Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
The LifeCycle Model: Combining Particle Swarm Optimisation, Genetic Algorithms and HillClimbers
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A Combined Swarm Differential Evolution Algorithm for Optimization Problems
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
EPSO - best-of-two-worlds meta-heuristic applied to power system problems
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
An approach to multimodal biomedical image registration utilizing particle swarm optimization
IEEE Transactions on Evolutionary Computation
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Particle swarm optimization (PSO) algorithm is an intelligent search method based on swarm intelligence It has been widely used in many fields because of its conciseness and easy implementation But it is also easy to be plunged into local solution and its later convergence speed is very slow In order to increase its convergence speed, nonlinear simplex method (NSM) is integrated into it, which not only can increase its later convergence speed but also can effectively avoid dependence on initial conditions of NSM In order to bring particles jump out of local solution regions, tabu search (TS) algorithm is integrated into it to assign tabu attribute to these regions, which make it with global search ability Thus the hybrid PSO algorithm is an organic composition of the PSO, NSM and TS algorithms Finally its basic operation process and optimization characteristics are analyzed through some benchmark functions and its effectiveness is also verified.