By Zvi Retchkiman Konigsberg (auth.), Ying Tan, Yuhui Shi, Zhen Ji (eds.)
This publication and its significant other quantity, LNCS vols. 7331 and 7332, represent the complaints of the 3rd foreign convention on Swarm Intelligence, ICSI 2012, held in Shenzhen, China in June 2012. The one hundred forty five revised complete papers offered have been conscientiously reviewed and chosen from 247 submissions. The papers are geared up in 27 cohesive sections masking all significant subject matters of swarm intelligence examine and developments.
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Additional info for Advances in Swarm Intelligence: Third International Conference, ICSI 2012, Shenzhen, China, June 17-20, 2012 Proceedings, Part I
Folly, K. : Comparative Study of Population-Based Techniques for Power System Stabilizer Design. In: 15th Int. Conf. A. Folly 13. : Population-Based Incremental Learning Versus Genetic Algorithms: Iterated Prisoners Dilemma. Technical Report CSM-40, University of Essex, England (2004) 14. : The Population-Based Incremental Learning Algorithm Converges to Local Optima. Neurocomputing 69, 1772–1775 (2006) 15. : Effect of Learning Rate on the Performance of the Population-Based Incremental Learning Algorithm.
In PSO, the two acceleration coefficients are equal to 2, and Vmax = 4 . 25 . 12, i = 1, 2," n ) i =1 In the following simulations, we use binary-encoding, and the length of every variable is 15 bits. e. i = 1, 2 . All the results are the average of 200 times. The first function we use is Griewank function. x is in the interval of [ −600, 600] . The global minimum value for this function is 0 and the corresponding global optimum solution is xopt = ( x1 , x2 ," , xn ) = (100,100," ,100 ) . From Figure 1, we can see that although classic algorithms have fast convergence rate, but they all trap into local convergence.
The difficult part about finding optimal solutions to this function is that an optimization algorithm can easily be trapped in a local optimum on its way towards the global optimum. From Figure 4, we can see that GA, QGA and PSO have similar performance, but they all trap into local convergence. Our algorithm overcomes the disadvantage of local convergence and has much higher convergence value. Fig. 3. The performance of four algorithms Fig. 4. The performance of four algorithms using Schwefel function using Rastrigin function 36 4 J.