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The data analysis in medical field is a very crucial task in order to gain insights from a large collection of data. The analysis of data comprises of several well-defined processes like data collection, data preprocessing and exploratory data ...

Human Learning Optimization (HLO) is an emergent inborn binary meta-heuristic inspired by human learning mechanisms. To solve continuous problems efficiently, continuous HLO (CHLO) variants were presented. However, the research on CHLO is at its ...

Humans perform Bayesian inference in a wide variety of tasks, which can help people make selection decisions effectively and therefore enhances learning efficiency and accuracy. Inspired by this fact, this paper presents a novel human learning ...

Human Learning Optimization (HLO) is a simple yet powerful meta-heuristic developed based on a simplified human learning model. Many cognitive activities of humans contain an element of reasoning, and with reasoning, humans can gain ...

• This paper proposes a novel human learning optimization algorithm with reasoning learning (HLORL).

Human Learning Optimization (HLO) is a simple yet efficient binary meta-heuristic, in which three learning operators, i.e. the random learning operator (RLO), individual learning operator (ILO) and social learning operator (SLO), are developed to ...

In this paper, a novel context-based 3D Otsu algorithm using human learning optimization (HLO) is proposed for multilevel color image segmentation. The performance of 3D Otsu algorithm is reported to be poor while dealing with between-...

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• In this paper, a new context sensitive based 3D Otsu function for image segmentation is proposed.

• The role of the random learning operator (RLO) in HLO is deeply studied and analyzed.

Human learning Optimization (HLO) is an emergent promising meta-heuristic algorithm which uses the random learning operator, the individual learning operator, and the social learning operator to search out the optimal solution of ...

This paper proposes a new hybrid-coded HLO (HcHLO) framework to tackle mix-coded problems more efficiently and effectively.A new continuous human learning optimization algorithm is presented based on the linear learning mechanism of humans.The results ...

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 ...

This paper presents a novel meta-heuristic optimization algorithm, named Adaptive Simplified Human Learning Optimization (ASHLO), which is inspired by the human learning mechanisms. Three learning operators, i.e. the random learning operator, the ...

A novel meta-heuristic named human learning optimization (HLO) is presented.Four learning operators inspired by the human learning process are developed.HLO is applied to solve multi-dimensional knapsack problems.The experimental results show that HLO ...