Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
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In order to cope with the multidimensional non-linear optimization problems which involved a great number of discrete variables and continuous variables, a self-organizing learning algorithm (SOLA) was proposed in this paper, in which the parallel search strategy of genetic algorithm(GA) and the serial search strategy of simulated annealing (SA) were involved. Additionally, the learning principle of particle swarm optimization(PSO) and the tabu search strategy were adopted into the SOLA, wherein the integrated frame work was different from traditional optimization methods and the interactive learning strategy was involved in the process of random searching. SOLA was divided into two handling courses: self-learning and interdependent-learning. The local optimal solution would be achieved through self-learning in the process of local searching and the global optimal solution would be achieved via the interdependent learning based on the information sharing mechanism. The search strategies and controlled parameters of SOLA were adaptively fixed according to the feedback information from interactive learning with the environments thus SOLA is self-organizing and intelligent. Experiments for the multidimensional testbed functions showed that SOLA was far superior to traditional optimization methods at the robustness and the global search capability while the solution space ranged from low-dimensional space to the high-dimensional space.