Research On Multi-objective Clonal Selection Algorithm Chemical And Its Applications

Clonal selection mechanism of B cells is one of the important approaches to search antibodies in immune system, as well as one of important factors to generate diversity of antibodies. It has strong optimization ability. Based on this mechanism, a clonal selection algorithm for multi-modal function optimization has been proposed by formulating suitable clonal selection operators and introducing a new niching technology in this paper. The algorithm is applied to the optimizations of typical multi-modal functions, and the simulations reveal its simplicity and effectiveness.Multi-objective optimization problems have an optimal solution set, but not only an optimal solution. These kinds of problems always have large and complex search space.Using traditional methods to solve these problems may result in high time-complex. A multi-objective clonal selection algorithm was proposed based on the clonal selection principle in the immune system. And we expand the application of clonal selection algorithm, which has been used in solving single objective optimization problems, to the solving of multi-objective optimization. Only some Pareto optimal solutions are selected for further evolutionary operation in the algorithm. The Pareto optimal solutions are reserved in an external memory set which is renewed in each generation, and a simple mechanism is used to maintain a good spread of solutions. It is shown by experimental results that the method can reach the Pareto optimal front very quickly and retain the better diversity of the solutions.In order to test the practicability of the multi-objective clonal selection algorithm, we use it to design RBF neural network and propose a structure selection method for RBF neural networks based on multi-objective clonal selection algorithm. In this algorithm, we consider the work as multi-objective optimization problem on complexity of structure and approach accuracy of network, and use multi-objective clonal selection algorithm to obtain the Pareto optimal solution set of the problem. At last, some numerical simulation results show its validity.