Clustered Genetic Algorithm to solve Multidimensional Knapsack Problem

Dr. Prabha Shreeraj Nair
2017 International Journal of Trend in Scientific Research and Development  
Genetic Algorithm (GA) has emerged as a powerful tool to discover optimal for multidimensional knapsack problem (MDKP). Multidimensional knapsack problem has recognized as NP-hard problem whose applications in many areas like project selection, capital budgeting, loading problems, cutting stock etc. Attempts has made to develop cluster genetic algorithm (CGA) by mean of modified selection and modified crossover operators of GA. Clustered genetic algorithm consist of (1) fuzzy roulette wheel
more » ... ction for individual selection to form the mating pool (2) A different kind of crossover operator which employ hierarchical clustering method to form two clusters from individuals of mating pool. CGA performance has examined against GA with respect to 30 benchmark problems for multidimensional knapsack. Experimental results show that CGA has significant improvement over GA in relation to discover optimal and CPU running time. The data set for MDKP is available at
doi:10.31142/ijtsrd2237 fatcat:ecp3antnybbuvpfb6wklmkmrs4