Prediction and Optimization of Stability Parameters of Borehole Sensor for Deep Water Drilling Based on Genetic Algorithm

Study the optimization of aero-engine PID (Proportional-Integral-Derivative) controller parameters. In order to improve the tuning accuracy on aero-engine PID control parameters for the optimal solution, this paper presents a genetic algorithm-based PID parameter tuning method. Because the choice of crossover and mutation probabilities in genetic algorithm have a significant impact on the convergence speed and stability of the control system, genetic algorithm is adopted of which the crossover and mutation probability can automatically change with the fitness. Simulation results show that after variable crossover and mutation probability adaptive genetic algorithm to optimize PID control parameters, the average convergence algebra is significantly reduced and the overall control performance of the system is better. Aero engine work process is complex systems that big lag, nonlinear and multi-coupling throughout the flight envelope, Aero engine aerodynamic thermodynamic processes will occur a very big change, with the change of environmental conditions and flight status (airspeed, Mach number, etc.), so be quick, effective control is particularly important. PID control is one of the earliest developed control strategy, due to its simple structure, robustness, high reliability, easy to implement and other characteristics, widely used in process control and motion control, especially in the determination system that precise mathematical model can be established. Three parameters of proportional coefficient ( Kp ), integral time ( Ki ) and derivative time ( Kd ) tuning are direct influenced the control effect of the PID controller, the optimizing of PID controller parameters directly affect the control effect of control system. At present, there are many ways to optimize the PID parameters, such as Ziegler-Nichols method, indirect optimization method, the gradient method, climbing method, etc. The designer of these methods, mainly through the Mathematical model of controlled object, combined with practical experience, through repeated on-site equipment debugging obtained. Because these tuning method are relatively complicated, and affected by human factors, the final parameters of being got are not usually the optimal solution. The traditional tuning method based on a specific model is not applicable, for such aero engine as complex nonlinear systems. Therefore you can take advantage of intelligent control technology for optimizing PID parameters in order to achieve optimal control. Genetic Algorithm (GA) is an optimization method for parallel simulation of natural genetic mechanisms and biological evolution and the formation of a random search, survival of the fittest principle of biological evolution exist in nature, was introduced to optimize the parameters of a coded series groups.

The primary objective of this study is to solve the Vehicle Routing Problem with Stochastic Demands (VRPSD) under restocking policy by using adaptive Genetic Algorithm (GA). The problem of VRPSD is one of the most important and studied combinatorial optimization problems, which finds its application on wide ranges of logistics and transportation area. It is a variant of a Vehicle Routing Problem (VRP). The algorithms for stochastic VRP are considerably more intricate than deterministic VRP and very time consuming. This has led us to explore the used of metaheuristics focusing on the permutation-based GA. The GA is enhanced by automatically adapting the mutation probability to capture dynamic changing in population. The GA becomes a more effective optimizer where the adaptive schemes are depend on population diversity measure. The proposed algorithm is compared with standard GA on a set of randomly generated problems following some discrete probability distributions inspired by real case of VRPSD in solid waste collection in Malaysia. The performances of several types of adaptive mutation probability were also investigated. Experimental results show performance enhancements when adaptive GA is used.

As a classic issue in military operation research, weapon target assignment (WTA) has been of interest to researchers for a long time. The purpose of WTA is to determine the best assignment scheme to gain the largest benefit while satisfying a number of constraints deriving from the capability of available platforms and munitions (or weapons), target characteristics, timings for operation and so on. In order to overcome the drawbacks of easily falling into premature convergence and local optimum of existing heuristic algorithms when they are employed to optimize WTA problems, this paper proposes a hybrid genetic algorithm (HGA) which combines an adaptive genetic algorithm (AGA) with an adaptive variable neighborhood search algorithm (AVNSA) to balance the exploration and exploitation ability. In the framework of HGA, AGA is used for wide scope search in the solution space to avoid premature convergence while AVNSA for local search to jump out of the local optimal space. The simulation result demonstrates the effectiveness and feasibility of the proposed HGA in small-scale WTA optimization problems and through a comparison analysis, advantages of the algorithm over the adaptive genetic algorithm, the immune genetic algorithm and the standard genetic algorithm in large-scale WTA optimization problems are revealed.

BackgroundProtein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task.ResultsIn order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods.ConclusionsThe hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively.

Optimization model is build for solving the aircraft departure sequencing problem in this paper first. Then, an improved genetic algorithm (GA) using symbolic coding is proposed, where a type of total probability crossover and big probability mutation are performed. In this way, the evolutionary policy of Particle Swarm Optimization (PSO) is absorbed into the improved GA, which reduces the complexity and enhance the efficiency greatly. Last, a simulation program using basic GA, adaptive GA, and improved GA is performed. The simulation result shows that the model is effective and Improved GA has better performance than Basic GA or Adaptive GA.

