Genetic Algorithm Based Optimization Model Research Articles

Genetic Algorithm-based Optimization Model for Emergency Shelters Location Allocation in Public Health Emergency Situations

Deploying emergency resources after a disaster is crucial for reducing the event’s impact. A multi-objective (MO) resource allocation (RA) method is developed to achieve efficient distribution of relief items and the best choice of transportation routes, taking into account the variable and persistent nature of rescue operations. The MO Cellular Genetical Approach (MOC-GA) addresses the concept by incorporating auxiliary populations and neighborhood architecture into the cellular automaton. The comparative experiment conclusively demonstrates that MOC-GA performs favorably compared to Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), and Dragon Fly Optimisation (DFO) in several metrics such as the Pareto Front (PF), hypervolume, mean objective function value, and PF ratios. The findings demonstrate that MOC-GA effectively addresses the MO dynamical emergency RA approach, offering decision-makers a more comprehensive range of superior and diversified candidate rescue strategies than alternative methods. This research analyzes the existing rescue methods and proposes a theoretical rescue strategy to aid decision-makers in making scientifically informed decisions.

Optimal selection of cost-effective biological runoff management scenarios at watershed scale using SWAT-GA tool

Study regionStudy region: Hablehrood River (HR) watershed in the northern part of the Iranian Central Plateau. Study focusFinding the most economical management scenarios for implementing biological best management practices (BMPs) in the HR watershed is one way to solve the runoff issue in the study area. As, the best types, quantities, and locations of arid and semi-arid rangeland biological BMPs have not yet been identified or determined using the soil and water assessment tool (SWAT) or genetic algorithm (GA) to maximize runoff reductions at the lowest possible cost in arid and semi-arid rangelands, therefore, to estimate runoff and best identify biological management scenarios at the watershed scale in the current study, the SWAT and a GA model have been used. After determining the hydrological response units (HRUs), eight biological watershed management scenarios were identified through a combination of biological management activities. To determine the best management practice with the highest runoff reduction and the lowest possible cost, optimization was performed by GA based optimization model. New hydrological insights for the regionThe simulation results of this study show that all the developed biological BMPs had a significant effect on the surface runoff rate reduction and reduced the surface runoff from 4.4% to 8.2%. The fifth scenario including seeding using machine (SUM) and seeding without machine (SWM) biological activities as optimal and the best scenario with weighted coefficients of 0.95 and 0.05 for runoff reduction and cost, respectively, was determined to be the optimum practice. The results further indicate that runoff would be decreased by 76.4 million cubic meters compared base scenario runoff volume, at a cost of 469.8 billion IR Rials. The lowest volume of surface runoff, however, was related to scenario number eight, while it had the highest cost (1114.3 billion IR Rials) among all scenarios. Using optimization methods, decision-makers and managers may pinpoint the best biological BMP scenarios in terms of kinds, locations, and amounts that might reduce runoff rates as much as possible at the lowest possible cost.

Genetic Algorithm Based Optimization Model for Time-Cost Trade-Off for Construction Project

Abstract: The main cause of gradual development of project management is the necessity of controlling and optimizing the construction project’s objectives. In planning phase of construction project management, some objectives of proposed project are required to be set as per stakeholder’s perspective. Time and cost are of paramount importance objectives of construction project, which vary due to variation in the resource utilization amount. High-cost resources and advance technologies reduce the project time but make the project cost higher. While low-cost resources and traditional technologies give lower project cost but increase the project time. Basically, a construction project is said to be successful if it is completed in minimum possible time and cost. Therefore, the two fundamental goals of any building project are to do it as quickly and inexpensively as possible. The development of time-cost trade-off models has received a lot of focus. However, in addition to a wide range of approaches for time-cost trade-off (TCT) models, this work offers a TCT model based on a genetic algorithm. This model was created in a way that makes it easier to find the best approaches to complete the project on time and for the lowest possible cost. The applicability of proposed TCT model is demonstrated through solving two practically existing construction project. Outcomes of proposed model were found satisfactory based on statistical analysis.

