Swarm Intelligence in Engineering

Baozhen Yao, Rui Mu, Bin Yu
2013 Mathematical Problems in Engineering  
Swarm intelligence (SI) is an artificial intelligence technique based on the study of behavior of simple individuals (e.g., ant colonies, bird flocking, animal herding, and honey bees), which has attracted much attention of researchers and has also been applied successfully to solve optimization problems in engineering. However, for large and complex problems, SI algorithms consume often much computation time due to stochastic feature of the search approaches. Therefore, there is a potential
more » ... uirement to develop efficient algorithm to find solutions under the limited resources, time, and money in real-world applications. Within this context, this special issue servers as a forum to highlight the most significant recent developments on the topics of SI and to apply SI algorithms in real-life scenario. The works in this issue contain new insights and findings in this field. A broad range of topics has been discussed, especially in the following areas, benchmarking and evaluation of new SI algorithms, convergence proof for SI algorithms, comparative theoretical and empirical studies on SI algorithms, and SI algorithms for real-world application. Some works focus on the application of genetic algorithm in different area, for example, G. Ning et al. 's "Economic analysis on value chain of taxi fleet with battery-swapping mode using multiobjective genetic algorithm" presents an economic analysis model on value chain of taxi fleet with battery-swapping mode in a pilot city. A multiobjective genetic algorithm is used to solve the problem. The real data collected from the pilot city proves that the multiobjective genetic algorithm is tested as an effective method to solve this problem. B. Zhenming et al. "Direct index method of beam damage location detection based on difference theory of strain modal shapes and the genetic algorithms application" applies direct index method SMSD and the Genetic Algorithms into structural damage identification. Numerical simulation shows that the criteria of damage location detection can be obtained by strain mode difference curve through cubic spline interpolation. F. Zong et al. 's "Daily commute time prediction based on genetic algorithm" presents a joint discrete-continuous model for activity-travel time allocation by employing the ordered probit model for departure time choice and the hazard model for travel time prediction. Genetic algorithm (GA) is employed for optimizing the parameter in the hazard model. The results also show that the genetic algorithm contributes to the optimization and thus the high accuracy of the hazard model. Qu et al. 's "The optimized transport scheme of empty and heavy containers with novel genetic algorithm" proposed a model with objective maximizing the route benefits to design the transport scheme of empty and heavy containers reasonably. A novel GA is developed to solve the model. The case study about China-Europe route proves that this model can improve the liner company's benefits effectively. W. Juan et al. 's "Genetic algorithm for multiuser discrete network design problem under demand uncertainty" presents a bilevel model for discrete network design. An iterative approach including an improved genetic algorithm and a Frank-Wolfe algorithm is used to solve the bilevel model. The numerical results on the Nguyen Dupuis network show
doi:10.1155/2013/835251 fatcat:ywsswmzdhzgd5pqvkkf63bonmu