Multi-spacecraft trajectory optimization and control using genetic algorithm techniques

This paper presents the fusion of artificial intelligence (AI) learning algorithms that are genetic algorithms (GA) and conjugate gradient (CG) methods. Both methods are used to find the optimum weights for the hidden and output layers of feedforward artificial neural network (ANN) model. Each algorithm is presented in separate module and we proposed three different types of Dynamic Connection Strategies (DyConS) for combining both algorithms to improve the recognition performance of spoken Malay speech recognition. Two different GA techniques are used in this research: a mutated GA technique is proposed and compared with the standard GA technique. One hundred experiments with 5000 words are conducted using the proposed DyConS. Owing to previous facts, GA combined with ANN proved to attain certain advantages with sufficient recognition performance. Thus, from the results, it was observed that the performance of mutated GA algorithm when combined with CG is better than standard GA and CG models. Integrating the GA with feed-forward network improved mean square error (MSE) performance and with good connection strategy by this two stage training scheme, the recognition rate is increased up to 99%.

In this paper Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) techniques has been used for active power loss minimization through optimally placing the Unified Power Flow Controller (UPFC) in the power system network. Two objective functions are taken as the indexes of the system performance; identification of suitable buses using line loss sensitivity index and minimization of active power losses by employing PSO and GA techniques. For the validation and comparison purposes of the proposed techniques, simulations are carried out on modified IEEE 14-bus power system. The results obtained shows that PSO is an easy to use, robust and fast optimization technique compared to GA.

Genetic algorithms (GA) have been applied in many fields to solve multivariable, non linear optimization problems. There are lots of different GA techniques which are used to improve the performance of the algorithm. In this paper, the implementation of GA in optimization of electrical system design for offshore wind farms is proposed and several different GA techniques are investigated. The main problems of GA implementation in this field are addressed and the suitable GA techniques are proposed. The GA method is applied to a real offshore wind farm and the results are evaluated. It is concluded that the genetic algorithm is suitable for the optimization of offshore wind farm layout design as long as proper GA techniques are employed

In this paper, we present a comprehensive and in-depth investigation on the optimization of Proportional-Integral (PI) controller tuning for achieving stability and desired overshoot in the step response. The main objective of this study is to compare the effectiveness of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques in finding the optimal parameters for the PI controller. The PI controller is a widely used control algorithm that plays a crucial role in many industrial processes. Its tuning greatly affects the system's performance, particularly in terms of stability and overshoot. Therefore, finding the optimal tuning parameters is of utmost importance. To address this optimization problem, we propose the utilization of two popular metaheuristic algorithms, GA and PSO. These algorithms are known for their ability to efficiently search through large solution spaces and find near-optimal solutions. By applying these algorithms to the PI controller tuning problem, we aim to determine which technique yields better results in terms of stability and overshoot tuning. In our comparative study, we provide a detailed explanation of both GA and PSO algorithms, focusing on their working principles and mathematical formulations. We also describe how these algorithms can be applied to the PI controller tuning problem. Furthermore, we highlight the key differences between GA and PSO, shedding light on their strengths and weaknesses. To assess the performance of GA and PSO, we conduct several experiments using different benchmark functions and step response models. We measure the stability and overshoot metrics for various parameter settings obtained through GA and PSO. By thoroughly analyzing the obtained results, we draw meaningful conclusions regarding the effectiveness of each technique. Our findings demonstrate that both GA and PSO exhibit promising results in optimizing PI controller tuning. These observations provide valuable insights and guidelines for choosing the appropriate algorithm based on specific control requirements. In conclusion, this comparative study is thought to contribute to the field of control systems engineering by offering a comprehensive analysis of GA and PSO techniques in the context of PI controller tuning. By highlighting their strengths and weaknesses, it is aimed to provide researchers and practitioners with valuable information for making informed decisions when optimizing control parameters for stability and overshoot reduction purposes.

