Adaptive Immune Evolutionary Algorithms Based on Immune Network Regulatory Mechanism

Based on immune network regulatory mechanism, a new adaptive immune evolutionary algorithm （AIEA） is proposed to improve the performance of genetic algorithms （GA） in this paper. AIEA adopts novel selection operation according to the stimulation level of each antibody. A memory base for good antibodies is devised simultaneously to raise the convergent rapidity of the algorithm and adaptive adjusting strategy of antibody population is used for preventing the loss of the population adversity. The experiments show AIEA has better convergence performance than standard genetic algorithm and is capable of maintaining the adversity of the population and solving function optimization problems in an efficient and reliable way.     

Stator Winding Turn Faults Diagnosis for Induction Motor by Immune Memory Dynamic Clonal Strategy Algorithm

Quick detection of a small initial fault is important for an induction motor to prevent a consequent large fault.The mathematical model with basic motor equations among voltages,currents,and fluxes is analyzed and the motor model equations are described.The fault related features are extracted.An immune memory dynamic clonal strategy （IMDCS） system is applied to detecting the stator faults of induction motor.Four features are obtained from the induction motor,and then these features are given to the IMDCS system.After the motor condition has been learned by the IMDCS system,the memory set obtained in the training stage can be used to detect any fault.The proposed method is experimentally implemented on the induction motor,and the experimental results show the applicability and effectiveness of the proposed method to the diagnosis of stator winding turn faults in induction motors.     

Intrusion detection based on rough set and artificial immune

In order to increase intrusion detection rate and decrease false positive detection rate , a novel intrusion detection algorithm based on rough set and artificial immune ( RSAI-IDA) is proposed. Using artificial immune in intrusion detection , anomaly actions are detected adaptively , and with rough set , effective antibodies can be obtained .A scheme , in which antibodies are partly generated randomly and others are from the artificial immune algorithm , is applied to ensure the antibodies di-versity.Finally, simulations of RSAI-IDA and comparisons with other algorithms are given .The ex-perimental results illustrate that the novel algorithm achieves more effective performances on anomaly intrusion detection , where the algorithm ’ s time complexity decreases , the true positive detection rate increases , and the false positive detection rate is decreased .     

A RBF Network Learning Scheme Using Immune Algorithm Based on Information Entropy

A hybrid learning method combining immune algorithm and least square method is proposed to design the radial basis function(RBF) networks. The immune algorithm based on information entropy is used to determine the structure and parameters of RBF nonlinear hidden layer, and weights of RBF linear output layer are computed with least square method. By introducing the diversity control and immune memory mechanism, the algorithm improves the efficiency and overcomes the immature problem in genetic algorithm. Computer simulations demonstrate that the RBF networks designed in this method have fast convergence speed with good performances.     

Anomaly Detection with Artificial Immune Network

Inspired by the immune network theory, an adaptive anomaly detection paradigm based on artificial immune network, referred as APAI, is proposed. The implementation of the paradigm includes： initially, the first is to create the initial antibody network; then, through the learning of each training antigen, the antibody network is evolved and updated by the optimal antibodies. Finally, anomaly detection process is accomplished by majority vote of the k nearest neighbor antibodies in the network. The experiments used the famous Sonar Benchmark dataset in our study, which is taken from the UCI machine learning database. The obtained detection accuracy of APAI was 97.7%, which was very promising with regard to the other classification applications in the literature for this problem. In addition to its nonlinear classification properties, APAI possesses biological immune network properties such as clonal selection, immune network, and immune memory, which can be applied to pattern recognition, classification, and etc.     

EP-based Optimization of Strategic Safety Stocks in Reverse Logistics Systems

A new heuristic strategic safety stock optimization is proposed based on evolutionary programming(EP) algorithm for reverse logistics supply chain systems. The supply chain is described with a network and the modeling complexity of external as well as internal product returns and reuses of supply chains is considered with. It is assumed that customer demands for final products are uncertain. Products are randomly returned from external customers to stock points. The optimization model is established and three different cases with different structures are used to show the strength of the algorithm.     

A Novel Immune System Model and Its Application to Network Intrusion Detection

Based on analyzing the techniques and architecture of existing network Intrusion Detection System (IDS), and probing into the fundament of Immune System (IS), a novel immune model is presented and applied to network IDS, which is helpful to design an effective IDS. Besides, this paper suggests a scheme to represent the self profile of network. And an automated self profile extraction algorithm is provided to extract self profile from packets. The experimental results prove validity of the scheme and algorithm, which is the foundation of the immune model.     

Aheuristic Algorithm for Berth Scheduling Problem in Container Ports

In this paper,the berth scheduling problem is transformed into a special two-dimensional packing problem with some constraints.A nonlinear programming model for the problem is established,and a heuristic algorithm is proposed to solve the model.Simulation results show that the algorithm can improve the utilization of berths on discrete berth scheduling in the container port.     

