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Multivariant Optimization Algorithm for the 0-1 Knapsack Problem

In this paper, a novel heuristic algorithm named Multivariant Optimization Algorithm (MOA) is presented to solve the 0-1 Knapsack Problem (KP). In MOA, multivariant search groups (locate and global search groups) execute the global exploration and local exploitation iteratively to locate the optimal solution automatically. The presented algorithm has been compared with Genetic Algorithm (GA) and Particle swarm algorithm (PSO) based on five data sets, results show that the optimization of MOA is better than GA and PSO when the dimension of problem is high.