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A diverse human learning optimization algorithm

Author

Listed:
  • Ling Wang

    (Shanghai University)

  • Lu An

    (Shanghai University)

  • Jiaxing Pi

    (University of Florida)

  • Minrui Fei

    (Shanghai University)

  • Panos M. Pardalos

    (University of Florida)

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.

Suggested Citation

  • Ling Wang & Lu An & Jiaxing Pi & Minrui Fei & Panos M. Pardalos, 2017. "A diverse human learning optimization algorithm," Journal of Global Optimization, Springer, vol. 67(1), pages 283-323, January.
  • Handle: RePEc:spr:jglopt:v:67:y:2017:i:1:d:10.1007_s10898-016-0444-2
    DOI: 10.1007/s10898-016-0444-2
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    References listed on IDEAS

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    1. Bahriye Akay, 2013. "Synchronous and asynchronous Pareto-based multi-objective Artificial Bee Colony algorithms," Journal of Global Optimization, Springer, vol. 57(2), pages 415-445, October.
    2. Hyoungjin Kim & Meng-Sing Liou, 2013. "New fitness sharing approach for multi-objective genetic algorithms," Journal of Global Optimization, Springer, vol. 55(3), pages 579-595, March.
    3. Ana Rocha & M. Costa & Edite Fernandes, 2014. "A filter-based artificial fish swarm algorithm for constrained global optimization: theoretical and practical issues," Journal of Global Optimization, Springer, vol. 60(2), pages 239-263, October.
    4. Massimiliano Kaucic, 2013. "A multi-start opposition-based particle swarm optimization algorithm with adaptive velocity for bound constrained global optimization," Journal of Global Optimization, Springer, vol. 55(1), pages 165-188, January.
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    Cited by:

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