Erkan Erdemir^1,*, Adem Alpaslan Altun²

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2923-2941, 2022, DOI:10.32604/cmc.2022.022797

Abstract Metaheuristic algorithms are one of the methods used to solve optimization problems and find global or close to optimal solutions at a reasonable computational cost. As with other types of algorithms, in metaheuristic algorithms, one of the methods used to improve performance and achieve results closer to the target result is the hybridization of algorithms. In this study, a hybrid algorithm (HSSJAYA) consisting of salp swarm algorithm (SSA) and jaya algorithm (JAYA) is designed. The speed of achieving the global optimum of SSA, its simplicity, easy hybridization and JAYA's success in achieving the best solution have given us the idea… More >

Yunlong Wang^1,2,3, Xiong Luo^1,2,4,*, Jing Zhang^1,2,3, Zhigang Zhao¹, Jun Zhang⁵

Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 961-971, 2020, DOI:10.32604/iasc.2020.010128

Abstract K-means is a simple and commonly used algorithm, which is widely applied in many fields due to its fast convergence and distinctive performance. In this paper, a novel algorithm is proposed to help K-means jump out of a local optimum on the basis of several ideas from evolutionary computation, through the use of random and evolutionary processes. The experimental results show that the proposed algorithm is capable of improving the accuracy of K-means and decreasing the SSE of K-means, which indicates that the proposed algorithm can prevent K-means from falling into the local optimum to some extent. More >