Automated color image edge detection using improved PCNN model
WSEAS Transactions on Computers
Immune inspired somatic contiguous hypermutation for function optimisation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
A new immune clone algorithm to solve the constrained optimization problems
WSEAS Transactions on Computers
Mobile robot path planning using polyclonal-based artificial immune network
Journal of Control Science and Engineering - Special issue on Advances in Methods for Networked and Cyber-Physical System
| Hi-index | 0.00 |
The traditional single clonal selection algorithm has a lot of disadvantages, for example, it is easy to be trapped into local optima, and it has a lot of massive redudacy iteration in its later period and inferior global search ability and so on. In this paper, a new artificial immune algorithm is proposed based on the clonal selection theory and the structure of anti-idiotype(IAAI), which is improved as follows: firstly, IAAI constructs a dynamic clonal expansion, secondly clonal mutation, thirdly dislocated line clonal recombinant, lastly clonal selection. Through the above mentioned several key steps, IAAI is improved in order to achieve the evolution of the whole antibody population, then the new algorithm can have strong search capabilities which make it reach better performance by perfoming global search and local search in many directions in the solution space. Then the global convergence of the new algorithm is analyzed from the test of several typical complex functions. The result shows that the algorithm can effectively overcome the premature problem, and improve the ability of global optimization, the speed of convergence.