An effective hybrid optimization strategy for job-shop scheduling problems
Computers and Operations Research
Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling
Computers and Industrial Engineering
A new adaptive neural network and heuristics hybrid approach for job-shop scheduling
Computers and Operations Research
Density as the segregation mechanism in fish school search for multimodal optimization problems
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
An artificial immune system for solving production scheduling problems: a review
Artificial Intelligence Review
Hi-index | 0.00 |
A multi-modal immune algorithm is utilized for finding optimal solutions to job-shop scheduling problem emulating the features of a biological immune system. Inter-relationships within the algorithm resemble antibody molecule structure, antibody-antigen relationships in terms of specificity, clonal proliferation, germinal center, and the memory characteristics of adaptive immune responses. In addition, Gene fragment recombination and several antibody diversification schemes were incorporated into the algorithm in order to improve the balance between exploitation and exploration. Moreover, niche scheme is employed to discover multi-modal solutions. Numerous well-studied benchmark examples were utilized to evaluate the effectiveness of the proposed approach.