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. 2022 Apr 30:2022:5699472.
doi: 10.1155/2022/5699472. eCollection 2022.

Differential Human Learning Optimization Algorithm

Pinggai Zhang  1   2 , Ling Wang  2 , Jiaojie Du  2 , Zixiang Fei  3 , Song Ye  1 , Minrui Fei  2 , Panos M Pardalos  4
Affiliations

Differential Human Learning Optimization Algorithm

Pinggai Zhang et al. Comput Intell Neurosci. .

Abstract

Human Learning Optimization (HLO) is an efficient metaheuristic algorithm in which three learning operators, i.e., the random learning operator, the individual learning operator, and the social learning operator, are developed to search for optima by mimicking the learning behaviors of humans. In fact, people not only learn from global optimization but also learn from the best solution of other individuals in the real life, and the operators of Differential Evolution are updated based on the optima of other individuals. Inspired by these facts, this paper proposes two novel differential human learning optimization algorithms (DEHLOs), into which the Differential Evolution strategy is introduced to enhance the optimization ability of the algorithm. And the two optimization algorithms, based on improving the HLO from individual and population, are named DEHLO1 and DEHLO2, respectively. The multidimensional knapsack problems are adopted as benchmark problems to validate the performance of DEHLOs, and the results are compared with the standard HLO and Modified Binary Differential Evolution (MBDE) as well as other state-of-the-art metaheuristics. The experimental results demonstrate that the developed DEHLOs significantly outperform other algorithms and the DEHLO2 achieves the best overall performance on various problems.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
The flowchart of DEHLO1.
Figure 2
Figure 2
The flowchart of DEHLO2.
Figure 3
Figure 3
The convergence curves of the MKP (maximum generation = 5000).
Algorithm 1
Algorithm 1
Pseudocode of HLO.
See this image and copyright information in PMC

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