Evolutionary Genetic Algorithm Research Articles

Design and Optimization of a Permanent Magnet Synchronous Motor for a Two-Dimensional Piston Electro-Hydraulic Pump

A two-dimensional (2D) piston electro-hydraulic pump has been proposed further to enhance the power density of the electro-hydraulic pump. The 2D piston pump, characterized by high power density and a slender shape, is embedded within the stator of the motor in a co-rotor configuration where the piston and the motor’s rotor are in tandem. The intimate design of the hydraulic pump and the motor results in a coupling between the two, with intricate relationships and influences existing between the geometric parameters of the piston pump and the dimensions of the motor’s rotor. Based on the operational requirements and structure of the 2D piston pump, a permanent magnet synchronous motor (PMSM) designed for use with a 2D piston electro-hydraulic pump is developed. This study examines the impact of the motor’s stator iron core geometric parameters on both the electromagnetic and mechanical properties of a PMSM and completes the necessary performance validations. The optimization objectives of the motor are determined through an analysis of the influence of the key parameters of the rotor and stator on torque, torque ripple, and motor loss. A surrogate optimization model is constructed using a metamodel of optimal prognosis (MOP) to optimize the torque, torque ripple, and motor loss. Evolutionary genetic algorithms are utilized to achieve the multi-objective optimization design. A finite element simulation is used to compare the electromagnetic performance of the initial motor and optimal motor. Based on the optimal motor parameters, a 2.5 kW motor prototype is manufactured, and the experimental results validate the feasibility and effectiveness of the motor design and optimization.

Perception, Evolution, and the Explanatory Scope of Scientific Theories

According to the interface theory of perception, our perceptual systems have evolved to provide a species-specific interface to guide adaptive behaviour, and not to provide veridical representations of an observer-independent world. Results of simulations of evolutionary resource games, genetic algorithms, and multiple mathematical theorems have supported and fleshed out this claim in various ways. They indicate that the probability is zero that any perceptual system has been shaped by natural selection to represent the true structure of an observer-independent world. Bagwell (2023) thinks that this argument is self-defeating because it implies that organisms, resources, genes, etc. that are essential constituents of Darwin's theory do not provide an insight into the nature of observerindependent reality. Bagwell's argument relies on a false premise, however — namely, that the mathematical formulation of a scientific theory cannot dethrone its fundamental concepts. More broadly, his argument involves a fundamental misunderstanding of the nature of scientific theories — of their explanatory scope and their necessary limits.

Enhancing skimmed milk powder functionalities through multicriteria optimization of outlet drying air temperature and spraying air pressure

This study investigated the effects of spraying air pressure and outlet air temperature on the physicochemical and functional properties of skimmed milk powder. Experimental design methodology showed that higher outlet air temperatures improved dry matter content, gelation ability, and flowability, but strengthened colour, moisture sensitivity, and favoured protein denaturation. Spraying air pressure primarily influenced particle size, affecting powder rheological properties and colour. This study highlights the importance of controlling drying parameters to optimize powder properties. The use of a genetic evolutionary algorithm in the optimization process allowed the simultaneous improvement of several functionalities of skimmed milk powder. The findings of this study can contribute to the development of enhanced powders with tailored properties for specific industrial applications.

Proximal evolutionary strategy: improving deep reinforcement learning through evolutionary policy optimization

Evolutionary Algorithms (EAs), including Evolutionary Strategies (ES) and Genetic Algorithms (GAs), have been widely accepted as competitive alternatives to Policy Gradient techniques for Deep Reinforcement Learning (DRL). However, they remain eclipsed by cutting-edge DRL algorithms in terms of time efficiency, sample complexity, and learning effectiveness. In this paper, aiming at advancing evolutionary DRL research, we develop an evolutionary policy optimization algorithm with three key technical improvements. First, we design an efficient layer-wise strategy for training DNNs through Covariance Matrix Adaptation Evolutionary Strategies (CMA-ES) in a highly scalable manner. Second, we establish a surrogate model based on proximal performance lower bound for fitness evaluations with low sample complexity. Third, we embed a gradient-based local search technique within the evolutionary policy optimization process to further improve the learning effectiveness. The three technical innovations jointly forge a new EA for DRL method named Proximal Evolutionary Strategies (PES). Our experiments on ten continuous control problems show that PES with layer-wise training can be more computationally efficient than CMA-ES; our surrogate model can remarkably reduce the sample complexity of PES in comparison to latest EAs for DRL including CMA-ES, OpenAI-ES, and Uber-GA; PES with gradient-based local search can significantly outperform several promising DRL algorithms including TRPO, AKCTR, PPO, OpenAI-ES, and Uber-GA.

