A diverse human learning optimization algorithm

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...

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Vydané v:Journal of global optimization Ročník 67; číslo 1-2; s. 283 - 323
Hlavní autori: Wang, Ling, An, Lu, Pi, Jiaxing, Fei, Minrui, Pardalos, Panos M.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.01.2017
Springer
Springer Nature B.V
Predmet:
Algorithms
Automation
Computer Science
Heuristic
Heuristic methods
Human performance
Knowledge
Learning
Learning activities
Machine learning
Mathematical optimization
Mathematics
Mathematics and Statistics
Normal distribution
Operations Research/Decision Theory
Operators
Optimization
Optimization algorithms
Performance evaluation
Real Functions
Searching
Social aspects
Studies
Swarm intelligence
Human learning optimization
Gaussian distribution
Computational experiments
Global optimization
Meta-heuristic
ISSN:0925-5001, 1573-2916
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Popis
Shrnutí: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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ISSN:0925-5001
1573-2916
DOI:10.1007/s10898-016-0444-2