Artificial memory optimization

作者：Huang, Guang-qiu^*

来源：Applied Soft Computing, 2017, 61: 497-526.

DOI：10.1016/j.asoc.2017.08.021

• Nature-inspired algorithms
• Swarm intelligence optimization
• Metaheuristic
• Artificial memory-inspired optimization
• Memory

To solve some complicated optimization problems, an artificial memory optimization (AMO) is constructed based on the human memory mechanism. In AMO, a memory cell is used to trace an alternative solution of a problem to be solved; memorizing and forgetting rules of the human memory mechanism are used to control state transition of each memory cell; the state of a memory cell consists of two components, one is the solution state which associates with an alternative solution being traced; another is the memory state which associates with the memory information resulting from tracing results, where the memory residual value (MRV) is stored; the states of memory cells are divided into three types: instantaneous, short- and long-term memory state, each of which can be strengthened or weakened by accepted stimulus strength. If the solution state of a memory cell has transferred to a good position, its MRV will increase, and then the memory cell is not easily to be forgotten; when the solution state of a memory cell is at sticky state, its MRV will decrease until the memory cell is forgotten; this will effectively prevent invalid iteration. In the course of evolution, a memory cell may strive to evolve from the instantaneous, short-term memory state to long-term memory state, it makes search to be various. Because AMO has 6 operators at the curent version, it has wider adaptability to solve different types of optimization problems. Besides, these operators are automatically dispatched according to their executing efficiency. Results show that AMO possesses of strong search capability and high convergence speed when solving some complicated function optimization problems.

• 出版日期2017-12
• 单位西安建筑科技大学