Jiekai Wang

We propose an improved immune clone optimisation algorithm which is based on elite bank mechanism( ICOAEB) for constrained optimisation problem.This algorithm utilises the elite bank mechanism to dynamically store the superior individuals of father generation during the iteration process,implements the multi-generation memory of superior individuals,and thereby improves algorithm's optimisation ability.ICOAEB also uses the cataclysm operator to disturb the running process of the algorithm so as… Show more

We propose an improved immune clone optimisation algorithm which is based on elite bank mechanism( ICOAEB) for constrained optimisation problem.This algorithm utilises the elite bank mechanism to dynamically store the superior individuals of father generation during the iteration process,implements the multi-generation memory of superior individuals,and thereby improves algorithm's optimisation ability.ICOAEB also uses the cataclysm operator to disturb the running process of the algorithm so as to get rid of the slow iteration state and to prevent local convergence of the algorithm.By testing five constrained optimisation functions,the experimental results show that the solution accuracy and stability of ICOAEB is higher,and it can well solve the constrained optimisation problem.Finally,correlated experiment results and analyses are given for the selection of two important parameters which can affect the performance of the algorithm. Show less

An improved evolutionary algorithm (SCAGA) is proposed in this paper for solving optimization
problem. In order to control genetic operations in an effective range, the new algorithm regulate both of the
crossover probability and mutation probability with the iteration process. In addition, SCAGA presents a
new crossover strategy that restricts the cross of the chromosomes to some extent to protect good genes
schema. We also perform the schema theorem on the algorithm process to… Show more

An improved evolutionary algorithm (SCAGA) is proposed in this paper for solving optimization
problem. In order to control genetic operations in an effective range, the new algorithm regulate both of the
crossover probability and mutation probability with the iteration process. In addition, SCAGA presents a
new crossover strategy that restricts the cross of the chromosomes to some extent to protect good genes
schema. We also perform the schema theorem on the algorithm process to analyze the working
mechanism of SCAGA, and we conclude that the new algorithm is effective. According to experiment
results for some test functions and TSP problems, SCAGA have a high performance in both constrained
unconstrained optimization problems. Show less