I. Introduction
Nowadays, optimization problems are becoming more complex and difficult. Their aims are to find a set of parameter values that satisfy the constraints while optimizing their single or multiple performance indicators. Their solution methods can be roughly divided into two categories: 1) exact methods and 2) intelligent optimization algorithms [1]. The former can get the optimal solution and always generates the same results for different runs under the same conditions. The latter often provides a near-optimal solution and tends to generate different solutions under the same conditions. For large-scale problems, the former is time-consuming and often impossible, while the latter can provide a satisfactory solution in a reasonable time. Therefore, many intelligent optimization algorithms have been proposed including genetic algorithm [2], differential evolution [3], cultural algorithm [4], particle swarm optimization (PSO) [5], ant colony optimization [6], artificial bee colony (ABC) [7], bat algorithm [8], grey wolf algorithm [9], harmony search [10], brain storm optimization [11], fireworks algorithm [12], and teaching-learning-based optimization (TLBO) [13]. These algorithms have been applied in many fields [14]–[17].