Design and multiobjective optimization of a two‐point contact ladder‐climbing robot using a genetic algorithm

AbstractThis paper presents the design and optimization of a climbing robot. The design of a ladder‐climbing robot is done with fundamental mathematical considerations. The designed robot is robust enough to manage all environmental calamities, and at the same time, it is optimized for lightweight to reduce the actuator's cost and ease of transportation. An analytical evaluation is carried out for both static and dynamic conditions to determine strength and motion characteristics. The multiobjective optimization of the design parameters of a ladder‐climbing robot is done to obtain optimized values of design parameters. The formulation of an optimization problem that considers the minimization of weight and natural frequency is performed. Using an evolutionary genetic algorithm (GA) for the multicriteria optimization problem is solved, and a Pareto front solution is obtained. The optimal values of the parameters are decided based on the knee selection technique. As both objective functions are contradictory, the optimum results significantly improve the robot's performance. Controlling the proportional–integral–derivative (PID) parameters is crucial as the robot climbs with a two‐point contact gait pattern. The controlling parameters impart stability to the robot. PID parameters like proportional, integral and derivative gain are tunned using the GA. Finally, the developed prototype is tested on the ladders of the tower, and satisfactory climbing motion is achieved.

Global Path Planning for Articulated Steering Tractor Based on Multi-Objective Hybrid Algorithm.

With the development of smart agriculture, autopilot technology is being used more and more widely in agriculture. Because most of the current global path planning only considers the shortest path, it is difficult to meet the articulated steering tractor operation needs in the orchard environment and address other issues, so this paper proposes a hybrid algorithm of an improved bidirectional search A* algorithm and improved differential evolution genetic algorithm(AGADE). First, the integrated priority function and search method of the traditional A* algorithm are improved by adding weight influence to the integrated priority, and the search method is changed to a bidirectional search. Second, the genetic algorithm fitness function and search strategy are improved; the fitness function is set as the path tree row center offset factor; the smoothing factor and safety coefficient are set; and the search strategy adopts differential evolution for cross mutation. Finally, the shortest path obtained by the improved bidirectional search A* algorithm is used as the initial population of an improved differential evolution genetic algorithm, optimized iteratively, and the optimal path is obtained by adding kinematic constraints through a cubic B-spline curve smoothing path. The convergence of the AGADE hybrid algorithm and GA algorithm on four different maps, path length, and trajectory curve are compared and analyzed through simulation tests. The convergence speed of the AGADE hybrid algorithm on four different complexity maps is improved by 92.8%, 64.5%, 50.0%, and 71.2% respectively. The path length is slightly increased compared with the GA algorithm, but the path trajectory curve is located in the center of the tree row, with fewer turns, and it meets the articulated steering tractor operation needs in the orchard environment, proving that the improved hybrid algorithm is effective.

Intelligent Indoor Layout Design Based on Interactive Genetic and Differential Evolution Algorithms

Intelligent Indoor Layout Design Based on Interactive Genetic and Differential Evolution Algorithms

Modeling thermoelectric performance of doped BiCuSeO oxychalcogenide ceramics using genetically hybridized support vector regression computational method

Oxychalcogenide BiCuSeO thermoelectric material has demonstrated significant potentials in addressing energy crisis through thermal to electrical energy conversion. Low toxicity, reduced cost of input materials and good stability when operated at high temperatures are unique features that promote the candidature of BiCuSeO ceramics. However, the thermoelectric figure of merit (FM) which influences the conversion (energy) efficiency of BiCuSeO material is characterized with small value due to low carrier mobility and concentration. Incorporation of various kind of dopants at different sites, vacancies and defects creation are among the experimental approaches of enhancing thermoelectric performance (usually, figure of merit). In order to circumvent laborious experimental procedures and to strengthen material design for sustainable applications, the maximum value of thermoelectric figure of merit of doped BiCuSeO ceramics is modeled in this work through genetic evolutionary algorithm hybridized support vector regression (HSVR) using ionic radii and dopant concentrations-based descriptors. FM-HSVR-G model developed with Gaussian kernel performs better than FM-HSVR-P that employs polynomial transformation kernel using different performance assessment parameters such as root mean square error (RMSE), correlation coefficient (CC) and mean absolute error (MAE). When validated on the testing samples of BiCuSeO ceramics, FM-HSVR-G model shows 45.40 % (for CC parameter), 16.87 % (for MAE parameter) and 14.00 % (for RMSE parameter) performance improvement over FM-HSVR-P model. Comparison of the present models with the existing machine learning (ML) model indicates that FM-HSVR-P and FM-HSVR-G model outperform ML (2022) model with enhancement of 22.05% and 72.04 %, respectively using metric based on RMSE. Ability of some elemental dopants to strengthen the thermoelectric performance of pristine BiCuSeO ceramic was investigated using the model developed while the obtained behavior agrees well with the experimental trends. The characteristic precision and accuracy of the developed models would definitely strengthen thermoelectric performance of BiCuSeO based compounds and further facilitate BiCuSeO ceramic material design for controlling energy crisis and other sustainable applications.