Derivation of unit hydrograph using genetic algorithm-based optimization model

Derivation of unit hydrograph using genetic algorithm-based optimization model

Real data-driven occupant-behavior optimization for reduced energy consumption and improved comfort

A significant amount of energy can be saved through improving occupant behavior. However, implementing energy-saving behavioral changes requires careful consideration based on real-life data to avoid sacrificing comfort. Towards addressing this need, this paper proposes a real data-driven method to assess the potential of occupant-behavior improvements in simultaneously reducing energy consumption and enhancing comfort. The proposed method consists of two main components: (1) machine learning-based occupant-behavior-sensitive models for real data-driven prediction of cooling and lighting energy consumption and thermal and visual occupant comfort; and (2) a genetic algorithm-based optimization model, which uses the machine-learning models to compute the energy consumption and occupant comfort and accordingly optimizes occupant behavior for reduced energy consumption and improved comfort. The proposed method was tested on real data collected from an office building. The experimental results showed potential behavioral energy savings in the range of 11–22%, with a significant improvement in occupant comfort.

Configuration optimization of bionic piezoelectric hair sensor for acoustic/tactile detection

Specialized sensory hairs are important biological sensors for arthropods to detect and recognize environmental conditions including acoustic, pressure and airflow signals. However, the present design methodology of such biomimic micro devices are mainly depending on shape mimicking, which greatly restricts their performance. In this paper, a novel genetic algorithm based optimization model for design of piezoelectric functional hair is developed for improving its acoustic pressure or tactile sensitivity. Furthermore, the sensing mechanism of axially polarized piezoelectric hair is explored and the main influencing factors on sensitivity including hair configuration and axial strain distribution are determined. Then, a series of optimized hair configurations are obtained in a specific frequency band from 1 Hz to 500 Hz, whose average sensitivity of 2.21 × 10−3 V Pa−1 is 10 times greater than that of the straight hair of 2.15 × 10−4 V Pa−1 with the same size. For tactile load detection, the output voltage of the optimized hair is about 1.5 times as much as that of the straight hair. The obtained hairs are similar with the spider’s trichobothria and tactile hair, which presents an explanation of biological hairs sensitive to dynamic and static loads.

Capacity-Constrained Bus Bridging Optimization Framework

Urban rail systems frequently suffer from unexpected service disruptions, which can result in severe delays and user dissatisfaction. “Bus bridging” is the strategy most commonly applied in responding to rail service interruptions in North America and Europe. Buses are pulled from regular routes and dispatched to serve as shuttles along the disrupted rail segment until regular train service is restored. In determining the required number of buses and source routes, most transit agencies rely on ad hoc approaches based on operational experience and constraints, which do not necessarily alleviate the extensive delays and queue build-ups at affected stations, nor do they minimize system-wide impacts in an optimal manner. This paper proposes a genetic algorithm-based optimization model to determine the optimal number of shuttle buses and route allocation to minimize overall subway- and bus rider delay for any given rail disruption incident. The generated optimal solutions were sensitive to bus-bay capacity constraints along the shuttle service corridor of any given disrupted subway segment, utilizing methods found in the Transit Capacity and Quality of Service Manual. The model was used in an analysis of real-world incident data obtained from the Toronto Transit Commission and supplemented by other passenger and travel time data. The bus bridging toolkit showed strong potential to produce efficient shuttle response plans that reduced the transit user delays by more than 50% while ensuring minimum queue formation at disrupted stations and maximizing the utilization of shuttle buses.