An e?ective evolutionary algorithm for the multiple container packing problem

This paper focuses on a new optimization problem, which is called “The Multiple Container Packing Problem (MCPP)” and proposes a new evolutionary approach for it. The proposed evolutionary approach uses “Adaptive Link Adjustment Evolutionary Algorithm (ALA-EA)” as a basic framework and it incorporates a heuristic local improvement approach into ALA-EA. The first step of the local search algorithm is to raise empty space through the exchange among the packed items and then to improve the fitness value through packing unpacked items into the raised empty space. The second step is to exchange the packed items and the unpacked items one another toward improving the fitness value. The proposed algorithm is compared to the previous evolutionary approaches at the benchmark instances (with the same container capacity) and the modified benchmark instances (with different container capacity) and that the algorithm is proved to be superior to the previous evolutionary approaches in the solution quality.     

Immune Algorithm For Document Query Optimization

To efficiently retrieve relevant document from the rapid proliferation of large information collections, a novel immune algorithm for document query optimization is proposed. The essential ideal of the immune algorithm is that the crossover and mutation of operator are constructed according to its own characteristics of information retrieval. Immune operator is adopted to avoid degeneracy. Relevant documents retrieved am merged to a single document list according to rank formula. Experimental results show that the novel immune algorithm can lead to substantial improvements of relevant document retrieval effectiveness.     

Hyperthermia on skin immune system and its application in the treatment of HPV-infected skin diseases

In this paper, the effects of hyperthermia on cells and immune system are introduced briefly. The mechanism of action of hyperthermia on human papilloma virus （HPV）-infected skin diseases was elaborated as an example in this paper. Many studies have proved that hyperthermia affects a number of cellular and molecu- lar constitutes in the skin immune system, involving both innate and adaptive immune responses; the efficacy of hyperthermia in treating some infectious and cancerous conditions has been validated and applied in clinics, while molecular mechanisms of hyperthermia affecting the immunereaction is still unclear.     

A usable selection range standard based on test suite reduction algorithms

In this paper, we analyze the features and distinction of 6 classical algorithms: greedy algorithm (G), greedy evolution algorithm (GE), heuristics algorithm (H), greedy heuristic G (GRE), integer linear programming algorithm (ILP) and genetic algorithm (GA) to ensure the main influencing factors-the performance of algorithms and the running time of algorithms. What's more, we would not only present a research design that aims at gaining deeper understanding about the algorithm classification and its function as well as their distinction, but also make an empirical study in order to obtain a practical range standard that can guide the selection of reduction algorithms. When the size of a test object (product of test requirements and test cases) is smaller than 2000×2000, G algorithm is the commonly recommended algorithm. With the growth of test size, the usage of GE and GRE becomes more general.     

Parameter Optimization of Linear Quadratic Controller Based on Genetic Algorithm

The selection of weighting matrix in design of the linear quadratic optimal controller is an important topic in the control theory. In this paper, an approach based on genetic algorithm is presented for selecting the weighting matrix for the optimal controller. Genetic algorithm is adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. In this algorithm, the fitness function is used to evaluate individuals and reproductive success varies with fitness. In the design of the linear quadratic optimal controller, the fitness function has relation to the anticipated step response of the system. Not only can the controller designed by this approach meet the demand of the performance indexes of linear quadratic controller, but also satisfy the anticipated step response of close-loop system. The method possesses a higher calculating efficiency and provides technical support for the optimal controller in engineering application. The simulation of a three-order single-input single-output (SISO) system has demonstrated the feasibility and validity of the approach.     

Dependent-Chance Programming Models for Capital Budgeting in Fuzzy Environments

Capital budgeting is concerned with maximizing the total net profit subject to budget constraints by selecting an appropriate combination of projects. This paper presents chance maximizing models for capital budgeting with fuzzy input data and multiple conflicting objectives. When the decision maker sets a prospective profit level and wants to maximize the chances of the total profit achieving the prospective profit level, a fuzzy dependent-chance programming model, a fuzzy multi-objective dependent-chance programming model, and a fuzzy goal dependent-chance programming model are used to formulate the fuzzy capital budgeting problem. A fuzzy simulation based genetic algorithm is used to solve these models. Numerical examples are provided to illustrate the effectiveness of the simulation-based genetic algorithm and the potential applications of these models.     

