An Artificial Immune Dynamical System for Optimization

2011 ◽  
pp. 49-75
Author(s):  
Licheng Jiao ◽  
Maoguo Gong ◽  
Wenping Ma
Keyword(s):  
Dynamical System ◽  
Immune Response ◽  
Numerical Optimization ◽  
High Performance ◽  
Optimization Problems ◽  
Clonal Selection ◽  
Immune Memory ◽  
Artificial Immune ◽  
Dynamic Algorithm ◽  
Whole Process

Many immue-inspired algorithms are based on the abstractions of one or several immunology theories, such as clonal selection, negative selection, positive selection, rather than the whole process of immune response to solve computational problems. In order to build a general computational framework by simulating immune response process, this chapter introduces a population-based artificial immune dynamical system, termed as PAIS, and applies it to numerical optimization problems. PAIS models the dynamic process of human immune response as a quaternion (G, I, R, Al), where G denotes exterior stimulus or antigen, I denotes the set of valid antibodies, R denotes the set of reaction rules describing the interactions between antibodies, and Al denotes the dynamic algorithm describing how the reaction rules are applied to antibody population. Some general descriptions of reaction rules, including the set of clonal selection rules and the set of immune memory rules are introduced in PAIS. Based on these reaction rules, a dynamic algorithm, termed as PAISA, is designed for numerical optimization. In order to validate the performance of PAISA, 9 benchmark functions with 20 to 10,000 dimensions and a practical optimization problem, optimal approximation of linear systems are solved by PAISA, successively. The experimental results indicate that PAISA has high performance in optimizing some benchmark functions and practical optimization problems.

LAMARCKIAN LEARNING IN CLONAL SELECTION ALGORITHM FOR NUMERICAL OPTIMIZATION

2010 ◽  
Vol 19 (01) ◽  
pp. 19-37 ◽  
Author(s):  
MAOGUO GONG ◽  
LICHENG JIAO ◽  
JIE YANG ◽  
FANG LIU
Keyword(s):  
Local Search ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Clonal Selection ◽  
Benchmark Problems ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Tournament Selection ◽  
Selection Operator ◽  
Lamarckian Learning

In this paper, we introduce Lamarckian learning theory into the Clonal Selection Algorithm and propose a sort of Lamarckian Clonal Selection Algorithm, termed as LCSA. The major aim is to utilize effectively the information of each individual to reinforce the exploitation with the help of Lamarckian local search. Recombination operator and tournament selection operator are incorporated into LCSA to further enhance the ability of global exploration. We compare LCSA with the Clonal Selection Algorithm in solving twenty benchmark problems to evaluate the performance of LCSA. The results demonstrate that the Lamarckian local search makes LCSA more effective and efficient in solving numerical optimization problems.

A FUZZY INFERENCE SYSTEM TO DETERMINE THE NUMBER OF CLONES IN A CLASS OF ARTIFICIAL IMMUNE SYSTEMS

2012 ◽  
Vol 11 (01) ◽  
pp. 1250005
Author(s):  
LUIZ ANTONIO CARRARO ◽  
LEANDRO NUNES DE CASTRO ◽  
ANGELITA MARIA DE RE ◽  
FABRĹCIO OLIVETTI DE FRANÇA
Keyword(s):  
Immune Response ◽  
Immune Cell ◽  
Fuzzy Inference ◽  
Clonal Selection ◽  
Selection Process ◽  
Artificial Immune Systems ◽  
Reproduction Rate ◽  
Artificial Immune ◽  
Inference System ◽  
Immune Systems

Artificial immune systems are composed of techniques inspired by immunology. The clonal selection principle ensures the organism adaptation to fight invading antigens by an immune response activated by the binding of antigens and antibodies. Since the immune response must correctly allocate the available resources in order to attack an antigen with its best available antibody while trying to learning an even better one, the reproduction rate of each immune cell must be carefully determined. This paper presents a novel fuzzy inference technique to calculate the suitable number of clones for immune inspired algorithms that uses the clonal selection process as the evolutionary process. More specifically, this technique is applied to the CLONALG algorithm for solving pattern recognition tasks and to the copt-aiNet algorithm for solving combinatorial optimization tasks, particularly the Traveling Salesman Problem. The obtained results show that the fuzzy approach makes it possible to automatically determine the number of clones in CLONALG and copt-aiNet, thus eliminating this key user-defined parameter.

scholarly journals Realization of a Framework for Simulation-Based Large-Scale Shape Optimization Using Vertex Morphing

