Evolutionary Optimization Framework Based on Transfer Learning of Similar Historical Information

Most of existing evolutionary algorithms search optimal solutions from zero initial information of a given problem. Because of the lacking a mechanism to use historical information, this must waste computing resources to some extent when solving a problem similar to old ones. This paper extends the idea of transfer learning to the field of evolutionary optimization, and studies a new evolutionary optimization framework based the transfer learning of similar historical information. To improve the search efficiency of the population, the proposed framework finds out the history problem from the model library, which matches current new problem, and transfers the knowledge of the historical problem into optimization process of the new problem. First, a maximum mean discrepancy indicator based on multi-distribution estimation is defined to evaluate the matching degree between a new problem and historical models. Secondly, the knowledge of matched historical problem is transferred into the new problem, and a new initialization strategy of the population based on matching degree is given to accelerate the search speed of the algorithm. Next, a preservation strategy based on iterative clustering is presented to save good information generated during the evolutionary process, for updating the model library. Finally, embedding an adaptive bare-bones particle swarm optimization (PSO) into the proposed framework, a bare-bones PSO algorithm based on the transfer learning of similar historical information is presented. Testing on several improved typical functions, experimental results show that the proposed transfer strategy accelerates the search process of the particle swarm, and significantly improve the convergence speed and the search efficiency of the proposed PSO algorithm. Copyright © 2021 Acta Automatica Sinica. All rights reserved.