Development of Differential Evolution Algorithm for Simple Assembly Line Balancing Problem Type 1

The Simple Assembly Line Balancing Problem Type 1 (SALBP-1) is a widely embraced method in the industry for its simplicity in organizing production processes and enhancing efficiency. Consequently, a Differential Evolution Algorithm (DDE) using a backward task sequence was developed in this study to assist in production process management by determining the optimal number of stations. The efficacy of this method was assessed by juxtaposing it with heuristic approaches, including Longest Operation Time (LOT), Most Following Tasks (MFT), Ranked Positional Weight (RPW), Shortest Operation Time (SOT), Fewest Following Tasks (FFT), Ant Colony Optimization (ACO), Differential Evolution (DE), and Immune Genetic Algorithm (IGA). The findings reveal that DDE outperforms LOT, MFT, RPW, SOT, and FFT in discovering superior solutions and consistently matches solutions achieved by ACO, DE, and IGA methods across all problems. Notably, the DDE method exhibits a shorter time frame for solution discovery.

Comparative multi-objective optimization using neural networks for ejector refrigeration systems with LiBr and LiCl working agents

Education's evolution in the context of energy systems is essential for addressing sustainable energy challenges and developing a workforce equipped for future innovations, emphasizing both formal curricula and informal lifelong learning through successful energy case studies. As the global energy sector transforms to reduce carbon emissions and reliance on fossil fuels, innovations in renewable technologies like solar thermal are pivotal for promoting energy security and economic stability, supported by an educational foundation that fosters awareness and technical skills for sustainable development. Exposure to successful renewable energy systems, such as solar-powered refrigeration, offers an informal educational experience that enhances understanding and supports global educational goals, initiating with the innovative design and optimization of these systems using artificial intelligence (neural networks). Based on this formulation, the current article developed two sustainable energy systems by comparing the refrigeration cycles with two different operating fluids and various arrangements, and multi-objective optimization with an evolutionary genetic algorithm is performed for the proposed systems. The studied systems are refrigeration cycles using an ejector and without an ejector with two working fluids of lithium bromide and lithium chloride. The present work's main aim is to examine the working fluid and refrigeration system arrangements. Energy and economic modeling were performed for the proposed systems, and then parametric analysis and two-objective optimization were extracted. Parameters such as generator temperature, condenser temperature, absorber temperature, and evaporator temperature, which significantly impact the proposed system's performance, have been selected as decision parameters, and parametric analysis has been extracted for them. In addition to the mentioned parameters, diffusion mixing efficiency, nozzle efficiency, and heat exchanger have also been studied in the ejector asset system. To find the best values of decision variables, multi-objective optimization for both arrangements is conducted, and results are presented. The results have indicated that the refrigeration system using lithium chloride working fluid without an ejector achieves a coefficient of performance of 0.766 and a cost of 0.922 $/h at the optimal point, while the system with an ejector yields a higher coefficient of performance (1.047) and a slightly lower cost rate (0.991 $/h). The outcomes of this work can play a critical role for higher education institutions in advancing innovative solutions to pressing energy challenges. Lifelong learning, at the heart of educational innovation, can benefit from the integration of sustainable energy systems as a core component of informal education through the optimization of ejector refrigeration systems.