The main concern of all economic companies is the resources equipping and allocating them in different economic sectors with the aim of maximizing profit and minimizing risk. Decentralization is one of the important factors that reduce investment risk. The investors plan to create investment by carefully planning and collecting sufficient information on the economic situation and analyzing the situation of various industries. As an economic enterprise, banks are looking for short- and long-term investments in a types of loans ,such as bailment of a capital , civil participation, reward, etc, which guarantees the return of their capital. In this paper, considering the condition of a bank as an economic enterprise, a model is presented which not only increases profit but also reduces risk. Two objective functions have been defined that the first objective is to minimize the risk and the second objective function is to maximize the of the bank profit, which is used by robust programming and Malvi Sim model. In this paper, we have investigated the Risky and non-Risky Partfolio and the optimal portfolio of bank assets from scenario based solution of the model and by using PSO and Genetic Optimization Algorithm. At all levels of confidence and optimal values of risk based on the estimation of SPP-CVAR method by Particle Swarm Algorithm (PSA) is less than genetic algorithm, which indicates better performance of Particle Swarm Algorithm (PSA) than Genetic Algorithm (GA). Also, the optimum wealth obtained from PSA solution is higher at all levels of confidence than the corresponding value of Genetic Algorithm (GA), and this is another reason to confirm the performance of PSO algorithm compared to the Genetic Algorithm (GA). The values of the first goal function, obtained from the PSO algorithm, for all confidence levels are lower than those of the genetic algorithm. The optimum wealth obtained from PSA is higher than genetic algorithm. At 0.9 level, the value of LR of kupiec statistics for the SPP-CVAR method was less than the Chi-square statistics (Critical value) which was assumed to be acceptable.

With the development of measurement technology, the Flexible Measuring Arm (FMA) is widely used in quality test of automobile processing and industrial production. FMA is a kind of nonlinear system with many parameters. Low cost and efficient calibration method have become the focuses of attention. This article presents a fast calibration method for FMA based on an adaptive Genetic Algorithm (GA) just with several standard balls and a ball plate. It can greatly reduce the calibration cost than common external calibration method which needs high precision instruments and sensors. Firstly, the kinematic model of FMA is established by RPY theory. Secondly, the common GA is optimized and improved, and an adaptive mechanism is added to the algorithms which can realize the automatic adjustment of crossover and mutation operators. A Normalized Genetic Algorithm (NGA) with adaptive mechanism is proposed to complete the optimization calculation. It can improve the numbers of optimal individuals and the convergence speed. So, the search efficiency will be enhanced greatly. Finally, the Least square method (LSM), the General Genetic Algorithm (GGA), and the proposed NGA are respectively used to finish the calibration work. The compensation accuracy and the search efficiency with the above three different algorithms have been systematically analyzed. Experiment indicates that the performance of NGA is much better than LSM and GGA. The data also has proved that the LSM is suitable to complete optimization calculation for linear system. Its convergence stability is much poorer than NGA and GGA because of the ill-condition Jacobin matrix. GGA is easy to fall into local optimization because of the fixed operators. The proposed NGA obviously owns fast convergence speed, high accuracy and better stability than GGA. The position error is reduced from 3.17 to 0.5 mm after compensation with the proposed NGA. Its convergence rate is almost two time of GGA which applies constant genetic factors. The effectiveness and feasibility of proposed method are verified by experiment.

Optimization and design of an aircraft’s morphing wing-tip demonstrator for drag reduction at low speed, Part I – Aerodynamic optimization using genetic, bee colony and gradient descent algorithms

The identification of stiffness parameters of armoured vehicles is the prerequisite and key to the accurate calculation of armoured vehicle dynamics. A linear multi-rigid-flexible body dynamics model of the armoured vehicle system is developed by using transfer matrix method of multibody system. The overall transfer equation, overall transfer matrix, and characteristics equation of the system are deduced based on the automatic deduction theorem of overall transfer equation of multibody system. Combined with an adaptive genetic algorithm, the objective function is built using the eigenfrequencies obtained from the modal test and simulation to optimize the minimum value. In comparison with the ordinary genetic algorithm, the adaptive genetic algorithm has a faster convergence speed. Numerical simulation and experimental verification show the effectiveness and accuracy of the method. The paper provides an effective method for studying the identification of stiffness parameters of armoured vehicles.

