Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

Akış tipi çizelgeleme problemlerinin yapay bağışıklık sistemleri ile çözümünde yeni bir yaklaşım

The n-job, m-machine flow shop scheduling problem is one of the most general job scheduling problems. This study deals with the criteria of makespan minimization for the flow shop scheduling problem. Artificial Immune Systems (AIS) are new intelligent problem solving techniques that are being used in scheduling problems. AIS can be defined as computational systems inspired by theoretical immunology, observed immune functions, principles and mechanisms in order to solve problems. In this research, a computational method based on clonal selection principle and affinity maturation mechanisms of the immune response is used. The operation parameters of meta-heuristics have an important role on the quality of the solution. Thus, a generic systematic procedure which bases on a multi-step experimental design approach for determining the efficient system parameters for AIS is presented. Experimental results show that, the artificial immune system algorithm is more efficient than both the classical heuristic flow shop scheduling algorithms and simulated annealing.n iş m makina akış tipi iş çizelgeleme problemi en genel iş çizelgeleme problemlerinden biridir. Bu çalışma akış tipi çizelgeleme problemi için toplam tamamlanma zamanı minimizasyonu ile ilgilenmektedir. Yapay Bağışıklık Sistemleri (YBS), çizelgeleme problemlerinde son dönemlerde kullanılan yeni bir problem çözme tekniğidir. YBS, doğal bağışıklık sisteminin prensiplerini ve mekanizmalarını kullanarak problemlere çözüm üreten bir hesaplama sistemidir. Bu çalışmada, bağışıklık tepkisinin iki ayrı mekanizması olan klonel seçim prensibi ve benzerlik mekanizması üzerine kurulmuş bir metod kullanılmıştır. Meta sezgisel yöntemlerde seçilen operatörler, çözüm kalitesi üzerinde önemli bir role sahiptir. Bu nedenle, yapay bağışıklık sisteminin etkin parametrelerinin belirlenmesinde çok aşamalı bir deney tasarımı prosedürü uygulanmıştır. Deney sonuçları, yapay bağışıklık sistemlerinin klasik çizelgeleme ve tavlama benzetimi algoritmalarından daha iyi sonuçlar verdiğini göstermiştir

A survey of diversity-oriented optimization

The concept of diversity plays a crucial role in many optimization approaches: On the one hand, diversity can be formulated as an essential goal, such as in level set approximation or multiobjective optimization where the aim is to find a diverse set of alternative feasible or, respectively, Pareto optimal solutions. On the other hand, diversity maintenance can play an important role in algorithms that ultimately searc

A New Approach to Solve Flowshop Scheduling Problems By Artificial Immune Systems

n iş m makina akış tipi iş çizelgeleme problemi en genel iş çizelgeleme problemlerinden biridir. Bu çalışma akış tipi çizelgeleme problemi için toplam tamamlanma zamanı minimizasyonu ile ilgilenmektedir. Yapay Bağışıklık Sistemleri (YBS), çizelgeleme problemlerinde son dönemlerde kullanılan yeni bir problem çözme tekniğidir. YBS, doğal bağışıklık sisteminin prensiplerini ve mekanizmalarını kullanarak problemlere çözüm üreten bir hesaplama sistemidir. Bu çalışmada, bağışıklık tepkisinin iki ayrı mekanizması olan klonel seçim prensibi ve benzerlik mekanizması üzerine kurulmuş bir metod kullanılmıştır. Meta sezgisel yöntemlerde seçilen operatörler, çözüm kalitesi üzerinde önemli bir role sahiptir. Bu nedenle, yapay bağışıklık sisteminin etkin parametrelerinin belirlenmesinde çok aşamalı bir deney tasarımı prosedürü uygulanmıştır. Deney sonuçları, yapay bağışıklık sistemlerinin klasik çizelgeleme ve tavlama benzetimi algoritmalarından daha iyi sonuçlar verdiğini göstermiştir.The n-job, m-machine flow shop scheduling problem is one of the most general job scheduling problems. This study deals with the criteria of makespan minimization for the flow shop scheduling problem. Artificial Immune Systems (AIS) are new intelligent problem solving techniques that are being used in scheduling problems. AIS can be defined as computational systems inspired by theoretical immunology, observed immune functions, principles and mechanisms in order to solve problems. In this research, a computational method based on clonal selection principle and affinity maturation mechanisms of the immune response is used. The operation parameters of meta-heuristics have an important role on the quality of the solution. Thus, a generic systematic procedure which bases on a multi-step experimental design approach for determining the efficient system parameters for AIS is presented. Experimental results show that, the artificial immune system algorithm is more efficient than both the classical heuristic flow shop scheduling algorithms and simulated annealing