Prediction and optimization of oscillating wave surge converter using machine learning techniques

With the increase in global environmental problems and the energy crisis, the oscillating wave surge converter has been extensively studied in the past decades owing to its simple geometry and direct energy capture mechanism. However, systematic optimization of this converter is yet to be achieved. Consequently, in this study, a scaled oscillating wave surge converter under regular waves is numerically investigated using the smoothed particle hydrodynamics method, which is validated against experimental data. With the random changes in nine typical design parameters (i.e., the wave period, wave height, water depth, width of bottom border of the flap, width of top border, flap height, hinge height, flap density, and damping of the power take-off system), a total of 379 cases are generated and simulated. Subsequently, the capture factors corresponding to each case are calculated to quantitatively describe the energy conversion efficiency. With the design parameter combinations as input and the capture factors as output, a radial basis function neural network is trained as the prediction model of capture factors of the converters, which performs satisfactorily. Finally, this prediction model is used with the genetic algorithm to optimize the converters corresponding to different wave periods, wave heights, and water depths. By interpolating the open-source optimization results, a converter with high performance can be easily designed. The optimization method used in this study includes a radial basis function neural network based prediction model and genetic algorithm based optimization model, which can not only optimize oscillating wave surge converters but also has the potential to solve other scientific and technical optimization problems.

Optimizing irrigation and drainage by considering agricultural hydrological process in arid farmland with shallow groundwater

Agricultural production in arid and semi-arid area faces a global problem of water resources shortage and land salinization. Irrigation and drainage are the important measure to enhance crop yield and control soil salinity. Generally, at field scale only irrigation is optimized to pursue the higher water use efficiency and crop yield, while drainage is difficult to optimize owing to controlled by dynamic groundwater levels. Here, we develop a new collaborative optimization model of irrigation and drainage to improve irrigation water use efficiency and to control soil salinity. The model is formulated by integrating simulation of physical processes of field water - salt balance and a genetic algorithm-based optimization model. The new model is to search optimized irrigation and drainage strategic decision for enhancing field economic benefit with the condition controlling salinity with limited water resources. Then, a case study on optimally allocating irrigation and drainage water to different growth stages of maize field in the Hetao Irrigation District, arid area of northwest China shows enhanced applicability of the developed model. Five groundwater depth levels (1 m, 1.5 m, 2 m, 2.5 m and 3 m) and five groundwater salinity levels (2 g/L, 2.25 g/L, 2.5 g/L, 2.75 g/L and 3 g/L) are provided to show and compare the solutions of the optimal irrigation and drainage water allocation. Results indicate the developed model can supply reasonable field monthly irrigation and drainage decision with considering field hydrology, especially contribution of groundwater to crop water demand and groundwater role to soil salt accumulation. The contrary relationship between system benefit and irrigation water use efficiency was described successfully by the developed model. And compared with traditional single irrigation optimization model, the developed irrigation-drainage collaborative optimization model can enhance drainage function to keep the optimal groundwater levels and improve the system benefit by 0–8%. Overall, the developed model can provide more applicable irrigation water and drainage strategies to the sustainable development of irrigation agriculture.

A Genetic Algorithm-based Optimization Model for Designing an Efficient, Sustainable Supply Chain Network under Disruption Risks

A Genetic Algorithm-based Optimization Model for Designing an Efficient, Sustainable Supply Chain Network under Disruption Risks

Smart Operation of Electric Vehicles With Four-Quadrant Chargers Considering Uncertainties

Given the expected impact of electric vehicle (EV) charging on power grids, this paper presents a novel two-step approach for the smart operation of EVs with four-quadrant chargers in a primary distribution feeder, accounting for the uncertainties associated with EVs, and considering the perspectives of both the utility and the EV owners. In the first step of the proposed approach, the mean daily feeder peak demand and corresponding hourly feeder control schedules, such as taps and switched capacitor setpoints, considering the bidirectional active and reactive power transactions between EVs and the grid, are determined. A nonparametric bootstrap technique is used, in conjunction with a genetic algorithm-based optimization model, to account for EV uncertainties and discrete variables. In the second step, the maximum possible power that can be given to connected EVs at each node, while providing active and/or reactive power to maintain the peak demand value and corresponding feeder dispatch schedules defined in the first step, is computed every few minutes in a way which is fair to the EVs. The proposed approach is validated using the distribution feeder model of a real primary feeder in Ontario, Canada, considering significant EV penetration levels. The results show that the proposed approach could be implemented in practice to properly operate EVs, satisfying feeder, and peak demand constraints, which would be better than the business-as-usual practice or a popular heuristic method in terms of number of tap operations, system peak demand, and voltage regulation.