As a result of the emergence of many kinds of high-speed communication systems and increasing demand of distributed multimedia applications, efficient and effective support of quality of service (QoS) has become more and more essential. Many service models and mechanisms have been put forward by IETF to meeting QoS requirement, multicast service is a key one of them and is becoming a key requirement of computer network supposing multimedia application.Multicasting consists of concurrently sending the same information from a source to a subset of all possible destinations in a computer network. Multicast utilizes a tree delivery structure, on which data packets are duplicated only at fork nodes and are forwarded only once over each link. This approach makes multicast resource—efficient in delivering data to a group of member simultaneously and can scale well to support very large multicast group. This paper focuses on the algorithms to construct low cost multicast routing tree with QoS requirements.In this paper, we study the bandwidth, delay, delay jitter, and packet loss constrained least cost multicast routing problem which is known to be NP-complete, and present a adaptive genetic simulated annealing algorithm (AGASA) to solve the problem. The proposed genetic algorithm has the following characteristics: (1) Tree structure coding scheme, by which we can use any data structure of the tree to describe the chromosome structure, is an effective solution to the algorithm in the coding and decoding. (2) Genetic algorithm is improved, and simulated annealing algorithm is combined with. Simulated annealing algorithm has stronger local search capabilities, which avoids a partial optimum during the search process. (3) The crossover and mutation probability of adaptive genetic algorithm is improved. The selection of the crossover and mutation probability is the key to the behavior and performance of the algorithm, which has the direct impact on the convergence of the algorithm.The simulation results show that the algorithm has fast convergence and better performance. It is able to satisfy the changing network scale.

It is now common knowledge that blind search algorithms cannot perform with equal efficiency on all possible optimization problems defined on a domain. This knowledge applies also to Genetic Algorithms when viewed as global and blind optimizers. From this point of view it is necessary to design algorithms capable of adapting their search behavior by making use in a direct fashion of the knowledge pertaining to the search landscape. The paper introduces a novel adaptive Genetic Algorithm where the exploration/exploitation is directly controlled during evolution using a Bayesian decision process. Test cases are analyzed as to how parameters affect the search behavior of the algorithm.

Aiming at prematureness, slow convergence rate and reduction in diversity which exist in Genetic Algorithm (GA), this paper presents Adaptive Immune Genetic Algorithm (AIGA) based on GA and immune system mechanism. Adaptive Immune Genetic Algorithm introduces antigens recognition function, immune memory function and antibodies self-adjusting function to Genetic Algorithm, and replaces the fixed probability crossover and mutation operator of Genetic Algorithm with the adaptive probability crossover and mutation operator. AIGA overcomes some disadvantages of GA, such as prematureness, slow convergence speed and reduction in diversity. And AIGA has strong global optimization ability and high searching efficiency. Then AIGA is used to optimize PID parameter for the main steam temperature control system. The simulation comparison experiment with different methods shows that PID parameters obtained by AIGA may provide better control effect than those obtained by GA and the engineering tuning methods. That is, the system control effect adopting AIGA-PID parameter has small overshoot, short adjusting time, and smooth transition. The simulation result also proves the validity of AIGA.

The traditional single intelligent algorithm has slow convergence speed in the frequency assignment, and the effect cannot meet the increasing frequency equipment in the naval battlefield. Based on the single intelligent frequency assignment algorithm and the frequency conflict analysis model between systems, this paper proposes three heuristic frequency assignment algorithms based on hybrid genetic algorithms, namely, Genetic Algorithm and Tabu Search (GATS), Hybrid Genetic Simulated Annealing Algorithm (HGSAA) and Genetic Algorithm-ant Colony Algorithm (GAA). The simulation results show that the hybrid algorithm can quickly converge to a better allocation result than the single intelligent algorithm in the frequency assignment problem, and the optimal value is better than the single intelligent algorithm. Among them, the convergence speed and optimal value of the GAA are the best of the three algorithms. Therefore, this algorithm can be applied to frequency assignment with more frequency-using equipment within a fixed range.

Nomenclature Aircraft design is a complex multidisciplinary process to determine aircraft configuration variables that satisfy a set of mission requirements. It is very hard for aircraft designers to foresee the consequences of changing certain variables. Furthermore, conventional optimization processes are limited by the type and number of parameters used, resulting in sub-optimal designs. The objective of this research is to test the functionality and implementation of a multidisciplinary aircraft conceptual design optimization method using an adaptive genetic algorithm (GA), as a feasible alternative to the existing sizing and optimization methods. To illustrate the approach the algorithm is used to optimize a medium range commercial aircraft, with takeoff weight as an optimization goal, subjected to constraints in performance and geometric parameters. Adaptive and traditional formulations for the handling of constraints by the GA are tested and compared. Results show the ability of the adaptive GA to unbiased search through the design space of aircraft conceptual designs, leading to more viable aircraft configurations than the traditional GA approach at reduced timeframes, with a lower cost than current aircraft design optimization procedures.

A hybrid shuffled complex evolution approach with pattern search for unconstrained optimization