A Regional Local Search and Memory based Evolutionary Algorithm for Dynamic Multi-objective Optimization
Sanyi Li, Yanfeng Wang, Weichao Yue · 2020
This paper presents a novel dynamic multi-objective optimization algorithm based on region local search and memory (DMOA-RLSM). Firstly, the NSGA2-DM stores useful information (memory) to guide population initialization in the future; secondly, in the stage of population regeneration, DMOA-RLSM get corner points and sparse point according to the results of non-dominated sorting of current populations, define these points as the centers of border areas and sparse area respectively; thirdly, search around the corner points and sparse point locally. DMOA-RLSM adopts extreme optimization strategy and random search strategy simultaneously to improve the quality of solutions and convergence rate. Performance of DMOA-RLSM is compared with two reported dynamic multi-objective optimization algorithms (DMOAs) for dMOP functions and FDA functions. Results show that the DMOA-RLSM performs better than the compared algorithms because of its low computational complexity.