Groundwater Pollution Source Identification Using Genetic Algorithm Based Optimization Model

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Offline training for improving online performance of a genetic algorithm based optimization model for hourly multi-reservoir operation

A novel framework, which incorporates implicit stochastic optimization (Monte Carlo method), cluster analysis (machine learning algorithm), and Karhunen-Loeve expansion (dimension reduction technique) is proposed. The framework aims to train a Genetic Algorithm-based optimization model with synthetic and/or historical data) in an offline environment in order to develop a transformed model for the online optimization (i.e., real-time optimization). The primary output from the offline training is a stochastic representation of the decision variables that are constituted by a series of orthogonal functions with undetermined random coefficients. This representation preserves covariance structure of the simulated decisions from the offline training as gains some “knowledge” regarding the search space. Due to this gained “knowledge”, better candidate solutions can be generated and hence, the optimal solutions can be obtained faster. The feasibility of the approach is demonstrated with a case study for optimizing hourly operation of a ten-reservoir system during a two-week period.

A genetic algorithm-based optimization model for pool boiling heat transfer on horizontal rod heaters at isolated bubble regime

A new optimized model which can predict the heat transfer in the nucleate boiling at isolated bubble regime is proposed for pool boiling on a horizontal rod heater. This model is developed based on the results of direct observations of the physical boiling phenomena. Boiling heat flux, wall temperature, bubble departing diameter, bubble generation frequency and bubble nucleation site density have been experimentally measured. Water and ethanol have been used as two different boiling fluids. Heating surface was made by several metals and various degrees of roughness. The mentioned model considers various mechanisms such as latent heat transfer due to micro-layer evaporation, transient conduction due to thermal boundary layer reformation, natural convection, heat transfer due to the sliding bubbles and bubble super-heating. The fractional contributions of individual mentioned heat transfer mechanisms have been calculated by genetic algorithm. The results show that at wall temperature difference more that about 3 K, bubble sliding transient conduction, non-sliding transient conduction, micro-layer evaporation, natural convection, radial forced convection and bubble super-heating have higher to lower fractional contributions respectively. The performance of the new optimized model has been verified by comparison of the existing experimental data.

A Genetic Algorithm-based optimization model for supporting green transportation operations

Green Logistics (GL) has emerged as a trend in the management of the distribution of goods and the collection of end-of-life products. With its focus on maximizing the economic and environmental value by means of recycling and emission control, GL contributes to the sustainable development of industry but also requires a more comprehensive transportation scheme when conducting logistics services. This study is motivated by the practice of delivering and collecting water carboys. In this paper, a Genetic Algorithm-based optimization model (GOM) is proposed for designing a green transportation scheme of economic and environmental cost efficiency in forward and reverse logistics. Two vehicle routing models with simultaneous delivery and pickup (full or partial pickup) are formulated and solved by a Genetic Algorithm. A cost generation engine is designed to perform a comprehensive cost comparison and analysis based on a set of economic and environmental cost factors, so as to examine the impact of the two models and to suggest optimal transportation schemes. The computational experiments show that the overall cost is evidently lower in the full pickup model. Notably, the impact of product cost after recycling and reusing empty carboys on total cost is more significant than the impact of transportation cost and CO2 emission cost. In summary, the proposed GOM is capable of suggesting a guidance for the logistics service providers, who deal with green operations, to adopt a beneficial transportation scheme so as to eventually achieve a low economic and environmental cost.

