Lining Zhang

Baldwinian learning in clonal selection algorithm for optimization

Artificial immune systems are a kind of new computational intelligence methods which draw inspiration from the human immune system. Most immune system inspired optimization algorithms are based on the applications of clonal selection and hypermutation, and known as clonal selection algorithms. These clonal selection algorithms simulate the immune response process based on principles of Darwinian evolution by using various forms of hypermutation as variation operators. The generation of new individuals is a form of the trial and error process. It seems very wasteful not to make use of the Baldwin effect in immune system to direct the genotypic changes. In this paper, based on the Baldwin effect, an improved clonal selection algorithm, Baldwinian Clonal Selection Algorithm, termed as BCSA, is proposed to deal with optimization problems. BCSA evolves and improves antibody population by four operators, clonal proliferation, Baldwinian learning, hypermutation, and clonal selection. It is the first time to introduce the Baldwinian learning into artificial immune systems. The Baldwinian learning operator simulates the learning mechanism in immune system by employing information from within the antibody population to alter the search space. It makes use of the exploration performed by the phenotype to facilitate the evolutionary search for good genotypes. In order to validate the effectiveness of BCSA, eight benchmark functions, six rotated functions, six composition functions and a real-world problem, optimal approximation of linear systems are solved by BCSA, successively. Experimental results indicate that BCSA performs very well in solving most of the test problems and is an effective and robust algorithm for optimization.

Solving multiobjective clustering using an immune-inspired algorithm

In this study, we introduced a novel multiobjective optimization algorithm, Nondominated Neighbor Immune Algorithm (NNIA), to solve the multiobjective clustering problems. NNIA solves multiobjective optimization problems by using a nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators and elitism. The main novelty of NNIA is that the selection technique only selects minority isolated nondominated individuals in current population to clone proportionally to the crowding-distance values, recombine and mutate. As a result, NNIA pays more attention to the less-crowded regions in the current trade-off front. The experimental results on seven artificial data sets with different manifold structure and six real-world data sets show that the NNIA is an effective algorithm for solving multiobjective clustering problems, and the NNIA based multiobjective clustering technique is a cogent unsupervised learning method.

Differential immune clonal selection algorithm

Based on the antibody clonal selection theory of immunology and differential evolution paradigm, we put forward a novel artificial immune system algorithm, differential immune clonal selection algorithm, termed as DICSA. The DICSA evolves and improves the population by clonal proliferation operator, differential mutation operator, differential crossover operator and (mu+lambda)-selection operator. In experiments, six composition functions are used to validate the performance of the DICSA. The results confirm its potential to solve the global optimization problems.

Improved real-valued clonal selection algorithm based on a novel mutation method

Chaos theory describes complex motion and the dynamics of nonlinear systems. As a complex nonlinear system, the immune system is chaotic. This paper introduces chaos into the clonal selection algorithm by a novel mutation method, self-adaptive chaotic mutation (SACM). In detail, based on the logistic chaotic sequence, SACM extracts antibodys affinity and distribution to adjust the mutation scale. Compared with the clonal selection algorithm using random mutation and standard genetic algorithm, the improved clonal selection algorithm can enhance the precision and stability, and overcome prematurity effectively with a high convergence speed.

Improved Clonal Selection Algorithm based on Lamarckian local search technique

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 compared LCSA with the clonal selection algorithm (CSA) in solving twenty benchmark problems to test the performance of LCSA. The results demonstrate that LCSA is effective and efficient in solving numerical optimization problems.

Solving traveling salesman problems by artificial immune response

This paper introduces a computational model simulating the dynamic process of human immune response for solving Traveling Salesman Problems (TSPs). The new model is 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 Aldenotes the dynamic algorithm describing how the reaction rules are applied to antibody population. The set of immunodominance rules, the set of clonal selection rules, and a dynamic algorithm TSP-PAISA are designed. The immunodominance rules construct an immunodominance set based on the prior knowledge of the problem. The antibodies can gain the immunodominance from the set. The clonal selection rules strengthen these superior antibodies. The experiments indicate that TSP-PAISA is efficient in solving TSPs and outperforms a known TSP algorithm, the evolved integrated self-organizing map.

Improved Clonal Selection Algorithm based on Baldwinian learning

In this paper, based on Baldwin effect, an improved clonal selection algorithm, Baldwin clonal selection algorithm, termed as BCSA, is proposed to deal with complex multimodal optimization problems. BCSA evolves and improves antibody population by three operations: clonal proliferation operation, Baldwinian learning operation and clonal selection operation. By introducing Baldwin effect, BCSA can make the most of experience of antibodies, accelerate the convergence, and obtain the global optimization quickly. In experiments, BCSA is tested on four types of functions and compared with the clonal selection algorithm and other optimization methods. Experimental results indicate that BCSA achieves a good performance, and is also an effective and robust technique for optimization.

Optimal approximation of linear systems by an improved Clonal Selection Algorithm

Based on the theory of clonal selection in immunology, by introducing Baldwin effect, an improved clonal selection algorithm, termed as Baldwin clonal selection algorithm (BCSA), is proposed to solve the optimal approximation of linear systems. For engineering computing, the novel algorithm adopts three operations to evolve and improve the population: clonal proliferation operation, Baldwinian learning operation and clonal selection operation. The experimental study on the optimal approximation of a stable linear system and an unstable one show that the approximate models searched by the new algorithm have better performance indices than those obtained by some existing algorithms including the differential evolution algorithm, multi-agent genetic algorithm and artificial immune response algorithm.