objective functions, the fuzzy membership functions for each of the objective functions were suitably optimized for each objective function. A mixed integer genetic algorithm was used to find the optimal answer to this multi-objective problem. The simulation results show that the proposed algorithm has worked well to find the optimal solution. The results of multi-objective planning proposed in this study show that with proper planning, it can be done at a low cost and even with a relatively high profit to cost and with the proper place of CPDs, to solve issues related to power quality issues in distribution networks.

A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on Mixed Integer Genetic Algorithm

In this paper, a methodology has been proposed to decrease the solution time of the joint energy-reserve market clearing problem under Payment Cost Minimization (PCM) mechanism. PCM mechanism is recently proposed to replace the available economically-inefficient clearing mechanisms in electricity markets. While the exact methods have been successfully applied to solve the market clearing problem under available mechanisms, their computational efficiency is poor when applied to solve this problem under PCM mechanism due to special characteristics of PCM formulation. The proposed solution methodology uses linear programming along with Mixed Integer Genetic Algorithm (MIGA) to minimize the payment cost. Most of efforts are focused on speeding up the applied optimization technique, since the available frameworks for solving the PCM problem suffer from very slow convergence. Different custom-designed functions have been added to the basic MIGA to decrease the solution time. The results are compared to those obtained using branch and cut technique.

Hybridization of Biogeography-Based

The aim of this paper is to evaluate a hybrid biogeography-based optimization approach based on the hybridization of biogeography-based optimization with differential evolution to solve the optimal power flow problem. The proposed method combines the exploration of differential evolution with the exploitation of biogeography-based optimization effectively to generate the promising candidate solutions. Simulation experiments are carried on standard 26-bus and IEEE 30-bus systems to illustrate the efficacy of the proposed approach. Results demonstrated that the proposed approach converged to promising solutions in terms of quality and convergence rate when compared with the original biogeography-based optimization and other population based optimization techniques like simple genetic algorithm, mixed integer genetic algorithm, particle swarm optimization and craziness based particle swarm optimization.