Human Learning Optimization Algorithm With Diversified Searches

Abstract

A Human Learning Optimization with Diversified Search (DSHLO) algorithm is proposed to address the limitation of existing human learning optimization algorithms, such as smaller search space, and local optima due to the replication of optima in both individual and social learning operations. By introducing diversified search strategies, the DSHLO algorithm uses different methods to explore different solution spaces by simulating different human learning styles. Firstly, chaotic mapping is employed to enhance the population's likelihood of evolution. Secondly, inductive learning operators enrich the population diversity by combining learned individual and social knowledge with new one. Thirdly, the stochastic learning operator, based on the triangular walking strategy, increases the local optimization capability of the algorithm. Finally, the social learning operator, based on social hierarchy dominance, improves the convergence rate. The proposed algorithm is validated on the CEC2017 test set by comparison with nine baseline algorithms. The experimental results show that the DSHLO algorithm achieves faster convergence speeds and higher accuracy in most of the cases. Experiments on a supply chain planning and scheduling application prove that the proposed algorithm is also eligible to solve the practical engineering problems.