Abstract
AbstractTeaching-learning based optimization is a newly developed intelligent optimization algorithm. It imitates the process of teaching and learning simply and has better global searching capability. However, some studies have shown that TLBO is good at exploration but poor at exploitation and often falls into local optimum for certain complex problems. To address these issues, a novel autonomous teaching-learning based optimization algorithm is proposed to solve the global optimization problems on the continuous space. Our proposed algorithm is remodeled according to the three phases of the teaching and learning process, learning from a teacher, mutual learning and self-learning among students instead of two phases of the original one. Moreover, the motivation and autonomy of students are considered in our proposed algorithm, and the expressions of autonomy are formulated. The performance of our proposed algorithm is compared with that of the related algorithms through our experimental results. The res...
Highlights
Optimization problems are solved commonly by mathematical methods to obtain the optimal solution, but it is basically very difficult to find the optimal solution within an acceptable time because the optimization problems in practice are NP-hard
The algorithms inspired by some biological phenomena including genetic algorithm (GA)[1], artificial immune algorithm (AIA)[2], ant colony optimization (ACO3, particle swarm optimization (PSO) 4, differential evolution algorithm (DE)[5], artificial bee colony algorithm (ABC) 6, chaos ant swarm optimization(CASO)[7] and so on
To verify the performance of our autonomous teaching-learning based optimization (ATLBO) algorithm, we devise two types of experiments, one is for the global convergence of the proposed algorithm, and the other is for the effectiveness of our algorithm
Summary
Optimization problems are solved commonly by mathematical methods to obtain the optimal solution, but it is basically very difficult to find the optimal solution within an acceptable time because the optimization problems in practice are NP-hard. Rao et al proposed a teaching-learning based optimization (TLBO)[14], which is under help of teaching and learning process of human. To avoid the disadvantages of TLBO and to improve the autonomy, we reconstruct an autonomous teaching-learning based optimization, called ATLBO, on the basis of the teaching and learning process. We propose a novel behavior-inspired swarm intelligence algorithm on the basis of the teaching and learning process. This population-based optimization algorithm demonstrates an outstanding performance in the global optimization. The rest of this paper is organized as follows: Section 2 presents the TLBO algorithm proposed by R. Where and are the lower bound and the upper bound of the independent variable , respectively
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