Parallelization and Performance Optimization on Face Detection Algorithm with OpenCL: A Case Study

Face detect application has a real time need in nature. Although Viola-Jones algorithm can handle it elegantly, today’s bigger and bigger high quality images and videos still bring in the new challenge of real time needs. It is a good idea to parallel the Viola-Jones algorithm with OpenCL to achieve high performance across both AMD and NVidia GPU platforms without bringing up new algorithms. This paper presents the bottleneck of this application and discusses how to optimize the face detection step by step from a very nave implementation. Some brilliant tricks and methods like CPU execution time hidden, stubbles usage of local memory as high speed scratchpad and manual cache, and variable granularity were used to improve the performance. Those technologies result in 4-13 times speedup varying with the image size. Furthermore, those ideas may throw on some light on the way to parallel applications efficiently with OpenCL. Taking face detection as an example, this paper also summarizes some universal advice on how to optimize OpenCL program, trying to help other applications do better on GPU.     

An effective evolutionary algorithm for the multiple container packing problem

This paper focuses on a new optimization problem, which is called ＂The Multiple Container Packing Problem （MCPP）＂ and proposes a new evolutionary approach for it. The proposed evolutionary approach uses ＂Adaptive Link Adjustment Evolutionary Algorithm （ALA-EA）＂ as a basic framework and it incorporates a heuristic local improvement approach into ALA-EA. The first step of the local search algorithm is to raise empty space through the exchange among the packed items and then to improve the fitness value through packing unpacked items into the raised empty space. The second step is to exchange the packed items and the unpacked items one another toward improving the fitness value. The proposed algorithm is compared to the previous evolutionary approaches at the bench-mark instances （with the same container capacity） and the modified benchmark instances （with different container capacity） and that the algorithm is proved to be superior to the previous evolutionary approaches in the solution quality.     

Hamilton-Jacobi-Bellman equations and dynamic programming for power-optimization of a multistage heat engine system with generalized convective heat transfer law

A multistage endoreversible Carnot heat engine system operating between a finite thermal capacity high-temperature fluid reservoir and an infinite thermal capacity low-temperature environment with generalized convective heat transfer law [q∝(ΔT) m ] is investigated in this paper.Optimal control theory is applied to derive the continuous Hamilton-Jacobi-Bellman (HJB) equations,which determine the optimal fluid temperature configurations for maximum power output under the conditions of fixed initial time and fixed initial temperature of the driving fluid.Based on the universal optimization results,the analytical solution for the Newtonian heat transfer law (m=1) is also obtained.Since there are no analytical solutions for the other heat transfer laws (m≠1),the continuous HJB equations are discretized and dynamic programming algorithm is performed to obtain the complete numerical solutions of the optimization problem.The relationships among the maximum power output of the system,the process period and the fluid temperature are discussed in detail.The results obtained provide some theoretical guidelines for the optimal design and operation of practical energy conversion systems.     

A SAA—based Novel Hybrid Intelligent Evolutionary Algorithm for Job Shop Scheduling Problem

Through systematic analysis and comparison of the common features of SAA,ES and traditional LS(local search)algorithm,a new hybrid strategy of mixing SA,ES with LS,namely HIEA(Hybrid Intelligent Evolutionary Algorithm),is proposed in this paper,Viewed as a whole,the hybrid strategy is also an intelligent heuristic searching procedure.But it has some characteristics such as generality,robustness,etc.,because it synthesizes advantages of SA.ES and LS,while the shortages of the three methods are overcome.This paper applies Markov chain theory to describe the hybrid strategy mathematically,and proves that the algorithm possesses the global asymptotical convergence and alalyzes the performance of HIEA.     

Linked-Tree： An Aggregate Query Algorithm Based on Sliding Window over Data Stream

How to process aggregate queries over data streams efficiently and effectively have been becoming hot re search topics in both academic community and industrial community. Aiming at the issues, a novel Linked-tree algorithm based on sliding window is proposed in this paper. Due to the proposal of concept area, the Linked-tree algorithm reuses many primary results in last window and then avoids lots of unnecessary repeated comparison operations between two successive windows. As a result, execution efficiency of MAX query is improved dramatically. In addition, since the size of memory is relevant to the number of areas but irrelevant to the size of sliding window, memory is economized greatly. The extensive experimental results show that the performance of Linked-tree algorithm has significant improvement gains over the traditional SC （Simple Compared） algorithm and Ranked-tree algorithm.     

Computational algorithms for simulating the grain structure formed on steel billets using cellular automaton and chaos theories

The development of some computational algorithms based on cellular automaton was described to simulate the structures formed during the solidification of steel products. The algorithms described take results from the steel thermal behavior and heat removal previously calculated using a simulator developed by present authors in a previous work. Stored time is used for displaying the steel transition from liquid to mushy and solid. And it is also used to command computational subroutines that reproduce nucleation and grain growth. These routines are logically programmed using the programming language C++ and are based on a simultaneous solution of numerical methods (stochastic and deterministic) to create a graphical representation of different grain structures formed. The grain structure obtained is displayed on the computer screen using a graphical user interface (GUI). The chaos theory and random generation numbers are included in the algorithms to simulate the heterogeneity of grain sizes and morphologies.