Elite Multi-Criteria Decision Making—Pareto Front Optimization in Multi-Objective Optimization

Optimization is a process of minimizing or maximizing a given objective function under specified constraints. In multi-objective optimization (MOO), multiple conflicting functions are optimized within defined criteria. Numerous MOO techniques have been developed utilizing various meta-heuristic methods such as Evolutionary Algorithms (EAs), Genetic Algorithms (GAs), and other biologically inspired processes. In a cooperative environment, a Pareto front is generated, and an MOO technique is applied to solve for the solution set. On other hand, Multi-Criteria Decision Making (MCDM) is often used to select a single best solution from a set of provided solution candidates. The Multi-Criteria Decision Making–Pareto Front (M-PF) optimizer combines both of these techniques to find a quality set of heuristic solutions. This paper provides an improved version of the M-PF optimizer, which is called the elite Multi-Criteria Decision Making–Pareto Front (eMPF) optimizer. The eMPF method uses an evolutionary algorithm for the meta-heuristic process and then generates a Pareto front and applies MCDM to the Pareto front to rank the solutions in the set. The main objective of the new optimizer is to exploit the Pareto front while also exploring the solution area. The performance of the developed method is tested against M-PF, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), and Non-Dominated Sorting Genetic Algorithm-III (NSGA-III). The test results demonstrate the performance of the new eMPF optimizer over M-PF, NSGA-II, and NSGA-III. eMPF was not only able to exploit the search domain but also was able to find better heuristic solutions for most of the test functions used.

Optimization of a Circular Planar Spiral Wireless Power Transfer Coil Using a Genetic Algorithm

Optimization of a Circular Planar Spiral Wireless Power Transfer Coil Using a Genetic Algorithm

Heuristic control of power consumption by up to 1000 V electrical loads at mining enterprises

Purpose. To develop a method for synthesizing the structure and algorithm of the system for automated control of power consumption by up to 1000 V electrical receivers at mining enterprises with iron ore underground mining methods. This enables direct control of the load connection to the industrial power grid to ensure minimum power costs depending on its cost per day ahead. Methodology. The problem of controlling power consumption of electrical receivers at iron ore underground mines is formalized as a binary form of mixed integer programming. To solve it, a binary implementation of the heuristic genetic algorithm is used. The mathematical modeling method analyzes the impact of genetic algorithm settings, such as the number of phenotypes in the population, the number of elite phenotypes that pass unchanged to the next generation, and the method of phenotype crossover on its quality. Findings. As a result of the research, it is found that the most effective way to control the process of power consumption based on an evolutionary genetic algorithm is to use the Laplace crossover function and keep the percentage of elite phenotypes in the population at 10 %. Moreover, at the smallest population size, the best accuracy is observed when using the Laplace function, while at one- and two-point crossover functions, it worsens, but not significantly (no more than 0.2 %). However, as the number of elite phenotypes increases, the duration of the evolutionary search in the control process is reduced by almost a factor of two in the case of one- and two-point crossovers. Originality. For the first time, the structure of a heuristic system for automated control of power consumption by underground electrical receivers with a supply voltage of up to 1000 V at iron ore underground mines has been developed on the basis of an evolutionary genetic algorithm. Depending on the designed volumes of ore production and the daily power cost per day, this allows determining the optimal power load schedule of underground distribution substations in advance, which, subject to the accepted limits on hourly and daily power, minimizes the cost of purchasing power, and thus reduces the cost of the final product. Practical value. The architecture of a heuristic system for controlling power consumption by electrical receivers with a voltage of up to 1000 V based on an evolutionary genetic algorithm is developed and recommended when optimizing the power load schedule of transformer substations of mining and metallurgical enterprises, in particular, of iron ore underground mines operating in this voltage class.

4E analysis and triple objective NSGA-II optimization of a novel solar-driven combined ejector-enhanced power and two-stage cooling (EORC-TCRC) system

This study proposed an innovative combined ejector-enhanced organic Rankine cycle and two-stage compression refrigeration cycle (EORC-TCRC), to investigate its potential to revolutionize energy utilization and offer a sustainable solution for the current energy challenge. Energy, exergy, economic, and environmental (4E) analysis of the novel EORC-TCRC system was conducted first. The performance appraisal of the novel system compared to the conventional combined power and ejector refrigeration system has been evaluated. The evolutionary non-dominated Sort Genetic (NSGA-II) optimization algorithm was implemented to ascertain triple-objective optimal system operating conditions. The results revealed a significant improvement in refrigeration output, energy, and exergy efficiency with values of 220.06 kW, 11.67%, and 17.07%, respectively, compared to the conventional Rankine power and ejector refrigeration system. By different selections of the objective functions, four groups comprised of Multi-Objective CT–ղex, Multi-Objective CT–ղTh, Multi-Objective ղex–ղTh, and Triple-Objective mode presented to sought NSGA-II optimization results. The optimization results of Multi-Objective CT–ղTh mode indicated that the best thermal efficiency and overall system cost rate operating conditions are 28.25% and 78,820 ($/year), respectively. While the optimal system operating condition occurs in the Triple-Objective ղex- ղTh-CT with the exergetic efficiency of 41.69%.

