## Literature Review
### The Basic SFLA  
As a kind of population and evolutionary-based metaheuristic, the basic SFLA (B-SFLA) employs a swarm of frogs to find the position (solution) with the greatest amount of food. The goodness of a position can be indicated by an objective function value. In a \(D\)-dimensional space, the current position of a frog\(f\) is denoted as \(X_{f}(t)=(X_{f1},\ldots,\)\(X_{fD})\). During memetic evolution, frogs are separated into \(m\) groups, with the best frog being assigned to the memeplex 1; the 2 nd best to the memeplex 2; the \(m\)th best to the memeplex \(m\); the \(m+1\)th best then back to the memeplex 1 again, and so on. As a result, each group has \(n=F/m\) frogs, and these frogs are ordered decreasingly according to their objective function values [35]. Following this, triangular distribution sampling, as in Equation (1), is used to form sub-memeplexes.  
\[P_{f}=2(n+1-f)/n(n+1)\ f=1,\ldots,\ n \tag{1}\]  
With Equation (1), the best frog in each memeplex has the highest probability \(2/(n+1)\), while the worst frog has the lowest probability \(2/n(n+1)\), of being selected into a corresponding sub-memeplex. Then, Roulette Wheel Selection (RWS) can be used to select \(q\) frogs from the original memeplex into the sub-memeplex for evolution and local searching. At the current time \(t\), the leaping distance of the frog \(f\) is denoted as \(D_{f}(t)\) and determined by Equation (2).  
\[D_{f}(t)=\left\{\begin{array}{ll}\min\left\{\text{int}\left\{R\left(X_{b}(t )-X_{f}(t)\right),S_{m}\right\}\right\}&\text{for positive step}\\ \max\left\{\text{int}\left\{R\left(X_{b}(t)-X_{f}(t)\right),-S_{m}\right\} \right\}&\text{for negative step}\end{array}\right. \tag{2}\]  
The \(R\) is a random number in [0, 1]; \(X_{b}(t)\) is the position of the best frog in the sub-memeplex; \(S_{m}\) is the maximum step allowed. The next position of the frog\(f\) is determined by Equation (3).  
\[X_{f}(t+1)=X_{f}(t)+D_{f}(t) \tag{3}\]  
If feasible and better, this next position will be accepted; otherwise, the \(X_{b}(t)\) in Equation (2) will be replaced with \(X_{g}(t)\) and the next position will be found again, where \(X_{g}(t)\) is the position of the global best frog. If this next position is once again not better, a random jump will be made for the frog. Once all frogs in a sub-memeplex have completed several local searches, they then move on to process the next sub-memeplex until all sub-memeplexes have been completed. After completing one iterative run, the frogs are reshuffled and the next iterative run is started. This is carried out until the termination criteria are reached. For more details about the B-SFLA, one can refer to [PERSON] et al. [35].  
Our literature review shows that SFLA and its variants have been used in various areas, including water distribution network design [19], project management [36], knapsack problem [37], component pick-and-place problem [38], routing problem [39], gateway loading balance [40], local dimming optimization [41], underwater sonar image detention [42], job scheduling [43], big data [44], feature selection [45], etc. However, to our best knowledge, SFLA has never been used to deal with the BAP.