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  A Comparative Study of Meta-HeuristicOptimization Algorithms for 0 – 1 Knapsack Problem: Some Initial Results Absalom E. Ezugwu, Verosha Pillay, Divyan Hirasen, Kershen Sivanarain, Melvin Govender Abstract— In this paper, we present some initial results of several meta-heuristic optimization algorithms, namely, geneticalgorithms, simulated annealing, branch and bound, dynamic programming, greedy search algorithm, and a hybrid geneticalgorithm-simulated annealing for solving the 0–1 knapsack problems. Each algorithm is designed in such a way that it penalizesinfeasible solutions and optimizes the feasible solution. The experiments are carried out using both low-dimensional and high-dimensional knapsack problems. The numerical results of the hybrid algorithm are compared with the results achieved by theindividual algorithms. The results revealed the superior performances of the branch and bound dynamic programming, andhybrid genetic algorithm with simulated annealing methods over all the compared algorithms. This performance was establishedby taking into account both the algorithm computational time and the solution quality. In addition, the obtained results alsoindicated that the hybrid algorithm can be applied as an alternative to solve small- and large-sized 0–1 knapsack problems. Transform coding Atrophy Pathogens Software reviews Pediatrics Decoding HTML Long Term Evolution Fabrication Reconfigurable devices.Cardiac tissue Smart phones Emergency lighting Force control Millimeter wave radar. Leaching Orthopedic procedures Construction Web TVNeurostimulation Galvanizing Social engineering (security) Fetal heart rate Quasi-doping Collective intelligence Multiresolution analysis Meansquare error methods. 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Current density Power demand Active noise reduction Yttrium Parallel processing Dogs Radar antennasRespiratory system. (4)
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