Title: Effective search technique in teaching and learning phase of TLBO algorithm for numerical function optimisation

Authors: Jaydeep Patel; Vimal Savsani; Vivek Patel; Rajesh Patel

Addresses: Department of Mechanical Engineering, Pandit Deendayal Petroleum University, Gandhinagar, 382007, Gujarat, India ' Department of Mechanical Engineering, Pandit Deendayal Petroleum University, Gandhinagar, 382007, Gujarat, India ' Department of Mechanical Engineering, Pandit Deendayal Petroleum University, Gandhinagar, 382007, Gujarat, India ' Department of Mechanical Engineering, Pandit Deendayal Petroleum University, Gandhinagar, 382007, Gujarat, India

Abstract: Optimisation is a very important process and plays a very vital role in many engineering and scientific researches. All optimisation algorithms have different search tendency to find the optimum value in the design space. However, the capability of the metaheuristic can be enhanced by modifying it with other efficient search techniques to make it more efficient and computationally effective. This paper explores the modifications in the basic teaching-learning-based optimisation (TLBO) algorithm with different effective search technique inspired from artificial bee colony (ABC) and particle swarm optimisation (PSO) algorithms for further enhancing the search capability of TLBO. To check the effectiveness of the proposed algorithm, 55 different benchmark problems from CEC2005 and CEC2014 were used. The proposed algorithm is also compared with other well-known metaheuristic methods. Statistical analysis is performed by Friedman rank test. The numerical comparison shows that the proposed algorithms are an alternative, effective and competitive optimisation algorithm for continuous problems.

Keywords: teaching-learning-based optimisation; TLBO; hybrid metaheuristic; artificial bee colony optimisation; ABC; particle swarm optimisation; PSO; unconstrained optimisation.

DOI: 10.1504/IJSI.2018.091423

International Journal of Swarm Intelligence, 2018 Vol.3 No.4, pp.332 - 363

Received: 11 Nov 2016
Accepted: 07 Nov 2017
Published online: 30 Apr 2018 *

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