A human learning optimization algorithm and its application to multi-dimensional knapsack problems

• A novel meta-heuristic named human learning optimization (HLO) is presented.
• Four learning operators inspired by the human learning process are developed.
• HLO is applied to solve multi-dimensional knapsack problems.
• The experimental results show that HLO is a promising optimization tool.

Inspired by human learning mechanisms, a novel meta-heuristic algorithm named human learning optimization (HLO) is presented in this paper in which the individual learning operator, social learning operator, random exploration learning operator and re-learning operator are developed to generate new solutions and search for the optima by mimicking the human learning process. Then HLO is applied to solve the well-known 5.100 and 10.100 multi-dimensional knapsack problems from the OR-library and the performance of HLO is compared with that of other meta-heuristics collected from the recent literature. The experimental results show that the presented HLO achieves the best performance in comparison with other meta-heuristics, which demonstrates that HLO is a promising optimization tool.

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