Personalized-Template-Guided Intelligent Evolutionary Algorithm

Abstract

Existing heuristic algorithms are based on inspiration sources and have not yet done a good job of basing themselves on optimization principles to minimize and utilize historical information, which may lead to low efficiency, accuracy, and stability of the algorithm. To solve this problem, a personalized-template-guided intelligent evolutionary algorithm named PTG is proposed. The core idea of PTG is to generate personalized templates to guide particle optimization. We also find that high-quality templates can be generated to guide the exploration and exploitation of particles by using the information of the population particles when the optimal value remains unchanged, the knowledge of population distribution changes, and the dimensional distribution properties of particles themselves. By conducting an ablation study and comparative experiments on the challenging CEC2022 test and CEC2005 test functions, we have validated the effectiveness of our method and concluded that the stability and accuracy of the solutions obtained by PTG are superior to other algorithms. Finally, we further verified the effectiveness of PTG through four engineering problems.

Keywords

• evolutionary computation
• engineering optimization
• optimization paradigm
• template guidance
• distribution of particle dimensions