2021 ◽  
Author(s):  
Aditya Ghantasala ◽  
Reza Najian Asl ◽  
Armin Geiser ◽  
Andrew Brodie ◽  
Efthymios Papoutsis ◽  
...  
Keyword(s):  
Shape Optimization ◽  
Numerical Optimization ◽  
High Performance ◽  
Large Scale ◽  
Optimization Problems ◽  
Engineering Design Process ◽  
Current Contribution ◽  
Simulation Based ◽  
2D And 3D ◽  
Performance Computing

AbstractThere is a significant tendency in the industry for automation of the engineering design process. This requires the capability of analyzing an existing design and proposing or ideally generating an optimal design using numerical optimization. In this context, efficient and robust realization of such a framework for numerical shape optimization is of prime importance. Another requirement of such a framework is modularity, such that the shape optimization can involve different physics. This requires that different physics solvers should be handled in black-box nature. The current contribution discusses the conceptualization and applications of a general framework for numerical shape optimization using the vertex morphing parametrization technique. We deal with both 2D and 3D shape optimization problems, of which 3D problems usually tend to be expensive and are candidates for special attention in terms of efficient and high-performance computing. The paper demonstrates the different aspects of the framework, together with the challenges in realizing them. Several numerical examples involving different physics and constraints are presented to show the flexibility and extendability of the framework.

SOLVING CONSTRAINED GLOBAL OPTIMIZATION VIA ARTIFICIAL IMMUNE SYSTEM

2011 ◽  
Vol 20 (01) ◽  
pp. 1-27 ◽  
Author(s):  
JUI-YU WU
Keyword(s):  
Bone Marrow ◽  
Global Optimization ◽  
Optimization Problems ◽  
Clonal Selection ◽  
Numerical Results ◽  
Network Selection ◽  
Receptor Editing ◽  
Idiotypic Network ◽  
Artificial Immune ◽  
Immune Systems

Artificial immune systems (AISs) are computational intelligence (CI) oriented methods using information based on biological immune systems. In this study, an AIS, which combines the metaphor of clonal selection with idiotypic network theories, is developed. Although they are contradictory approaches, clonal selection and idiotypic network may prove useful in designing a stochastic global optimization tool. The AIS method consists of idiotypic network selection, somatic hypermuation, receptor editing and bone marrow operators. The idiotypic network selection operator determines the number of good solutions. The somatic hypermutation and receptor editing operators comprise the searching mechanisms for the exploration of the solution space. Diversity on the population of solutions is ensured by the bone marrow operator. The performance of the proposed AIS method is tested on a set of global constrained optimization problems (GCO), comprising of four benchmark nonlinear programming problems and four generalized polynomial programming (GPP) problems, where GPP problems are nonconvex optimization problems. The best solution found by the AIS algorithm is compared with the known global optimum. Numerical results show that the proposed method converged to the global optimal solution to each tested CGO problem. Moreover, this study compares the numerical results obtained by the AIS approach with those taken from published CI approaches, such as alternative AIS methods and genetic algorithms.

Constrained design optimization of active magnetic bearings through an artificial immune system

2016 ◽  
Vol 33 (8) ◽  
pp. 2395-2420 ◽  
Author(s):  
Yu-Cheng Chou ◽  
Yi-Hua Fan ◽  
Madoka Nakajima ◽  
Yi-Lin Liao
Keyword(s):  
Design Optimization ◽  
Structural Design ◽  
Optimization Problems ◽  
Clonal Selection ◽  
Magnetic Bearings ◽  
Active Magnetic Bearings ◽  
Artificial Immune ◽  
Content Type ◽  
Structural Design Optimization ◽  
Single Objective

Purpose The purpose of this paper is to present the use of artificial immune systems (AISs) to solve constrained design optimization problems for active magnetic bearings (AMBs). Design/methodology/approach This research applies the AIS approach, more specifically, a representative clonal selection-based AIS called CLONALG, to the single-objective structural design optimization of AMBs. In addition, when compared with a genetic algorithm (GA) developed in the previous work, the CLONALG fails to produce best solutions when a nearly zero feasible ratio occurs in an AMB design problem. Therefore, an AIS called ARISCO (AIS for constrained optimization) is proposed to address the above issue. Findings A total of six AMB design cases are solved by the GA, CLONALG, and ARISCO. Based on the simulation results, in terms of solution quality, the ARISCO is shown to have better overall performance than the CLONALG and GA. In particular, when solving a problem with a nearly zero feasible ratio, the ARISCO and GA perform equally and both outperform the CLONALG. Originality/value In summary, the contributions of this paper include: this research applies the AIS approach, more precisely, the CLONALG, to the single-objective structural design optimization of AMBs; the ARISCO overall produces better AMB designs than the CLONALG and a GA developed in the previous work; in situations where a nearly zero feasible ratio occurs, the ARISCO and GA perform equally, and they both outperform the CLONALG.

