By Hui Sun, Chin-Yu Yang, Chun-Wei Lin, Jeng-Shyang Pan, Vaclav Snasel, Ajith Abraham (eds.)
ISBN-10: 3319122851
ISBN-13: 9783319122854
ISBN-10: 331912286X
ISBN-13: 9783319122861
This quantity of Advances in clever structures and Computing comprises permitted papers awarded at ICGEC 2014, the eighth foreign convention on Genetic and Evolutionary Computing. The convention this 12 months used to be technically co-sponsored via Nanchang Institute of expertise in China, Kaohsiung college of utilized technology in Taiwan, and VSB-Technical collage of Ostrava. ICGEC 2014 is held from 18-20 October 2014 in Nanchang, China. Nanchang is one in every of is the capital of Jiangxi Province in southeastern China, positioned within the north-central section of the province. because it is bounded at the west by means of the Jiuling Mountains, and at the east by means of Poyang Lake, it really is recognized for its surroundings, wealthy heritage and cultural websites. as a result of its significant situation relative to the Yangtze and Pearl River Delta areas, it's a significant railroad hub in Southern China. The convention is meant as a world discussion board for the researchers and pros in all components of genetic and evolutionary computing.
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Extra resources for Genetic and Evolutionary Computing: Proceeding of the Eighth International Conference on Genetic and Evolutionary Computing, October 18-20, 2014, Nanchang, China
Sample text
The parallel strategies simply share the computation load over several processors. The sum of the computation time for all processors can be reduced compared with the single processor works on the same optimum problem. In this paper, the concepts of parallel processing and communication strategy are applied to hybrid Particle Swarm Optimization with Bat Algorithm is presented. In this new method, the several poorer particles in PSO will be replaced with best artificial bats in Bat algorithm after running some fixed iterations and on the contrary, the poorer individuals of BA will be replaced with the better particles of PSO.
Each population is initialized by BA or by PSO independently. Defined the iteration set R for executing the communication strategy. D. D is dimension of solutions and t is current iteration number. Set t = 1. 2. Evaluation: Evaluate the value of f1( ), f2( ) for both PSO and BA in each population. The evolvement of the populations is executed independently by both PSO and BA. 3. Update: Update the velocity and the positions of PSO using equation (1), and (2). Update the location and velocity of Bat in the best fitness value, which are found by the bat using equation (6), (7).
Additionally, we can get the same conclusion on performance efficiency as that given in Example 1, namely ASDIOA spends the least time to execute the process of solution search but HPSO is worst when doing so. 6 Conclusions With the increasing requirement of handling uncertain optimization problems, CCP will become increasingly popular in the field of intelligent optimization. Thus, inspired by the dynamic characteristics and mechanisms of the immune system, this work focuses on probing into a bio-inspired adaptive immune optimization algorithm for chance-constrained programming problems with any distributions.
Genetic and Evolutionary Computing: Proceeding of the Eighth International Conference on Genetic and Evolutionary Computing, October 18-20, 2014, Nanchang, China by Hui Sun, Chin-Yu Yang, Chun-Wei Lin, Jeng-Shyang Pan, Vaclav Snasel, Ajith Abraham (eds.)
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