NEMA NU 2-2018 evaluation and image quality optimization of a new generation digital 32-cm axial field-of-view Omni Legend PET-CT using a genetic evolutionary algorithm

A performance evaluation was conducted on the new General Electric (GE) digital Omni Legend PET-CT system with 32 cm extended field of view. The first commercially available clinical digital bismuth germanate system. The system does not use time of flight (ToF). Testing was performed in accordance with the NEMA NU2–2018 standard. A comparison was made between two other commercial GE scanners with extended fields of view; the Discovery MI − 6 ring (ToF enabled) and the Discovery IQ (non-ToF). A genetic evolutionary algorithm was developed to optimize image reconstruction parameters from image quality assessments. The Omni demonstrated average spatial resolutions at 1 cm radial offset as 3.9 mm FWHM. The total system sensitivity at the center was 44.36 cps/kBq. The peak NECR was measured as 501 kcps at 17.8 kBq ml−1 with a 35.48% scatter fraction. The maximum count-rate error below NECR peak was 5.5%. Using standard iterative reconstructions, sphere contrast recovery coefficients were from 52.7 ± 3.2% (10 mm) to 92.5 ± 2.4% (37 mm). The PET-CT co-registration accuracy was 2.4 mm. In place of ToF, the Omni employs software corrections through a pre-trained neural network (PDL) (trained on non-ToF to ToF) that takes Bayesian penalized likelihood reconstruction (Q.Clear) images as input. The optimum parameters for image reconstruction, determined using the genetic algorithm were a Q.Clear parameter, β, of 350 and a ‘medium’ PDL setting. Using standard iterative reconstructions, the Omni initially showed increased background variability compared to the Discovery MI. With optimized PDL reconstruction parameters selected using the genetic algorithm the performance of the Omni surpassed that of the Discovery MI on all NEMA tests. The genetic algorithm’s demonstrated ability to enhance image quality in PET-CT imaging underscores the importance of algorithm driven optimization and underscores the requirement to validate its use in the clinical setting.

Genetic Algorithm-Based Optimisation of a Double-Wall Effusion Cooling System for a High-Pressure Turbine Nozzle Guide Vane

Double-Wall Effusion Cooling schemes present an opportunity for aeroengine designers to achieve high overall cooling effectiveness and convective cooling efficiency in High-Pressure Turbine blades with reduced coolant usage compared to conventional cooling technologies. This is accomplished by combining impingement, pin-fin and effusion cooling. Optimising these cooling schemes is crucial to ensuring that cooling is achieved sufficiently at high-heat-flux regions and not overused at low-heat-flux ones. Due to the high number of design variables employed in these systems, optimisation through the use of Computational Fluid Dynamics (CFD) simulations can be a computationally costly and time-consuming process. This study makes use of a Low-Order Flow Network Model (LOM), developed, validated and presented previously, which quickly assesses the pressure, temperature, mass flow and heat flow distributions through a Double-Wall Effusion Cooling scheme. Results generated by the LOM are used to rapidly produce an ideal cooling system design through the use of an Evolutionary Genetic Algorithm (GA) optimisation process. The objective is to minimise the coolant mass flow whilst maintaining acceptable metal cooling effectiveness around the external surface of the blade and ensuring that the Backflow Margin for all film holes is above a selected threshold. For comparison, a Genetic Aggregation model-based optimisation using CFD simulations in ANSYS Workbench is also conducted. Results for both the reduction of coolant mass flow and the total optimisation runtime are analysed alongside those from the LOM, demonstrating the benefit of rapid low-order solving techniques.

Superconductivity of cubic MB6 (M = Na, K, Rb, Cs).

