Cultural transmission based multi-objective evolution strategy for evolutionary multitasking

作者：Xu, Zhiwei; Liu, Xiaoming; Zhang, Kai^*; He, Juanjuan

来源：Information Sciences, 2022, 582: 215-242.

DOI：10.1016/j.ins.2021.09.007

• Multi-objective evolutionary algorithm
• Evolutionary multitasking
• Multi-task optimization
• Cultural transmission
• Evolution strategy

In recent years, many efficient evolutionary multitasking (EMT) algorithms have been pro-posed to solve multi-objective multi-task optimization problems. However, EMT algo-rithms often face negative transfer problems. In this paper, a novel multi-objective evolution strategy, called CT-EMT-MOES, is proposed based on a cultural transmission the-ory for solving multi-objective multitask optimization problems. First, two evolutionary operators inspired by cultural transmission theory are proposed. The elite-guided variation strategy can transfer the information from the current Pareto front to all individuals and guide the population to quickly converge. The horizontal cultural transmission strategy can efficiently transfer information from the source task to the target task. Second, to solve the negative transfer problem, an adaptive information transfer strategy is proposed to adaptively adjust the probability of an information transfer. Third, the proposed algorithm can gain a Pareto front with good convergence and diversity by utilizing a smaller popula-tion size and fewer computing resources. As a result, the proposed algorithm can effec-tively utilize the implicit similarity and complementarity between simultaneous optimized tasks to improve the overall convergence efficiency and reduce a negative trans-fer. Finally, comprehensive experimental results show that the proposed algorithm can achieve a better performance compared with previous state-of-the-art multi-objective EMT algorithms.