A Lottery-based Spherical Evolution Algorithm with Elite Retention Strategy

Abstract

Spherical evolution (SE) is a recently proposed meta-heuristic algorithm. Its special search approach has been proved to be very effective in exploring the search space. SE is very powerful for optimization, but still has room for improvement due to some promising solutions usually fail to survive into the next generation. To alleviate this issue, we innovatively design a novel lottery-based elite retention strategy and propose a lottery elite spherical evolution algorithm (LESE). To verify the effectiveness of the proposed LESE, we experimentally compare it with the original SE algorithm and other representative meta-heuristics algorithms. We use 30 benchmark functions from the IEEE CEC2017 benchmark as the test set for our experiments. The effectiveness of LESE is demonstrated by analyzing the experimental results from perspectives of solution accuracy, convergence speed, solution distribution, and search dynamics.