A New Approach to Solve Flowshop Scheduling Problems By Artificial Immune Systems

n iş m makina akış tipi iş çizelgeleme problemi en genel iş çizelgeleme problemlerinden biridir. Bu çalışma akış tipi çizelgeleme problemi için toplam tamamlanma zamanı minimizasyonu ile ilgilenmektedir. Yapay Bağışıklık Sistemleri (YBS), çizelgeleme problemlerinde son dönemlerde kullanılan yeni bir problem çözme tekniğidir. YBS, doğal bağışıklık sisteminin prensiplerini ve mekanizmalarını kullanarak problemlere çözüm üreten bir hesaplama sistemidir. Bu çalışmada, bağışıklık tepkisinin iki ayrı mekanizması olan klonel seçim prensibi ve benzerlik mekanizması üzerine kurulmuş bir metod kullanılmıştır. Meta sezgisel yöntemlerde seçilen operatörler, çözüm kalitesi üzerinde önemli bir role sahiptir. Bu nedenle, yapay bağışıklık sisteminin etkin parametrelerinin belirlenmesinde çok aşamalı bir deney tasarımı prosedürü uygulanmıştır. Deney sonuçları, yapay bağışıklık sistemlerinin klasik çizelgeleme ve tavlama benzetimi algoritmalarından daha iyi sonuçlar verdiğini göstermiştir.The n-job, m-machine flow shop scheduling problem is one of the most general job scheduling problems. This study deals with the criteria of makespan minimization for the flow shop scheduling problem. Artificial Immune Systems (AIS) are new intelligent problem solving techniques that are being used in scheduling problems. AIS can be defined as computational systems inspired by theoretical immunology, observed immune functions, principles and mechanisms in order to solve problems. In this research, a computational method based on clonal selection principle and affinity maturation mechanisms of the immune response is used. The operation parameters of meta-heuristics have an important role on the quality of the solution. Thus, a generic systematic procedure which bases on a multi-step experimental design approach for determining the efficient system parameters for AIS is presented. Experimental results show that, the artificial immune system algorithm is more efficient than both the classical heuristic flow shop scheduling algorithms and simulated annealing

Artificial Immune Algorithm for exams timetable

The Artificial Immune System is a novel optimization algorithm designed on the resilient behavior of the immune system of vertebrates. In this paper, this algorithm is used to solve the constrained optimization problem of creating a university exam schedule and assigning students and examiners to each of the sessions. Penalties are imposed on the violation of the constraints. Abolition of the penalties on the hard constraints in the first stage leads to feasible solutions. In the second stage, the algorithm further refines the search in obtaining optimal solutions, where the exam schedule matches the preferences of the examiners

Inspection and Monitoring of Structural Damage Using Vibration Signatures and Smart Techniques

The structural damage detection plays an important role in the evaluation of structural systems and to ensure their safety. Structures like large bridges should be continuously monitored for detection of damage. The cracks usually change the physical parameters like stiffness and flexibility which in turn changes the dynamic properties such as natural frequencies and mode shapes. Crack detection of a beam element comprises of two aspects: the first one is the forward problem which is achieved from the Eigen parameters and the second one is the process to locate and quantify the effect of damage and is termed as ‘inverse process of damage detection’. In the present investigation the analytical and numerical methods are known as the forward problem includes determination of natural frequencies from the knowledge of beam geometry and crack dimension. The vibration signals are derived from the forward problem is exploited in the inverse problem. The natural frequency changes occur due to the various reasons such as boundary condition changes, temperature variations etc. Among all the changes boundary condition changes are the most important factors in structural elements. Many major structures like bridges are made up of uniform beams of unknown boundary conditions. So in the present investigation two of the boundary conditions i.e. fixed -free and fixed- fixed are considered. Using the forward solution method, the natural frequencies are determined. In the inverse solution method various Artificial Intelligence (AI) techniques with their hybrid methods are proposed and implemented. Damage detection problems using Artificial Intelligence techniques require a number of training data sets that represent the uncracked and cracked scenarios of practical structural elements. In the second part of the work different AI techniques like Fuzzy Logic, Genetic Algorithm, Clonal Selection Algorithm, Differential Evolution Algorithm and their hybrid methods are designed and developed. In summary this investigation is a step towards to forecast the position of the damage using the Artificial Intelligence techniques and compare their results. Finally, the results from the Artificial Intelligence techniques and their hybridized algorithms are validated by doing experimental analysis

