A cooperative particle swarm optimization with constriction factor based on simulated annealing

As many engineering optimization problems are rather complicated, it is usually necessary to search the optimal solution in a complex and huge search space. When faced with these large-scale problems, conventional optimization algorithms need to traverse the entire search space and it is impossible for them to finish the search within polynomial time. Moreover, it can't meet requirements in terms of computation velocity, convergence and sensitivity to initial value. So, it is very difficult to apply them to engineering optimization problems. Swarm intelligence methods simulate the collective behaviors of social creatures in the nature and they come from the relationship between the community formed by simple individuals and the environment as well as the interactions between the individuals. A single individual can only perform simple tasks, but the population formed by single individuals can fulfill complex tasks. Such intelligence presented by such population is called swarm intelligence. Due to the limitations of existing optimization algorithms, it is usually impractical to obtain excellent computational performance with only one optimization algorithm. In consideration of the jumping property of simulated annealing, it is not easy to get trapped into local minimum and it has strong local search capability near the optimal value and fast convergence velocity. This paper combines it with particle swarm optimization, proposes a cooperative particle swarm optimization with constriction factor based on simulated annealing (SA-CPSO), offers guidelines on selection of related parameters and dynamically adjusts the particle velocity according to its movement track. In this way, it improves the convergence velocity of the algorithm by improving the spatial search ability of the particle so as to make the particle accept the solution which makes the fitness of the objective function "better" as well as the solution that makes the said fitness "worse" at a certain probability during the flight of the particle. The experiment shows that the SA-CPSO improves the diversity of the particle and enhances its ability to get rid of locally optimal solutions. So, SA-CPSO is not easy to be trapped into local optimum and it has stronger ability of global optimization, a faster convergence velocity and higher convergence accuracy.