Integrated Planning of Land Use and Water Allocation on a Watershed Scale Considering Social and Water Quality Issues

AbstractSustainable development in river basins depends on sound management of land use and water allocation policies. Integrated water resources management (IWRM) is considered a path to bring many elements within the development schemes together toward a unified land-water planning and management process. In this study, an integrated water resources management model is developed to connect three groups of decision makers in pollution control, agricultural planning, and water resources allocation with economic, environmental, and social objectives. A genetic algorithm–based optimization model is developed for providing desirable water quality and quantity while maximizing agricultural production in the upstream region, mitigating the unemployment (social) impacts of land use changes, and providing reliable water supply to the downstream region. The upstream region is divided into subbasins, and a fuzzy-based multiobjective optimization model is used to determine the optimal land uses in each subbasin and...

Simulation-Optimization Modeling for Sustainable Groundwater Development: A Moroccan Coastal Aquifer Case Study

A transient simulation model characterizing groundwater flow in the coastal aquifer of Rhis-Nekor was constructed and calibrated. The flow model was then used in conjunction with a genetic algorithm based optimization model to explore the optimal pumping schemes that meet current and future water demands while minimizing the risks for several adverse environmental impacts, such as saltwater intrusion prevention, avoiding excessive drawdown, as well as controlling waterlogging and salinity problems. Modeling results demonstrate the importance of this combined simulation-optimization methodology for solving groundwater management problems associated with the Rhis-Nekor plain.

Optimal redesign of groundwater quality monitoring networks: a case study

Assessment and redesign of water quality monitoring networks is an important task in water quality management. This paper presents a new methodology for optimal redesign of groundwater quality monitoring networks. The measure of transinformation in discrete entropy theory and the transinformation-distance (T-D) curves are used to quantify the efficiency of sampling locations and sampling frequencies in a monitoring network. The existing uncertainties in the T-D curves are taken in to account using the fuzzy set theory. The C-means clustering method is also used to classify the study area to some homogenous zones. The fuzzy T-D curve of the zones is then used in a multi-objective hybrid genetic algorithm-based optimization model. The proposed methodology is utilized for optimal redesign of monitoring network of the Tehran aquifer in the Tehran metropolitan area, Iran.

Design of River Water Quality Monitoring Networks: A Case Study

Karoon River, from Gotvand Dam to Persian Gulf with more than 450 km in length and an annual discharge of 11,891 million cubic meters, is the largest river in Iran. Increasing water withdrawal from and wastewater discharge to the river has endangered the aquatic life of this important ecosystem. Furthermore, the drinking and in-stream water quality standards have been violated in many instances. In this paper, a river water quality monitoring network is designed, including determination of sampling frequencies as well as location of water quality monitoring stations. In this regard, two models are developed. The first model is a Genetic Algorithm-based optimization model and the second one is a combination of Kriging method and Analytical Hierarchy Process. The temporal variation of the concentration of water quality variables along Karoon and Dez Rivers are evaluated and the main water quality indicators are selected. Then, thirty five stations are selected and the application of Entropy Theory in calculating the sampling frequency is demonstrated. The results show the significant value of the proposed methodology in the design of monitoring network.

Optimal Design of a Stable Trapezoidal Channel Section Using Hybrid Optimization Techniques

A cost effective channel section for a specified flow rate, roughness coefficients, longitudinal slope, and various cost parameters can be determined using an optimization technique. However, the derived optimal channel section may not be feasible for construction because of in situ conditions. The local soil conditions may not support the optimal side slope of the channel and if constructed, the slope may fail. It is therefore necessary to also incorporate the criteria for side slope stability in designing an optimal open channel section. In this paper, a new methodology has been developed to design a stable and optimal channel section using hybrid optimization techniques. A genetic algorithm based optimization model is developed initially to determine the factor of safety of a channel slope for given soil parameters. This optimization model is then externally linked with a separate sequential quadratic programming based optimization model to evaluate the parameters of the stable and optimal channel section. Solution for various example problems incorporating different soil parameters are illustrated to demonstrate the applicability of the developed methodology.