Artificial memory-based optimization

作者：Huang Guang Qiu; Li Tao; Lu Qiu Qin

来源：Systems Engineering-Theory & Practice, 2014, 34(11): 2900-2912.

• Alternative solutions
• Global conver-gence
• High-dimensional optimization
• Intelligent optimization
• Intelligent optimization algorithm
• Optimization problems
• Stability condition
• Stochastic matrices

To solve complicated optimization problems (OP), the artificial memory-based optimization (AMO) with global convergence is constructed based on memory principles (MP). In the algorithm, each memory cell is just an alternative solution of OP; the memorizing and forgetting rules of MP are used to control transition of states of each memory cell; a memory cell's state consists of the state describing index associated with an alternative solution and the residual memory which is divided into three memory states such as instantaneous, short and long-term memory, each of which is strengthened or weakened by accepted stimulus strength; attenuation speed of instantaneous, short and long-term memory is from quick to slow, a memory cell that its residual memory is lower than a threshold is forgotten and then discarded. During evolution process, a memory cell's transferring from one state to another realizes the search for the optimum solution. The algorithm associates alternative solutions with memory, enabling alternative solutions to be classified automatically based on their quality; because the alternative solutions staying at long-term memory state have good quality, they transfer state values of some variables to the corresponding variables of the alternative solutions with poor quality, making their quality be improved; when alternative solutions staying at different states exchange state information of variables, only a small part of variables are dealt with, both the states of a large part of variables in these alternative solutions keep unchanged, and their quality can be improved, and also the number of variables to be processed decreases greatly, it can substantially improve convergence speed of the algorithm for high-dimensional optimization problems; as evolution proceeds, the alternative solutions with poorer quality will continue to be forgotten, the number of the alternative solutions to be processed will continue to decrease, therefore the convergence speed of the algorithm will become faster and faster as time lapses. The stability condition of a reducible stochastic matrix is applied to prove the global convergence of the algorithm. The case study shows that the algorithm has advantages of high speed of convergence and high accuracy of optimum solutions when comparing with the existed population-based intelligent optimization algorithms.

• 出版日期2014