In the past few years, an important increase in the small satellites market has been noticed by the space community. Following this boost on the small satellites business, the need for dedicated launch services arises and designing an efficient and competitive small launcher is a challenge. Together with an innovative design for a small launcher comes the need for the optimization of the trajectory. A trajectory optimization approach has a lot to bring in terms of launcher performances such as achievable payload mass or a higher altitude of the target orbit. This paper is focused on an in-house 3 degree-of-freedom (3DOF) tool for trajectory computation and optimization using a genetic algorithm approach. The cost function is built in such a way to maximize the payload mass of the launcher while achieving the target orbit. The tool is also used for performing sensitivity analyses considering structural mass uncertainties, that can play an important role in the design phase of the vehicle, in order to check their impact on the launcher's performances.

Optimum design of structures under time-variable loadings is a difficult task. Time-dependent behavior of constraints and cost of gradients calculations could be mentioned when applying time history loadings in the optimization problems. To overcome these difficulties, the response spectra as a seismic demand are used instead of using time history acceleration in the structural modeling. In this paper, the P-Delta effects are considered in the finite-element modeling of the frames. Furthermore, many practical constraints are included in the optimization formulation according to the Iranian national building code (Standard N. 2800). The developed MATLAB-based computer program is utilized for optimization of the low, intermediate- and relatively high-rise braced and un-braced steel frames. The obtained results of sequential quadratic programing (SQP) method are compared with the results of genetic algorithm (GA) technique for guarantying the global optimal designs. Because of the inexpensive costs of SQP method in comparison with genetic algorithm technique, SQP method could be confidently applied for obtaining the global optimum designs of the steel frames.

In surface directional drilling, the rational and precise design of wellbore trajectories is essential. Conventional directional wellbore trajectories suffer from issues such as high frictional resistance and torque, poor stability, and trajectory control uncertainties. To address these challenges, this study proposes an optimized trajectory model for short-radius branched directional deflection wells, aiming to minimize the total wellbore length. First, a single-objective nonlinear constrained optimization problem is formulated, incorporating constraints on depth increment, inclination angle, and azimuth range. Then, to solve this optimization problem efficiently, a hybrid method combining Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is developed, enabling optimal trajectory design under the shortest-length constraint. The proposed method combines the rapid solving capability of particle swarm optimization with the comprehensive nonlinear solving capability of genetic algorithms. Leveraging high-performance flexible drilling tools, a wellhole trajectory control mechanism centered on thrust regulation is investigated. Finally, simulations are conducted using ANSYS. The simulation results indicate that the optimized trajectory significantly reduces the stress concentration effects in the formation, with a 58% decrease in the peak stress, and reduces the trajectory length. When applied to roof drilling at a working face in Caojiatan Mine, Shaanxi, the optimized drilling method reduced the drill tool wear by 35%, decreased the maximum thrust difference from 2.7 to 0.8 MPa, and improved the work efficiency (hole completion time) by 14% compared to conventional drilling. The results validate the enhanced stability and reduced wear characteristics of the proposed approach, offering significant theoretical and practical insights for the construction and design of short-vertical-distance drilling operations.

A genetic algorithm (GA) is used to design Spectrally Modulated, Spectrally Encoded (SMSE) waveforms while characterizing the impact of parametric variation on coexistence. As recently proposed, the SMSE framework supports cognition-based, software defined radio (SDR) applications and is well-suited for coexistence analysis. For initial proof-of-concept, two SMSE waveform parameters (number of carriers and carrier bandwidth) are optimized in a coexistent scenario to characterize SMSE impact on Direct Sequence Spread Spectrum (DSSS) bit error performance. Given optimization via GA techniques have been successfully applied in many engineering fields, as well as operations research, they are viable candidates for robust SMSE waveform design. As demonstrated, the analytic SMSE framework is well-suited for parametric optimization via GA techniques.

Unmanned Aerial Vehicles (UAVs), in multi-UAV collaborative search systems, need to realize adaptive autonomous trajectory planning based on their own state and surrounding environmental conditions. To allow for a fast trajectory planning of multiple UAVs from the current state to the target state, this paper proposes a trajectory planning method for multi-UAV collaborative search tasks based on genetic algorithm (GA). And using genetic algorithm, the UAV's trajectory can be optimized online based on the current mission. Through the analysis of the tasks of the multi-UAV collaborative decision-making part and the autonomous trajectory planning part, the genetic algorithm is used to model and optimize the trajectory planning problem to obtain a feasible and efficient optimal search trajectory from the current state to the decision-making target state. Finally, through specific simulation experiments, the optimal trajectory from the current state to the target state of the UAV i in the multi-UAV system is obtained, which verifies the effectiveness of the algorithm.

