*4.6. Scouting Bees*

If a solution is not updated for a long time, the solution will be abandoned and the employed bee will then be transformed into a scout bee, choosing a new solution at random in the solution space. As random search is uncontrollable, this random strategy does not have a positive impact on the algorithm, therefore, this paper uses a neighborhoodbased solution swapping strategy to improve the efficiency of the scout bee phase of the algorithm [34]. This is because the solutions of neighboring sub-problems should be similar.

The scout bee searches in the following way: for a solution that has not improved after *L* cycles, the scout bee first finds a more suitable solution among its neighboring individuals. Then they exchange them with each other. If no better one is found, one individual is randomly chosen to exchange with each other. The basic procedure is described in Algorithm 5, where *L*(**X***i*) denotes the number of cycles **X***i* has gone through, *T* denotes the number of neighboring individuals, **X***i,j* denotes the *j*-th neighboring individual of the *i*-th solution.

```
Algorithm 4 Onlooker bee phase
Input: population, P;
Output: new population, P
                                 '
                                 ;
1: P
    '
      = P;
2: for j = 1 to N do
3: sj = Quality(Xj);
4: end for
5: Sort(P
          '
           , si);
6: for j = 1 to N do
7: Xindex = Select(P
                          '
                          )
8: Ti = GetNeighborhood(Xindex);
9: Xchild = TPX(Xindex,Ti);
10: if 0.1 < Random() then
11: Xchild =Mutation(Xchild);
12: end if
13: if Xchild < Xindex then
14: Xindex =Xchild;
15: end if
16: if Xchild) < Ti then
17: Ti = Xchild;
18: end if
19: end for
20: return P
              '
               .
```
#### **Algorithm 5** Scout bee phase

**Input:** population, *P*; **Output:** new population, *P '* ; 1: **for** *i* = 1 to *N* **do** 2: **if** L(*Xi*) > *L* **then** 3: **for** *j* = 1 to *T* **do** 4: **if** *Xi,j* < *Xi* **then** 5: *Xi,j* ↔ *Xi*; 6: **break;** 7: **end if** 8: **end for** 9: **if** *j* > *T* **then** 10: *r =* Rand(1, *T*); 11: *Xi,r* ↔ *Xi*; 12: **end if** 13: **end if** 14: **end for** 15: **return** *P '* .

#### *4.7. The Whole Process of the Algorithm*

This section outlines the entire algorithmic process of SDABC. It can be roughly divided into four steps.

Step 1: The population is initialized randomly and is energy-efficient to improve the quality of the solution.

Step 2: Sort the population in the manner described in Section 4.4 and perform the algorithmic operations described in Sections 4.5–4.7 in sequence, while updating the domain relationships of individuals in population and the external populations after each subsection is completed.

Step 3: Repeat Step 2 until the end conditions are met.

Step 4: Perform another energy saving procedure on the external population.

The algorithm flow of SDABC can be shown in Figure 1.

**Figure 1.** The overall flow of the SDABC.


**4.8 Hypocalcaemia and hyperphosphataemia is prevalent in our crf patients**

In the study the mean serum calcium of CRF subject was 5.83± 2.1mg/dl. 37(71%) of CRF subjects had hypocalcaemia (<8.5mg/dl). This finding agrees with that of Slatoposky et al (1986). Calcium supplementation is a known modality for the treatment of hypocalcaemia. The mean serum phosphate of CRF patients in this was 6.1±2.0mg/dl. 41 (79%) had hyperphosphataemia (>4.5mg/dl). This agrees with finding of Slotoposky et al (1986) who demonstrated hyperphosphataemia even in moderate CRF. Dietary phosphate restriction and phosphate binding are effective methods of control of hyperphosphataemia.

### **4.9 Conclusion and recommendations**


*Edited by Ashim Dutta and Hafiz Muhamad Ali*

In modern times, rheology has emerged as a powerful tool for materials scientists to explore the properties of soft matter or complex fluids, including such diverse materials as food, cosmetics, polymers, lubricants, drilling fluids and biological systems. Rheology parameters such as shear modulus (G'), storage modulus (G") and viscosity (η), together with microscopic imaging, provide considerable insight into the structure-property relationship in these materials. This in turn helps design materials with properties tailored to multiple applications. This book is a compilation of works by experts in their respective areas of specialization and covers a wide range of applications. The book will be useful both to experts in this area of research and to newcomers from a range of specializations.

ISBN 978-1-83769-996-4

Advances in Rheology of Materials

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