# A New Approach for Energy Efficient Routing in MANETs Using Multi Objective Genetic Algorithm

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3 7 Calculate cost of the newly generated children 8 Add new children (which are not in population) in the population 9 Sort population on the cost of the paths and select next population of best 20 paths (paths with minimum path length) 10 Repeat steps 5-9 till criteria to stop does not reach The algorithm has been implemented in java The proposed procedure is as follows: Let suppose node 1 is the source node and node 60 is the destination node and algorithm has to find an optimal path between these nodes First, it finds its neighbor nodes as friends and population will be generated randomly to transfer data from node 1 and node - 60 Node = 1 Friends Node = 2 Friends Node = 3 Friends Node = 4 Friends Node = 5 Friends Node = 60 Friends Node = 61 Friends Node = 62 Friends Node = 63 Friends Node = 64 Friends Node = 65 Friends According to these neighbor nodes, then it generates cost matrix of 65 x 65 Now it generates 50 random paths from source to destination It generates such 50 paths and now it calculates cost of each above paths from path 0 to path 49 and finds best path according to shortest path 0 = [1, 7, 8, 3, 4, 2, 9, 12, 10, 13, 14, 17, 19, 22, 27, 32, 37, 42, 43, 46, 45, 48, 49, 50, 55, 60] 1 = [1, 7, 6, 2, 4, 5, 8, 11, 15, 12, 16, 17, 19, 21, 22, 27, 26, 30, 33, 29, 31, 32, 34, 38, 41, 37, 35, 39, 44, 45, 46, 50, 49, 52, 53, 57, 62, 63, 60] Cost = = [1, 4, 5, 8, 2, 6, 7, 12, 14, 11, 15, 19, 22, 27, 29, 28, 31, 33, 32, 35, 37, 41, 46, 48, 44, 39, 36, 34, 38, 40, 42, 45, 47, 49, 52, 53, 58, 60] Cost = = [1, 8, 2, 9, 12, 7, 11, 10, 13, 14, 17, 19, 21, 23, 24, 29, 27, 26, 25, 30, 32, 37, 40, 38, 36, 34, 35, 39, 44, 48, 49, 50, 51, 53, 52, 57, 59, 60] Cost = 612 Best no = 0 [1, 7, 8, 3, 4, 2, 9, 12, 10, 13, 14, 17, 19, 22, 27, 32, 37, 42, 43, 46, 45, 48, 49, 50, 55, 60] These 50 paths form the population of the genetic algorithm and find fitness function and calculate average path cost This is the initial population of the genetic algorithm == Best no = 0 [1, 7, 8, 3, 4, 2, 9, 12, 10, 13, 14, 17, 19, 22, 27, 32, 37, 42, 43, 46, 45, 48, 49, 50, 55, 60] Average Cost = Now it sorts the initial population in ascending order of cost Now out of 50 total populations (paths) 20 randomly paths (parents) are selected Randomly Generated Parents Are Thereafter, first two parents (taken whole path) are crossed with each other using one-point crossover (say common city) and two new children are formed Then, next two parents are considered and two new children are generated and so on Hence, it generates 20 new children Parent 1: 2: [1, 4, 3, 8, 7, 12, 11, 14, 15, 19, 22, 27, 26, 21, 23, 25, 30, 32, 34, 35, 39, 44, 45, 47, 43, 48, 51, 49, 54, 55, 60] Cost = 402 Parent 2: 37: [1, 3, 5, 4, 8, 9, 12, 14, 17, 15, 19, 22, 21, 23, 24, 29, 30, 25, 26, 27, 31, 32, 37, 35, 34, 36, 40, 44, 39, 42, 45, 47, 43, 46, 50, 55, 60] Cost = 598 Common City = 4 Children Generated Are Child 1 [1, 4, 8, 9, 12, 14, 17, 15, 19, 22, 21, 23, 24, 29, 30, 25, 26, 27, 31, 32, 37, 35, 34, 36, 40, 44, 39, 42, 45, 47, 43, 46, 50, 55, 60] Cost = 548 Child 2 [1, 3, 5, 4, 3, 8, 7, 12, 11, 14, 15, 19, 22, 27, 26, 21, ISSN: All Rights Reserved 2015 IJSETR 1782

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