A Simple Human Learning Optimization Algorithm

This paper presents a novel Simple Human Learning Optimization (SHLO) algorithm, which is inspired by human learning mechanisms. Three learning operators are developed to generate new solutions and search for the optima by mimicking the learning behaviors of human. The 0-1 knapsack problems are adopted as benchmark problems to validate the performance of SHLO, and the results are compared with those of binary particle swarm optimization (BPSO), modified binary differential evolution (MBDE), binary fruit fly optimization algorithm (bFOA) and adaptive binary harmony search algorithm (ABHS). The experimental results demonstrate that SHLO significantly outperforms BPSO, MBDE, bFOA and ABHS. Considering the ease of implementation and the excellence of global search ability, SHLO is a promising optimization tool.

[1]  Panos M. Pardalos,et al.  An improved adaptive binary Harmony Search algorithm , 2013, Inf. Sci..

[2]  Shengyao Wang,et al.  A novel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem , 2013, Knowl. Based Syst..

[3]  K. Lee,et al.  A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice , 2005 .

[4]  Janez Brest,et al.  A Brief Review of Nature-Inspired Algorithms for Optimization , 2013, ArXiv.

[5]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[6]  B. Chakravarthy,et al.  The persistence of knowledge‐based advantage: an empirical test for product performance and technological knowledge , 2002 .

[7]  Jianhua Wu,et al.  Solving 0-1 knapsack problem by a novel global harmony search algorithm , 2011, Appl. Soft Comput..

[8]  Daniel R. Ilgen,et al.  Team learning: collectively connecting the dots. , 2003, The Journal of applied psychology.

[9]  Chun-Yin Wu,et al.  Topology optimization of structures using modified binary differential evolution , 2010 .

[10]  Marco Dorigo,et al.  Distributed Optimization by Ant Colonies , 1992 .

[11]  Alfonso Quarati,et al.  ICT Driven Individual Learning: New Opportunities and Perspectives , 2000, J. Educ. Technol. Soc..

[12]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[13]  William Ickes,et al.  "Social" Cognition and Social Cognition , 1994 .

[14]  Sadiq M. Sait,et al.  Binary particle swarm optimization (BPSO) based state assignment for area minimization of sequential circuits , 2013, Appl. Soft Comput..

[15]  Wen-Tsao Pan,et al.  A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example , 2012, Knowl. Based Syst..

[16]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[17]  B. Delahaye,et al.  Influences On Knowledge processes In Organizational Learning: The Psychosocial Filter , 2000 .