Adaptive Immune Genetic Algorithm for Tire Tread Pattern Pitch Parameters Optimization

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.

On the basis of the deficiencies of traditional immune genetic algorithm (IGA) in optimizing noise from tire thread, this thesis presents a new adaptive immune genetic algorithm (AIGA), which improves the genetic operator, provides adaptive crossover operator, mutation operator and reverse order operator, guarantees diversity of population, prevents precocity and promotes the ability of global search. Research was made on the method of intelligent optimization of noise from tire thread based on AIGA, and optimization simulation was carried combining specific tire thread pattern. Favorable result was acquired, which verified the effectiveness and practicability of this method.

In order to improve the convergence and accuracy of M&A risk assessment method, an M&A risk assessment method based on adaptive immune genetic algorithm is proposed. The risk of M&A is divided into M&A preparation stage, M&A implementation stage, and M&A integration stage. The event tree analysis method is used to clarify the inducement of M&A risk events. We optimize the traditional genetic algorithm, design adaptive immune genetic algorithm to ensure the convergence of risk assessment algorithm, select the risk assessment indicators of enterprise M&A, determine the weight of the assessment indicators, and construct the quantitative model of enterprise M&A risk assessment. The experimental results show that the proposed risk assessment method takes less time, has higher accuracy, and has less probability of enterprise data imbalance.

Retracted: Evaluation of M&A Risk Based on Adaptive Immune Genetic Algorithm

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.

Membrane flux is an important indicator to show and prove the extent of membrane pollution . Our team adopted the Elman neural network to establish the MBR contamination simulation prediction model. By setting up the non-linear relationship of influencing MBR membrane pllution’s factors and membrane flux, to complete the membrane flux prediction. Research shows that the MBR membrane flux prediction model based on Elman neural network still has some problems. After reviewing a large number of literatures, we applies improved adaptive immune genetic algorithm based on antibody concentration to optimize this model. At the same time, we compares the optimization effect of this algorithm with the optimization effect of traditional genetic algorithm. By the contrast and analysis, the Elman neural network model optimized input by the improved immune genetic algorithm is superior to the Elman neural network model optimized input by the traditional genetic algorithm on the prediction accuracy and stability of MBR membrane flux.

In order to overcome the shortcomings of Immune Genetic Algorithm's relatively slow convergence rate during the process of solving large-scale optimization problem, given Chaos optimization's benefits of sensitive to the initial value, easy to jump out of local minimum point, the fast search speed, global asymptotic convergence and so on, basing on the both search advantages of Immune Evolutionary Algorithm and Chaos Optimization Algorithm in their own space, considering that chaotic sequence could be simulated the proliferation of immune cells approach, we combined their characteristics of Chaos Optimization Algorithm and Immune Genetic Algorithm, making use of “exchange” and “shift” operations to solution matrix and memory matrix during the optimization process of chaos, and doing adaptive adjustment to selection probability and mutation probability during the Genetic operation, a kind of adaptive Immune Genetic Algorithm based on Chaos is proposed. The validity of the new algorithm is verified by real application.

In this paper, we formulate human motion tracking as a high dimensional constrained dynamic optimization problem. A novel generative method, called Sequential Immune Genetic Algorithm, is proposed for human motion tracking. The main contribution is that we introduce immune genetic algorithm (IGA) for pose optimization in latent space of human motion. As the latent space is low-dimensional and contains the prior knowledge of human motion, it makes pose analysis more efficient and accurate. We apply IGA for pose optimization. Compared with GA and other evolutionary methods, its main advantage is the ability to use the prior knowledge of human motion. As motion tracking is a dynamic optimization problem, we incorporate the temporal continuity information into the traditional IGA and propose a sequential IGA (S-IGA) algorithm. We demonstrate our methods on different videos of different motion types. Experimental results show that the S-IGA motion tracking method can achieve accurate and stable tracking of 3D human motion.

Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of p-Xylene Oxidation

Tire-pavement interaction noise (TPIN) is a dominant noise source for passenger cars and trucks above 25 mph (40 km/h) and above 43 mph (70 km/h), respectively. TPIN is generated due to excitations of the tread pattern and pavement texture. For the same tread pattern and pavement texture at the same speed, TPIN might also be influenced by the tire structure (e.g., the tread rubber hardness and tire size). In the present study, 42 tires with different rubber hardnesses and/or tire sizes were tested at five different speeds (45–65 mph, i.e., 72–105 km/h) on a nonporous asphalt pavement (a section of U.S. Route 460, both eastbound and westbound). An on-board sound intensity system was instrumented on the test vehicle to collect the tire noise data at both the leading edge and the trailing edge of the contact patch. An optical sensor recording the once-per-revolution signal was also installed to monitor the vehicle speed and, more importantly, to provide the data needed to perform the order-tracking analysis to break down the tire noise into two components. These two components are the tread pattern noise and the non–tread pattern noise. It is concluded that for the nonporous asphalt pavement tested, the non–tread pattern noise increases with rubber hardness by ∼0.23 dBA/Shore A. The tire carcass width (section width plus two times section height) influences the central frequencies of the non–tread pattern noise spectrum; the central frequencies decrease as the tire carcass width increases.

Sea ice thickness is an important environment load parameter for reliability assessment and life extension decision of existing ageing offshore platforms in ice region. It is a key factor to provide accurate sea ice thickness prediction. Introducing chaos random sequence and immune mechanism into traditional genetic evolution process, Chaos immune genetic optimization algorithm is constructed. Combining the chaos immune genetic optimization algorithm and the BP neural network, a hybrid neural network models is established. The ice thickness in Bohai Sea is predicted using the hybrid neural network model, and a good fitness is revealed between the prediction and practical values. The results show that the hybrid neural network model is feasible and effective. The parameters of Weibull distribution function for ice thickness is estimated, using predicted ice thickness specimens. The assessment load in later service period is updated and more reliable environment load parameters can be provided for ageing platform assessment.

The adaptive Immune genetic algorithm’s evolution speed is quick and the optimizing ability is unyielding, it’s a effective improvement of the standard genetic algorithm. But it still has the problem of easily falling into local optimum solution. The chaotic algorithm can carry out the ergodic character making a perturbation motion in a certain range of the value, so that the algorithm can jump out of local optimum solution, find the global parameter optimization. In this paper we lead chaos factor into the adaptive Immune algorithm to make the algorithm easier to find the global optimum solution

A tire tends to trap stones in its tread pattern when the vehicle is on a move and this might affects the tire balance due to uneven tread wear of tread portion. The study aims to simulate stone trapping performance under various tire tread patterns and road conditions as well as assessing the performance of tires with stones trapped. The stone trapping phenomena on different tire tread pattern were analyzed under dry and wet road conditions. The tire models chosen were the symmetrical tire, asymmetrical tire, and directional tire. The model of these tires, stone and a flat road surface were created using SolidWorks and Fusion360 software and the static structural simulation is performed by using finite element analysis method. Tire inflation analysis and steady state rolling analysis were conducted to evaluate three parameters: total deformation, Von-Mises stress and equivalent elastic strain of the tires. It found that all three parameters are higher when stone trapped in tire for all tread pattern types. Symmetrical tread pattern provides the least wear and tear since it has the lowest increment of maximum equivalent elastic strain in both road conditions. Stone trapping in tire grooves would impact on the lifespan of the tire.

The article combines Fruit Fly optimization algorithm with Immune Optimization Algorithm into Fruit Fly-Immune algorithm to solve the multi-distribution center location problem with a NP-hard nature. Using MATLAB simulation technology and comparing to the simulation results of traditional Immune Optimization Algorithm shows that the use of Fruit Fly-Immune algorithm to solve multi-distribution center location problem can get better convergence results and weaken faster.

Application of a pattern recognition technique to the prediction of tire noise