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Techniques for Resource Sharing in Cloud Computing Platform
Published in Indrajit Pan, Mohamed Abd Elaziz, Siddhartha Bhattacharyya, Swarm Intelligence for Cloud Computing, 2020
B.L. Radhakrishnan, S. Sudhakar, R.V. Belfin, P. Karthikeyan, E. Kirubakaran, K. Martin Sagayam
The main focus of GCC is using less energy in cloud data center. A clonal selection algorithm [64] proposed to reduce the energy consumption, time, and cost. A scheduling algorithm based on multi-step heuristic proposed in [13] to reduce the CO2 emission and improve the resource utilization. When the customer request for a resource with a deadline, the request is assigned to one of the data centers, which has less environmental effects without compromising the deadline.
Hybrid Cuckoo Search with Clonal Selection for Triclustering Gene Expression Data of Breast Cancer
Published in IETE Journal of Research, 2023
P. Swathypriyadharsini, K. Premalatha
The immune system is a group of cells, tissues, and organs that function together to defend the body against foreign invaders [21]. Clonal selection depicts how antibodies behave and what they can do in the acquired immune system [22]. Clonal selection algorithm is a form of artificial immune system inspired by the clonal selection theory, which describes how B and T lymphocytes enhance their response to antigens over time, a process known as affinity maturation [23]. This algorithm concentrates on the Darwinian theory, such as selection being influenced by antigen–antibody affinity, replication by cell division, and variation by somatic hypermutation [23]. The main features of Clonal Selection Algorithm are The number of clones, which is proportional to the antibody’s affinity with respect to the antigens;The rate of mutation, which is inversely proportional to affinity
Probabilistic assessment of transport network vulnerability with equilibrium flows
Published in International Journal of Sustainable Transportation, 2021
Yu Jiang, Yi Wang, W. Y. Szeto, Andy H. F. Chow, Anna Nagurney
It is well-known that bi-level programs are inherently non-convex and challenging to solve (Ban et al., 2006; Meng et al., 2001; Meng & Yang, 2002). Concerning the solution method, metaheuristics are gaining popularity in handling bi-level transportation optimization problems, owing to their insensitivity to the mathematical property of the problems. A number of metaheuristics or their hybrids have been extensively applied for tackling bi-level transportation optimization problems, including the Genetic Algorithm (GA) (e.g., Unnikrishnan & Lin, 2012), Ant Colony Optimization (e.g., Vitins & Axhausen, 2009), Chemical Reaction Optimization (e.g., Szeto et al., 2014), and Artificial Bee Colony (e.g., Jiang et al., 2013; Szeto & Jiang, 2012, 2014). This study adopts an evolutionary algorithm named the Clonal Selection Algorithm (CSA) to solve the proposed bi-level optimization problem. The CSA is inspired by Burnet’s clonal selection theory, which exploits the diversity and learning properties of the acquired immune system of vertebrates (Brownlee, 2007). It has been reported that the algorithm is capable of solving several benchmark problems in machine learning and optimization (De Castro & Von Zuben, 2000, 2002) and the algorithm performs better than other heuristics such as GA in some cases (Ulutas & Kulturel-Konak, 2011). In the field of transportation research, to the best of our knowledge, only Miandoabchi, Farahani, & Szeto, et al. (2012), Miandoabchi, Farahani, Dullaert, et al. (2012), and Miandoabchi et al. (2013) applied the algorithm to solve their studied bi-level transportation network design problems, which motivates us to investigate its capability for solving the proposed bi-level optimization problem.