Previous studies have shown that NaB6, KB6, and RbB6 adopting Pm3̄m are superconductors with a relatively high Tc under ambient conditions. In this paper, we conducted systematic structural and related properties research on CsB6 through a genetic evolution algorithm and total energy calculations based on density functional theory between 0 and 20GPa. Our results reveal a cubic Pm3̄m CsB6, which is dynamically stable under the pressures we studied. We systematically calculated the formation enthalpies, electronic properties, and superconducting properties of Pm3̄m MB6 (M = Na, K, Rb, Cs). They all exhibit metallic features, and boron has high contributions to band structures, density of states, and electron-phonon coupling (EPC). The calculated results about the Helmholtz free energy difference of Pm3̄m CsB6 at 0, 10, and 20GPa indicate that it is stable upon chemical decomposition (decomposition to simple substances Cs and B) from 0 to 400K. The phonon density of states indicates that boron atoms occupy the high frequency area. The EPC results show that Pm3̄m CsB6 is a superconductor with Tc = 11.7K at 0GPa, close to NaB6 (13.1K), KB6 (11.7K), and RbB6 (11.3K) at 0GPa in our work, which indicates that boron atoms play an essential role in superconductivity: vibrations of B6 regular octagons lead to the high Tc of Pm3̄m MB6. Our work about Pm3̄m hexaborides provides a supplementary study on the borides of the group IA elements (without Fr and Li) and has an important guiding significance for the experimental synthesis of CsB6.

Energy efficient design of regenerative shock absorbers for automotive suspensions: A multi-objective optimization framework

This study addresses the optimized design of electro-hydrostatic regenerative shock absorbers to enhance vibrational energy recovery in ground vehicles, aiming to reduce carbon footprint. The design strategy focuses on maximizing regeneration efficiency while minimizing actuator volume. Important trade-offs are considered as constraints, such as ride comfort and road holding. The approach employs a multi-objective evolutionary genetic algorithm, validated through numerical analysis, and applied to design a prototype. Experimental results show a peak regeneration efficiency of 45%, and simulations on a class-B vehicle indicate an average regenerated power of 101W per shock absorber, corresponding to a CO2 emission reduction of 5.25g/km.

Multimethod computing technology for searching energy-optimal motion modes of electric rolling stock of railways

The article is devoted to the problem of finding energy-optimal modes of movement of electric rolling stock of railways, considered as a set of problems of finding a global extremum of a functional defined on a set of logical-dynamic system (LDS) movement trajectories that are acceptable under operating conditions, and a structural-parametric synthesis of the LDS control strategy and optimization of its parameters based on random search methods, swarm intelligence, evolutionary algorithms and multi-agent models. A multi-method technology for optimizing the modes of train movement according to the criterion of minimum energy consumption, and software and algorithmic support based on the original multi-agent evolutionary-swarm method for synthesizing the optimal-terminal control of multi-mode moving objects developed by the author are proposed. The specified computing technology includes the following stages: formation of a set of promising control strategies; their optimization and recombination based on the evolutionary genetic algorithm; finding the global extremum of the functional using parametric optimization of the solutions identified at the previous stages using the modified particle swarm method. The computational experiments performed with the simulation model of train movement showed that when a sufficiently large number of options for control strategies are generated at the first stage, the algorithm converges to the global extremum of the functional.

Integrated Decision Making to Determine the Optimal Order Quantity for Raw Materials Using Genetic Evolutionary Algorithms

Decision Making in economic organizations, especially the productivity, is one of the problems that researchers are interested in. It is important for the organizations that seek global competition and the integration of their decision will lead to coordination of decisions between the departments of these organizations to determine optimal order quantity and establish a correct inventory policy to warrant production with least total costs (costs of transportation, holding and purchasing), these is the objective of any company to achieve an adequate and enough inventory level to meet the future needs. In this paper, integrated decision making with three stages, first stage is forecasting with demands to final products using time series, second stage determine required quantities for raw material to manufacture these final products, and formulated a mathematical model reduces the total cost in third stage, when the transportation and purchase are variable costs with order quantity, while in just-in-time model or economic order quantity model are a fixed demand and purchase cost. The aim of this paper is integrated decision making to reduce total cost of required raw materials for the manufacturing processes and without and determine the optimal order quantity using genetic algorithms. This study was applied in Wasit company for cotton products in the textile factory, the results shown Holt-Winter method is the best method to forecasting because has least mean absolute error and the percentage of purchasing cost 73%, Transportation cost 8%, and holding cost 19%. The percentage of purchasing cost of cotton is biggest value, more 99% of purchasing cost.