Search

The physical flame characteristics in the burning process are analyzed in detail. Combined with static and dynamic flame features, a flame detection algorithm based on multi-feature fusion is designed to quickly recognize fire flame using the theory of the degree of belief. The experiments show that the processing rate of the algorithm is as high as 25 frames/s without any manual adjustments. In complex situations, the algorithm remains with stronger anti-jamming ability and higher accuracy, for example, the suspected probability of street lamps and automotive lamps is lower than 10%. From comparing results, the video-based detection algorithm has the good capabilities. [ABSTRACT FROM AUTHOR]

Inspired by complementary strategies, a new fault diagnostic method, which integrates with the Adaptive Resonance Theory (ART) and Artificial Immune Network (AIN), is proposed in this paper. With the help of clustering of ART neural network, the vaccines that image features of data set are extracted effectively, and then an AIN named aiNet is adopted to realize data compression. Finally the memory antibodies optimized by aiNet can be used to recognize each feature of original dataset and to realize fault diagnosis. The experimental results show that the approach is useful and efficient for the fault diagnosis of the multilevel reciprocating compressor. [ABSTRACT FROM AUTHOR]

Inspired by the mechanism of small-world phenomenon, some small-world optimization operators, mainly including the local short-range searching operator and random long-range searching operator, are constructed in this paper. And a new optimization algorithm, Small-World Optimization Algo-rithm (SWOA) is explored. Compared with the corresponding Genetic Algorithms (GAs), the simulation experiment results of some complex functions optimization indicate that SWOA can enhance the diversity of the population, avoid the prematurity and GA deceptive problem to some extent, and have the high convergence speed. SWOA is shown to be an effective strategy to solve complex tasks. [ABSTRACT FROM AUTHOR]

A quaternion model of artificial immune response (AIR) is proposed in this paper. The model abstracts four elements to simulate the process of immune response, namely, antigen, antibody, rules of interaction among antibodies, and the drive algorithm describing how the rules are applied to antibodies. Inspired by the biologic immune system, we design the set of rules as three subsets, namely, the set of clonal selection rules, the set of immunological memory rules, and the set of immunoregulation rules. An example of the drive algorithm is given and a sufficient condition of its convergence is deduced. [ABSTRACT FROM AUTHOR]

Based on the Antibody Clonal Selection Theory and the dynamic process of immune response, a novel Immune Forgetting Multiobjective Optimization Algorithm (IFMOA) is proposed. IFMOA incorporates a Pareto-strength based antigen-antibody affinity assignment strategy, a clonal selection operation, and a technique simulating the progress of immune tolerance. The comparison of IFMOA with other two representative methods, Multi-objective Genetic Algorithm (MOGA) and Improved Strength Pareto Evolutionary Algorithm (SPEA2), on different test problems suggests that IFMOA extends the searching scope as well as increasing the diversity of the populations, resulting in more uniformly distributing global Pareto optimal solutions and more integrated Pareto fronts over the tradeoff surface. [ABSTRACT FROM AUTHOR]

In this paper, Immune Clonal Selection theory and Lamarckism are integrated to form a new algorithm, Lamarckian Polyclonal Programming Algorithm (LPPA), for solving the global numerical optimization problem. The idea that Lamarckian evolution described how organism can evolve through learning, namely the point of "Gain and Convey" is applied, then this kind of learning mechanism is introduced into Adaptive Polyclonal Programming Algorithm (APPA). In the experiments, ten benchmark functions are used to test the performance of LPPA, and the scalability of LPPA along the problem dimension is studied with great care. The results show that LPPA achieves a good performance when the dimensions are increased from 20-10,000. Moreover, even when the dimensions are increased to as high as 10,000, LPPA still can find high quality solutions at a low computation cost. Therefore, LPPA has good scalability and is a competent algorithm for solving high dimensional optimization problems. [ABSTRACT FROM AUTHOR]

A new algorithm based on antibody immunodominance (AIDA) for TSP is explored. The main content of this paper is to explore how to produce the set of immunodominance and the superior antibodies. The experience proves that the algorithm has higher convergence speed and better solution compared with the corresponding genetic algorithm and is fit to solving complex problems. [ABSTRACT FROM AUTHOR]

A novel artificial immune system algorithm, Immune Forgetting Clonal Programming Algorithm (IFCPA), is put forward. The essential of the clonal selection inspired operations is producing a variation population around the antibodies according to their affinities, and then the searching area is enlarged by uniting the global and local search. With the help of immune forgetting inspired operations, the new algorithm abstract certain antibodies to a forgetting unit, and the antibodies of clonal forgetting unit do not participate in the successive immune operations. Decimal coding with limited digits makes IFCPA more convenient than other binary-coded clonal selection algorithms in large parameter optimization problems. Special mutation and recombination methods are adopted in the antibody population's evolution process of IFCPA in order to reflect the process of biological antibody gene operations more vividly. Compared with some other Evolutionary Programming algorithms such as Breeder Genetic Algorithm, IFCPA is shown to be an evolutionary strategy which has the ability for solving complex large parameter optimization problems, such as high-dimensional Function Optimizations, and has a higher convergence speed. [ABSTRACT FROM AUTHOR]

Based on Lamarckism and Immune Clonal Selection Theory, Lamarckian Clonal Selection Algorithm (LCSA) is proposed in this paper. In the novel algorithm, the idea that Lamarckian evolution described how organism can evolve through learning, namely the point of "Gain and Convey" is applied, then this kind of learning mechanism is introduced into Standard Clonal Selection Algorithm (SCSA). Through the experimental results of optimizing complex multimodal functions, compared with SCSA and the relevant evolutionary algorithm, LCSA is more robust and has better convergence. [ABSTRACT FROM AUTHOR]