Self-adaptive Percolation Behavior Water Cycle Algorithm Shilei Qiao 1 , Yongquan Zhou 1,2(&) , Rui Wang 1 , and Yuxiang Zhou 1 1 College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China yongquanzhou@126.com 2 Guangxi High School Key Laboratory of Complex System and Computational Intelligence, Nanning 530006, China Abstract. Water cycle algorithm is a new meta-heuristic optimization algo- rithm based on the observation of water cycle and how rivers and streams flow downhill towards the sea in the real world. In this paper, a new self-adaptive water cycle algorithm with percolation behavior is proposed. The percolation behavior is introduced to accelerate the convergence speed of proposed algo- rithm. At the same time, a self-adaptive rainfall process can generate the new stream, more and more new position can be explored, consequently, increasing the diversity of population. Eight typical benchmark functions are tested, the simulation results show that the proposed algorithm is feasible and effective than basic water cycle algorithm, and demonstrate that this proposed algorithm has superior approximation capabilities in high-dimensional space. Keywords: Water cycle algorithm  Percolation behavior  Rainfall  Self-adaptive water cycle algorithm  Function optimization 1 Introduction Nowadays, due to the evolutionary algorithm can solve some problem that the tradi- tional optimization algorithm cannot do easy, more and more modern meta-heuristic algorithms inspired by nature or social phenomenon are emerging and they become increasingly popular. For example, genetic algorithm (GA) [1] or particle swarm optimization (PSO) [2], and the physical annealing which is generally known as simulated annealing (SA) [3], glowworm swarm optimization (GSO) [4], harmony search (HS) [5], bacterial foraging optimization algorithm (BFOA) [6], and invasive weed optimization (IWO) [7], and so on [8, 9]. The water cycle algorithm (WCA) is proposed by Eskandar etc. (2012) [10]. Similar to other meta-heuristic algorithms, the proposed method begins with an initial population so called the raindrops. First, we assume that we have rain or precipitation. The best individual (best raindrop) is chosen as a sea. Then, a number of good rain- drops are chosen as a river and the rest of the raindrops are considered as streams which flow to rivers and sea. In addition, rivers flow to the sea which is the most downhill location. This algorithm gradually aroused people’s close attention, and which is increasingly applied to different area. Ail Sadollah etc. (2014) [11] proposed an © Springer International Publishing Switzerland 2015 D.-S. Huang et al. (Eds.): ICIC 2015, Part I, LNCS 9225, pp. 85–96, 2015. DOI: 10.1007/978-3-319-22180-9_9