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A diverse human learning optimization algorithm

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Abstract

Human Learning Optimization is a simple but efficient meta-heuristic algorithm in which three learning operators, i.e. the random learning operator, the individual learning operator, and the social learning operator, are developed to efficiently search the optimal solution by imitating the learning mechanisms of human beings. However, HLO assumes that all the individuals possess the same learning ability, which is not true in a real human population as the IQ scores of humans, one of the most important indices of the learning ability of humans, follow Gaussian distribution and increase with the development of society and technology. Inspired by this fact, this paper proposes a Diverse Human Learning Optimization algorithm (DHLO), into which the Gaussian distribution and dynamic adjusting strategy are introduced. By adopting a set of Gaussian distributed parameter values instead of a constant to diversify the learning abilities of DHLO, the robustness of the algorithm is strengthened. In addition, by cooperating with the dynamic updating operation, DHLO can adjust to better parameter values and consequently enhances the global search ability of the algorithm. Finally, DHLO is applied to tackle the CEC05 benchmark functions as well as knapsack problems, and its performance is compared with the standard HLO as well as the other eight meta-heuristics, i.e. the Binary Differential Evolution, Simplified Binary Artificial Fish Swarm Algorithm, Adaptive Binary Harmony Search, Binary Gravitational Search Algorithms, Binary Bat Algorithms, Binary Artificial Bee Colony, Bi-Velocity Discrete Particle Swarm Optimization, and Modified Binary Particle Swarm Optimization. The experimental results show that the presented DHLO outperforms the other algorithms in terms of search accuracy and scalability.

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Acknowledgments

This work is supported by National Natural Science Foundation of China (Grant No. 61304031, 61374044 & 61304143), Innovation Program of Shanghai Municipal Education Commission (14YZ007), Key Project of Science and Technology Commission of Shanghai Municipality under Grant No. 14JC1402200 and 14DZ1206302, Key Project of Shanghai Municipal Commission of Economy and Informatization (ZB-ZBYZ-02-14-0825), AirForce and DTRA grants (P. Pardalos, J. Pi), and a Paul and Heidi Brown Preeminent Professorship in Industrial and Systems Engineering, University of Florida.

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Authors and Affiliations

  1. Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, 200072, China

    Ling Wang, Lu An & Minrui Fei

  2. Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, 32611, USA

    Jiaxing Pi & Panos M. Pardalos

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  1. Ling Wang
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  2. Lu An
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  3. Jiaxing Pi
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  4. Minrui Fei
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  5. Panos M. Pardalos
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Correspondence to Ling Wang.

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Wang, L., An, L., Pi, J. et al. A diverse human learning optimization algorithm. J Glob Optim 67, 283–323 (2017). https://doi.org/10.1007/s10898-016-0444-2

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  • DOI: https://doi.org/10.1007/s10898-016-0444-2

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