A simulated annealing-based immune optimization method (original) (raw)

A Hybrid Artificial Immune Optimization Method

International Journal of Computational Intelligence Systems, 2009

This paper proposes a hybrid optimization method based on the fusion of the Simulated Annealing (SA) and Clonal Selection Algorithm (CSA), in which the SA is embedded in the CSA to enhance its search capability. The novel optimization algorithm is also employed to deal with several nonlinear benchmark functions as well as a practical engineering design problem. Simulation results demonstrate the remarkable advantages of our approach in achieving the diverse optimal solutions and improved convergence speed.

Clonal selection: an immunological algorithm for global optimization over continuous spaces

Journal of Global Optimization

In this research paper we present an immunological algorithm (IA) to solve global numerical optimization problems for high-dimensional instances. Such optimization problems are a crucial component for many real-world applications. We designed two versions of the IA: the first based on binary-code representation and the second based on real values, called opt-IMMALG01 and opt-IMMALG, respectively. A large set of experiments is presented to evaluate the effectiveness of the two proposed versions of IA. Both opt-IMMALG01 and opt-IMMALG were extensively compared against several nature inspired methodologies including a set of Differential Evolution algorithms whose performance is known to be superior to many other bio-inspired and deterministic algorithms on the same test bed. Also hybrid and deterministic global search algorithms (e.g., DIRECT, LeGO, PSwarm) are compared with both IA versions, for a total 39 optimization algorithms.The results suggest that the proposed immunological algorithm is effective, in terms of accuracy, and capable of solving large-scale instances for well-known benchmarks. Experimental results also indicate that both IA versions are comparable, and often outperform, the stateof-the-art optimization algorithms.

An immune inspired-based optimization algorithm: Application to the traveling salesman problem

2007

The clonal selection is a mechanism used by the natural immune system to select cells that recognize the antigens to proliferate. The proliferated cells are subject to an affinity maturation process, which improves their affinity to the selective antigens. The concept of clonal selection is a vitally important one to the success of the human immune system, and it provides an excellent example of the principles of selection at work. The Positive and negative selection is another interesting mechanism in the immune system that work together to both retain cells that recognize the self peptides, while also removing cells that recognize any self peptides. In this paper, a cloning-based algorithm inspired by the clonal and the positive/negative selection mechanism of the natural immune system is presented. This algorithm is inherently parallel and the cloning strategy employs greedy criteria which lends to an adaptive approach. The well known TSP is used to illustrate the approach with experimental comparison with Ant approach. Simulations demonstrate that this approach generates good solutions to traveling salesman problem and greatly improve the convergence speed compared to the Ant-based optimization approach.

An Immuno-Genetic Hybrid Algorithm

International Journal of Computers Communications & Control, 2009

The construction of artificial systems by drawing inspiration from natural systems is not a new idea. The Artificial Neural Network (ANN) and Genetic Algorithms (GAs) are good examples of successful applications of the biological metaphor to the solution of computational problems. The study of artificial immune systems is a relatively new field that tries to exploit the mechanisms of the natural immune system (NIS) in order to develop problem- solving techniques. In this research, we have combined the artificial immune system with the genetic algorithms in one hybrid algorithm. We proposed a modification to the clonal selection algorithm, which is inspired from the clonal selection principle and affinity maturation of the human immune responses, by hybridizing it with the crossover operator, which is imported from GAs to increase the exploration of the search space. We also introduced the adaptability of the mutation rates by applying a degrading function so that the mutation rates ...

A Human-Simulated Immune Evolutionary Computation Approach

Lecture Notes in Computer Science, 2012

Premature convergence to local optimal solutions is one of the main difficulties when using evolutionary algorithms in real-world optimization problems. To prevent premature convergence and degeneration phenomenon, this paper proposes a new optimization computation approach, humansimulated immune evolutionary algorithm (HSIEA). Considering that the premature convergence problem is due to the lack of diversity in the population, the HSIEA employs the clonal selection principle of artificial immune system theory to preserve the diversity of solutions for the search process. Mathematical descriptions and procedures of the HSIEA are given, and four new evolutionary operators are formulated which are clone, variation, recombination, and selection. Two benchmark optimization functions are investigated to demonstrate the effectiveness of the proposed HSIEA.

