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Particle swarm optimization (PSO) as a novel intelligent optimization algorithm has been used successfully in many fields, but its application to flood hazard risk assessment is a new research topic. This paper introduces the theory and flow of application of particle swarm optimization rule mining (PSO-Miner) algorithm to flood damage risk assessment. This paper selected Beijiang River Basin, China, as study area for flood damage risk assessment based on PSO-Miner algorithm and BPANN method. The results of a case study indicate that the advantages of PSO-Miner algorithm can be summarized as follows: It does not assume an implicit assumption for processing dataset and has strong robustness; it can mine very simple assessment rules; it can have a better performance than BPANN model. So the PSO-Miner algorithm provides a new approach for flood risk assessment. [ABSTRACT FROM AUTHOR]

Many real-world optimization problems are both dynamic and multi-modal, which require an optimization algorithm not only to find as many as possible optima under a specific environment but also to track their trajectory over dynamic environments. To address this requirement, this paper investigates a memetic particle swarm algorithm for dynamic multi-modal optimization problems. Within the framework of the proposed algorithm, a new speciation method is employed to locate multiple peaks and an adaptive local search method is also hybridized to accelerate the exploitation of species generated by the speciation method. In addition, the re-initialization schemes are introduced into the proposed algorithm in order to further enhance its performance in dynamic multi-modal environments. Based on the moving peaks benchmark problems, experiments are carried out to investigate the performance of the memetic particle swarm algorithm in comparison with several state-of-the-art algorithms in the literature. The experimental results show the efficiency of our proposed algorithm for dynamic multi-modal optimization problems. [ABSTRACT FROM AUTHOR]

Considering that the global optimal solutions often locate on or near the boundary of the feasible region for many constrained optimization problems, a novel genetic algorithm was proposed in this paper. The basic idea of the proposed algorithm was to put feasible solutions and infeasible solutions into two different containers respectively. Subsequently, a new designed crossover operator (named inward-outward crossover operator) was used to a feasible solution and a infeasible solution, then a line search along a potential decent direction was used to improve the offspring so as to find a good solution on or near to the boundary of feasible region. By this search procedure, the possibility for obtaining the globally optimal solution is obviously enhanced, and similarly, the convergent speed is also strengthened. The "particle swarm mutation" inherited the advantages of Particle Swarm Optimization (PSO) algorithm and searched for the potential solution along the direction of the best current particle and the direction of the best individual of the whole swarm in the past. Selection operator retained a constant rate of infeasible solutions. Numerical results indicate that the proposed algorithm can be efficient to get global optimal solutions or near to them in smaller population and less iteration times. [ABSTRACT FROM AUTHOR]