In the present paper, a composite formation strategy for multiple missiles is proposed, which is composed of the optimal formation configuration trajectory and a robust controller for trajectory tracking and formation keeping. Motion models describing multiple missiles formation configuration and keeping are established. Many constraints such as initial position, terminal position and collision avoidance are taken into account. Based on the Gauss Pseudospectral method, an optimal trajectory is designed which makes multiple missiles configurate the designated shape in the shortest time with less control energy. With the consideration of practical situations such as missiles' position error, measurement error of information between missiles and their limited control input, a robust controller is designed to track the optimal formation configuration trajectory and then keep the designated formation shape by adopting dynamic surface control. Simulation results demonstrate that by adopting the composite formation strategy, multiple missiles can configurate the designated shape in the shortest time with less control energy and then keep the formation shape in spite of much disturbance.

Today's society relies upon an array of complex national and international infrastructure networks such as transportation, telecommunication, financial and energy. Understanding these interdependencies is necessary in order to protect our critical infrastructure. The Critical Infrastructure Modeling System, CIMScopy, examines the interrelationships between infrastructure networks. CIMScopy development is sponsored by the National Security Division at the Idaho National Laboratory (INL) in its ongoing mission for providing critical infrastructure protection and preparedness. A genetic algorithm (GA) is an optimization technique based on Darwin's theory of evolution. A GA can be coupled with CIMScopy to search for optimum ways to protect infrastructure assets. This includes identifying optimum assets to enforce or protect, testing the addition of or change to infrastructure before implementation, or finding the optimum response to an emergency for response planning. This paper describes the addition of a GA to infrastructure modeling for infrastructure planning. It first introduces the CIMScopy infrastructure modeling software used as the modeling engine to support the GA. Next, the GA techniques and parameters are defined. Then a test scenario illustrates the integration with CIMScopy and the preliminary results.

The metaheuristic genetic algorithm is an evolutionary algorithm which means that it will always evolve to get an optimum solution. Due to this intrinsic characteristic, the conventional genetic algorithm might be trapped at local optimum when dealing with a global optimization problem that consists of several maximum points. As such, this paper aims to explore the potential of improving the genetic algorithm by manipulating its operators. With different procedures in selection and replacement operators, the genetic algorithm is be able to compute more efficiently. This mechanism is introduced to prevent the proposed algorithm to be trapped at the local optimum point with shorter computation time. The robustness of the proposed algorithm is tested in optimizing a wireless sensor network (WSN) because the WSN will exhibit multiple peaks with different network configuration. The existence of multiple peaks will lead to additional difficulties for the conventional routing protocol algorithm in tracking the global optimum network configuration or known as global optima. The simulation results show the effect of proposed genetic algorithm with different combinations of operators.

The paper presents a review of various swarm intelligence optimisation, the use of genetic algorithm (GA) and particle swarm optimisation (PSO) technique for optimisation of electronic circuit a case study; minimisation of a single stage common-emitter amplifier. The circuit was transform to small signal module while mesh analysis was applied to convert it to matrix form. Programs were coded in Matlab using GA and PSO technique to minimise the common-emitter circuit. Results, when compared to the original circuit show that both PSO and GA reduce power consumption. It further reviled that PSO was better than GA in terms of higher power reduction.

A genetic algorithms (GA) technique is employed to determine a set of design parameters, which maximize efficiency of B-series propeller with nonlinear constraints. This is the first-known application of the genetic algorithms technique for optimal nonlinear series propeller design. GA are powerful and broadly applicable stochastic search and robust optimization techniques which follow the Darwinian principle of survival of the fittest. GA are very suitable for large parameter space search such as propeller design. Real-world design cases are used to demonstrate that the proposed GA method can be successfully applied to optimal B-series propeller design. Transactions on the Built Environment vol 42, © 1999 WIT Press, www.witpress.com, ISSN 1743-3509 238 Marine Technology

A dynamic chain-like agent genetic algorithm for global numerical optimization and feature selection