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2 2017

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An analytical solution for dynamic response of water barrier subjected to strong shock waves caused by an underwater explosion to dams

Lu L., Zou D., Zhu Y., Dong Y., Zuo C., Wu Y.

Polish Maritime Research

2017

S 2

111--117

Shock waves arriving at a dam site are close to plane waves when the center of an underwater explosion is far from the dam site. In general, the wave pressure is calculated with COLE empirical formula. The COLE formula is a negative exponential function with respect to time. In this paper, a new analytical solution algorithm is proposed, which does not require the use of step-by-step time integration. In Comparison with the step-by-step time integration, the proposed algorithm requires relatively less calculation and avoids high-frequency oscillation. Furthermore, the vertical upstream surface and the sloping upstream surface in two types of the dams are analyzed in this paper. The research results indicate that the analytical solution can be applied for a dam with a vertical upstream surface. However, because the upstream face of a dam is inclined, the analytical solution can be obtained only for dams that are at lower height. Whenever the height of a dam is higher, then no analytical solution can be obtained, and only the use of step-by-step time integration can obtain a solution.

Stochastic fractal based multiobjective fruit fly optimization

Zuo C., Wu L., Zeng Z. F., Wei H. L.

International Journal of Applied Mathematics and Computer Science

2017

Vol. 27, no. 2

417--433

The fruit fly optimization algorithm (FOA) is a global optimization algorithm inspired by the foraging behavior of a fruit fly swarm. In this study, a novel stochastic fractal model based fruit fly optimization algorithm is proposed for multiobjective optimization. A food source generating method based on a stochastic fractal with an adaptive parameter updating strategy is introduced to improve the convergence performance of the fruit fly optimization algorithm. To deal with multiobjective optimization problems, the Pareto domination concept is integrated into the selection process of fruit fly optimization and a novel multiobjective fruit fly optimization algorithm is then developed. Similarly to most of other multiobjective evolutionary algorithms (MOEAs), an external elitist archive is utilized to preserve the nondominated solutions found so far during the evolution, and a normalized nearest neighbor distance based density estimation strategy is adopted to keep the diversity of the external elitist archive. Eighteen benchmarks are used to test the performance of the stochastic fractal based multiobjective fruit fly optimization algorithm (SFMOFOA). Numerical results show that the SFMOFOA is able to well converge to the Pareto fronts of the test benchmarks with good distributions. Compared with four state-of-the-art methods, namely, the non-dominated sorting generic algorithm (NSGA-II), the strength Pareto evolutionary algorithm (SPEA2), multi-objective particle swarm optimization (MOPSO), and multiobjective self-adaptive differential evolution (MOSADE), the proposed SFMOFOA has better or competitive multiobjective optimization performance.