Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A new approach to solve hybrid flow shop scheduling problems by artificial immune system
Future Generation Computer Systems - Special issue: Computational science of lattice Boltzmann modelling
Immune algorithms-based approach for redundant reliability problems with multiple component choices
Computers in Industry - Special issue: Application of genetics algorithms in industry
An artificial immune system architecture for computer securityapplications
IEEE Transactions on Evolutionary Computation
Application of two ant colony optimisation algorithms to water distribution system optimisation
Mathematical and Computer Modelling: An International Journal
Journal of Intelligent Manufacturing
Expert Systems with Applications: An International Journal
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Immune algorithm (IA) is a set of computational systems inspired by the defense process of the biological immune system. This study proposed an optimization procedure based on IA framework to optimize the designs of water distribution networks. A modified IA (mIA) procedure, which employs genetic algorithm (GA) to briefly screen initial antibody repertoires for IA, is also developed. The well-known benchmark instance, New York City Tunnel (NYCT) problem, is utilized as a case study to evaluate the optimization performance of IA and mIA. The least-cost designs of NYCT obtained by IA and mIA are compared with those by GA and fast messy GA previously published in the literature. The results of comparison reveal that IA and mIA are able to find the optimal solutions of NYCT with higher computational efficiency (less number of evaluations) than GA and fmGA. Notable performance enhancement is observed in mIA, indicating that the combination of GA can significantly improve the optimization performance of IA.