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For cluster formation in our purpose methodology we are using Evolutionary algorithms. These nature inspired algorithms form multiple solutions the most efficient and the optimized solution is selected among all solutions. For NP-hard problem there is no known polynomial algorithm so time for finding solution grows exponentially with the size of problem. For solving these problems, we define the desired criterion where our algorithm should terminate. Our defined problem (Clustering in inter-WBAN) is also an NP-hard, as we need to find optimum clusters with multiple nodes and multiple parameters. Most real word problems may have to achieve multi-objective, these objectives may be different in nature. Multi-objectives problems required simultaneous optimization. Each objective is achieved with its specific objective function. These objective functions are measured in different units, and usually, they are conflicting and competing. Suppose we want to buy a railway ticket with low cost and less time to reach the destination. It is a fact that with cheap ticket, railway service will be compromised, and will stop on every station and cost more time. On the other hand, an expansive ticket train may cost less time to reach the destination. Multi-objective functions with conflicting objectives raise the set of optimal solutions, as no single solution can be considered to be best, with respect to all objectives. These solutions can be classified on as dominated and no-dominated sets Figure 3.3.

Figure 3.3 Flow chart of proposed scheme.

a) Fitness Calculation

Evolutionary algorithms are used to find a different solution. Every solution generally signified as a string of binary numbers (Chromosome). To come up with the best solution it is required to test all these solutions. For this purpose, we need to identify the score of each solution to find how closely it meets the overall specified desired result. This score is generated by the application of fitness function.

b) Local Best/Global Best

We calculate two values local best and global best, the local best value of everyone, if the current value of velocity of an individual is better than older value, the local best value will be replaced with the new one, otherwise, remain the same. The same goes for the global best value. Global best value is the best value among all the solution sets till now.