## 1 Introduction  
The scheduling of operations has a vital role in the planning and managing of manufacturing processes. The job-shop scheduling problem (JSP) is one of the most popular scheduling models in practice. In the JSP, a set of jobs should be processed with a set of machines, and each job consists of a sequence of consecutive operations. Moreover, a machine can only process one operation at one time, and the operation cannot be interrupted. Additionally, JSP is aimed at minimizing the number of operations for these jobs under the above constraints. The flexible job-shop scheduling problem (FJSP) is an extension of the conventional JSP, in which operations are allowed to be processed on any one of the existed machines. FJSP is more complicated than JSP, because FJSP not only needs to identify the arrangement of all processes of all machines, but also needs to determine the sequence of processes on each machine. FJSP breaks through the uniqueness restriction of resources. Each process can be completed by many different machines, so that the job shop scheduling problem is closer to the real production process. JSP has been proven to be an NP-hard problem [1], and even for a simple instance with only ten operations and ten available machines for selection, it is still hard to search for a convincing result. As an extension of JSP, FJSP is more complicated to solve. A lot of the literature believes that adopting a heuristic method is a reasonable approach for solving this kind of complex problem [2; 3; 4; 5; 6; 7; 8; 9]. Thus, there are many researchers who have used heuristic methods to solve FJSP. [PERSON] [10] attempted to use a hierarchical approach to solve the flexible job shop scheduling problem, and a tabu search is adopted to enhance the effectiveness of the approach. [PERSON] _et al._[11] proposed a mathematical model for the flexible job shop scheduling problem, and two heuristic approaches (tabu search and simulated annealing heuristics) are also introduced to solve the real size problems. [PERSON] _et al._[12] developed a hybrid genetic algorithm to solve the flexible job shop scheduling problem. In the algorithm, the two vectors are used to represent the solution, the individuals of GA are improved by a variable neighborhood descent and the advanced genetic operators are presented by a special chromosome structurer. [PERSON] _et al._[2] presented an improved ant colony optimization to solve FJSP, and an adaptive parameter, crossover operation and pheromone updating strategy are used to improve the performance of the algorithm. The shuffled frog-leaping algorithm (SFLA) was developed by [PERSON] and [PERSON] [13]. It is a meta-heuristic optimization method, which combines the advantages of the genetic-based memetic algorithm (MA) and the social behavior-based PSO algorithm [14]. A meme is a kind of information body, which can be distributed, reproduced and exchanged by infecting the thoughts of human beings or animals. The most salient characteristic of the meme algorithm is that memes can share and exchange experience, knowledge and information along with relying on a local search method in the process of evolution. Therefore, with the meme algorithm allows an individual of the traditional genetic algorithm model to become more intelligent. The group of SFLA consists of the frog group in which the individual frogs can communicate with each other. Each frog can be seen as a meme carrier. Along with the communication among frogs, the meme evolution can be performed during the searching process of the algorithm. Due to its efficiency and practical value, SFLA has attracted more attention and has been successfully used in a number of classical combinatorial optimization problems [15; 16; 17; 18; 19]. In 1999, a new general-purpose local search optimization approach, extremal optimization (EO), was proposed by [PERSON] and [PERSON] [20]. The strategy was evolved by the fundamentals of statistical physics and self-organized criticality [21].