AN IMMUNE-INSPIRED EVOLUTION STRATEGY FOR CONSTRAINED OPTIMIZATION PROBLEMS

2011 ◽  
Vol 20 (03) ◽  
pp. 549-561 ◽  
Author(s):  
JIANYONG CHEN ◽  
QIUZHEN LIN ◽  
LINLIN SHEN
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Clonal Selection ◽  
Global Search ◽  
Evolution Strategy ◽  
Immune Memory ◽  
Constrained Optimization Problems ◽  
The Arts ◽  
Benchmark Test Functions ◽  
Search Capability

Based on clonal selection principle, this paper proposes an immune-inspired evolution strategy (IIES) for constrained optimization problems with two improvements. Firstly, in order to enhance global search capability, more clones are produced by individuals that have far-off nearest neighbors in the less-crowed regions. On the other hand, immune update mechanism is proposed to replace the worst individuals in clone population with the best individuals stored in immune memory in every generation. Therefore, search direction can always focus on the fittest individuals. These proposals are able to avoid being trapped in local optimal regions and remarkably enhance global search capability. In order to examine the optimization performance of IIES, 13 well-known benchmark test functions are used. When comparing with various state-of-the-arts and recently proposed competent algorithms, simulation results show that IIES performs better or comparably in most cases.

Applications of Artificial Immune Systems in Agents

2011 ◽  
pp. 99-122
Author(s):  
Luis Fernando Niño Vasquez ◽  
Fredy Fernando Muñoz Mopan ◽  
Camilo Eduardo Prieto Salazar ◽  
José Guillermo Guarnizo Marín
Keyword(s):  
Immune Response ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Multi Agent Systems ◽  
Artificial Immune ◽  
Immune Systems ◽  
Agent Systems ◽  
Adaptive Immune ◽  
Multi Agent ◽  
Object Transportation

Artificial Immune Systems (AIS) have been widely used in different fields such as robotics, computer science, and multi-agent systems with high efficacy. This is a survey chapter within which single and multi-agent systems inspired by immunology concepts are presented and analyzed. Most of the work is usually based on the adaptive immune response characteristics, such as clonal selection, idiotypic networks, and negative selection. However, the innate immune response has been neglected and there is not much work where innate metaphors are used as inspiration source to develop robotic systems. Therefore, a work that involves some interesting features of the innate and adaptive immune responses in a cognitive model for object transportation is presented at the end of this chapter.

Scalable Computing Architecture for Time-Dependent Transportation Optimization Problems Based on High-Performance Computing Techniques

2019 ◽  
Vol 2673 (4) ◽  
pp. 205-216 ◽  
Author(s):  
Pengfei (Taylor) Li ◽  
Peirong (Slade) Wang ◽  
Farzana Chowdhury ◽  
Li Zhang
Keyword(s):  
Objective Function ◽  
High Performance Computing ◽  
High Performance ◽  
Optimization Problems ◽  
Dynamic Network ◽  
Alternative Formulation ◽  
High Fidelity ◽  
Objective Functions ◽  
Performance Computing ◽  
Transportation Optimization

Traditional formulations for transportation optimization problems mostly build complicating attributes into constraints while keeping the succinctness of objective functions. A popular solution is the Lagrangian decomposition by relaxing complicating constraints and then solving iteratively. Although this approach is effective for many problems, it generates intractability in other problems. To address this issue, this paper presents an alternative formulation for transportation optimization problems in which the complicating attributes of target problems are partially or entirely built into the objective function instead of into the constraints. Many mathematical complicating constraints in transportation problems can be efficiently modeled in dynamic network loading (DNL) models based on the demand–supply equilibrium, such as the various road or vehicle capacity constraints or “IF–THEN” type constraints. After “pre-building” complicating constraints into the objective functions, the objective function can be approximated well with customized high-fidelity DNL models. Three types of computing benefits can be achieved in the alternative formulation: ( a) the original problem will be kept the same; ( b) computing complexity of the new formulation may be significantly reduced because of the disappearance of hard constraints; ( c) efficiency loss on the objective function side can be mitigated via multiple high-performance computing techniques. Under this new framework, high-fidelity and problem-specific DNL models will be critical to maintain the attributes of original problems. Therefore, the authors’ recent efforts in enhancing the DNL’s fidelity and computing efficiency are also described in the second part of this paper. Finally, a demonstration case study is conducted to validate the new approach.

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