Surrogate-Assisted Artificial Immune Systems for Expensive Optimization Problems

Evolutionary Computation, 2009

When an animal is exposed to antigens an efficient immune response is developed in order to defend the organism where specific antibodies are produced to combat them. The best antibodies multiply (cloning) and are improved (hypermutation and replacement) while new antibodies, produced by the bone marrow, are generated. Thus, if the organism is again attacked by the same antigen a quicker immune response takes place. This scheme of adaptation is known as clonal selection and affinity maturation by hypermutation or, more simply, clonal selection . Computational methods inspired by the biological immune system are called Artificial Immune Systems (AISs). Immune-inspired algorithms have found applications in many domains. One of the most important area, the optimization, is a mathematical principle largely applied to design and operational problems in all types of engineering, as well as a tool for formulating and solving inverse problems such as parameter identification in scientific and engineering situations. When applied to optimization problems, the AISs are stochastic populational search methods which do not require a continuous, differentiable, or explicit objective function, and do not get easily trapped in local optima. However, the AISs, as well as other nature-inspired techniques, usually require a large number of objective function evaluations in order to reach a satisfactory solution. As modern problems have lead to the development of increasingly complex and computationally expensive simulation models, this becomes a serious drawback to their application in several areas such as Computational Structural Mechanics, Reservoir Simulation, Environmental Modeling, and Molecular Dynamics. Thus, a good compromise between the number of calls to the expensive simulation model and the quality of the final solutions must often be established. A solution to this problem is to modify the search process in order to obtain either a reduction on the total computational cost or an increase in the efficiency of the optimization procedure. The solution considered here is the use of a surrogate model (or metamodel), which provides an approximation of the objective function, replacing the computationally intensive original simulator evaluation. Additionally, the surrogate model can help to smooth out the objective function landscape, and facilitate the optimization process. The idea of reducing the computation time or improving the solutions performing less computationally expensive function evaluations can be found in the evolutionary computation literature

A New Method for Fastening the Convergence of Immune Algorithms Using an Adaptive Mutation Approach

Journal of Signal and Information Processing, 2012

This paper presents a new adaptive mutation approach for fastening the convergence of immune algorithms (IAs). This method is adopted to realize the twin goals of maintaining diversity in the population and sustaining the convergence capacity of the IA. In this method, the mutation rate (p m) is adaptively varied depending on the fitness values of the solutions. Solutions of high fitness are protected, while solutions with sub-average fitness are totally disrupted. A solution to the problem of deciding the optimal value of p m is obtained. Experiments are carried out to compare the proposed approach to traditional one on a set of optimization problems. These are namely: 1) an exponential multi-variable function; 2) a rapidly varying multimodal function and 3) design of a second order 2-D narrow band recursive LPF. Simulation results show that the proposed method efficiently improves IA's performance and prevents it from getting stuck at a local optimum.

A review of the clonal selection algorithm as an optimization method

The artificial immune system (AIS) is a new optimization technique which mimics the defence system of animal organisms against pathogens. This paper represents a review of the clonal selection theory (CLONALG), under the roof of AIS. A biological background has been introduced to introduce to the way the CLONALG works in engineering studies. The optimization procedure is presented with a simulation example to illustrates CLONALG optimization process.

Evolutionary artificial immune system optimization

Proceedings of the …, 2010

In this paper, it is presented a new evolutionary algorithm of advanced optimization based in the technique of Artificial Immune Systems and in the Principles of Game Theory, more specifically, in the inclusion of evolutionary characteristics and a phase called "Social Interaction" in the algorithm AISO. In this way some theoretical aspects are presented, the new algorithm proposal and finally some simulation and comparison to Classical Genetic Algorithm and Genetic Algorithm with Social Interaction are made to the Traveling Salesman Problem.