Self-adaptive classification learning hybrid JAYA and Rao-1 algorithm for large-scale numerical and engineering problems

In this paper, a self-adaptive classification learning hybrid JAYA and Rao-1 algorithm, which is called EHRJAYA, is proposed for solving large-scale numerical problems and real-world complex engineering optimization problems. JAYA algorithm and Rao-1 algorithm are two kinds of algorithms with simple structure and superior performance, which have the characteristics of no public parameters. In EHRJAYA the evolution strategies of the two algorithms are selected through a random selection mechanism. Then, a novel self-adaptive classification learning strategy is proposed, which fully utilizes information from different individuals. On this basis, two different adaptive coefficients are introduced to guide the population towards the optimal individual and away from the worst individual. Finally, combining the linear population reduction strategy and the dynamic lens opposition-based learning strategy, the convergence speed and ability to jump out of local optimum of the algorithm are greatly improved. To verify the performance of the proposed EHRJAYA, 59 complex functions from the CEC2014 and CEC2017 competitions are solved by EHRJAYA. Then, EHRJAYA and more than 20 algorithms with superior performance jointly solve ten challenging real-world engineering optimization problems. Experimental results show that the proposed EHRJAYA can obtain optimal results with the least computational resources in most cases. Therefore, in the face of these problems, effective solutions can be provided by EHRJAYA.