Immunity-based evolutionary algorithm for optimal global container repositioning in liner shipping

Global container repositioning in liner shipping has always been a challenging problem in container transportation as the global market in maritime logistics is complex and competitive. Supply and demand are dynamic under the ever changing trade imbalance. A useful computation optimization tool to assist shipping liners on decision making and planning to reposition large quantities of empty containers from surplus countries to deficit regions in a cost effective manner is crucial. A novel immunity-based evolutionary algorithm known as immunity-based evolutionary algorithm (IMEA) is developed to solve the multi-objective container repositioning problems in this research. The algorithm adopts the clonal selection and immune suppression theories to attain the Pareto optimal front. The proposed algorithm was verified with benchmarking functions and compared with four optimization algorithms to assess its diversity and spread. The developed algorithm provides a useful means to solve the problem and assist shipping liners in the global container transportation operations in an optimized and cost effective manner. © 2010 The Author(s).published_or_final_versionSpringer Open Choice, 21 Feb 201

LQR Tuning Using AIS for Frequency Oscillation Damping

Commonly, primary control, i.e. governor, in the generation unit had been employed to stabilize the change of frequency due to the change of electrical load during system operation. But, the drawback of the primary control was it could not return the frequency to its nominal value when the disturbance was occurred. Thus, the aim of the primary control was only stabilizing the frequency to reach its new value after there were load changes. Therefore, the LQR control is employed as a supplementary control called Load Frequency Control (LFC) to restore and keep the frequency on its nominal value after load changes occurred on the power system grid. However, since the LQR control parameters were commonly adjusted based on classical or Trial-Error Method (TEM), it was incapable of obtaining good dynamic performance for a wide range of operating conditions and various load change scenarios. To overcome this problem, this paper proposed an Artificial Immune System (AIS) via clonal selection to automatically adjust the weighting matrices, Q and R, of LQR related to various system operating conditions changes. The efficacy of the proposed control scheme was tested on a two-area power system network. The obtained simulation results have shown that the proposed method could reduce the settling time and the overshoot of frequency oscillation, which is better than conventional LQR optimal control and without LQR optimal control

Artificial immune systems based committee machine for classification application

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A new adaptive learning Artificial Immune System (AIS) based committee machine is developed in this thesis. The new proposed approach efficiently tackles the general problem of clustering high-dimensional data. In addition, it helps on deriving useful decision and results related to other application domains such classification and prediction. Artificial Immune System (AIS) is a branch of computational intelligence field inspired by the biological immune system, and has gained increasing interest among researchers in the development of immune-based models and techniques to solve diverse complex computational or engineering problems. This work presents some applications of AIS techniques to health problems, and a thorough survey of existing AIS models and algorithms. The main focus of this research is devoted to building an ensemble model integrating different AIS techniques (i.e. Artificial Immune Networks, Clonal Selection, and Negative Selection) for classification applications to achieve better classification results. A new AIS-based ensemble architecture with adaptive learning features is proposed by integrating different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the combination of these techniques. Various techniques related to the design and enhancements of the new adaptive learning architecture are studied, including a neuro-fuzzy based detector and an optimizer using particle swarm optimization method to achieve enhanced classification performance. An evaluation study was conducted to show the performance of the new proposed adaptive learning ensemble and to compare it to alternative combining techniques. Several experiments are presented using different medical datasets for the classification problem and findings and outcomes are discussed. The new adaptive learning architecture improves the accuracy of the ensemble. Moreover, there is an improvement over the existing aggregation techniques. The outcomes, assumptions and limitations of the proposed methods with its implications for further research in this area draw this research to its conclusion