An Enhanced Search Direction-Based Knowledge Transfer for Multiobjective Many-Tasking Evolutionary Optimization

This paper proposes a multiobjective many-tasking evolutionary algorithm with enhanced search direction-based knowledge transfer (MMaTEA-ESD). Specifically, the search directions for all tasks are first dynamically computed based on the populations obtained during the evolutionary search process. Subsequently, the search direction of the target task is divided into multiple subsearch directions. After that, the most similar subsearch directions from candidate source tasks are identified by measuring their similarity to those of the target task. Finally, the selected subsearch directions are combined to form the enhanced search direction for knowledge transfer, effectively mitigating negative transfer. Numerical results on a widely used multiobjective many-tasking benchmark test suite demonstrate the competitive performance of MMaTEA-ESD over some state-of-the-art algorithms.