Abstract
An improved teaching-learning-based optimization with combining of the social character of PSO (TLBO-PSO), which is considering the teacher's behavior influence on the students and the mean grade of the class, is proposed in the paper to find the global solutions of function optimization problems. In this method, the teacher phase of TLBO is modified; the new position of the individual is determined by the old position, the mean position, and the best position of current generation. The method overcomes disadvantage that the evolution of the original TLBO might stop when the mean position of students equals the position of the teacher. To decrease the computation cost of the algorithm, the process of removing the duplicate individual in original TLBO is not adopted in the improved algorithm. Moreover, the probability of local convergence of the improved method is decreased by the mutation operator. The effectiveness of the proposed method is tested on some benchmark functions, and the results are competitive with respect to some other methods.
Highlights
Global optimization is a concerned research area in science and engineering
Linear decreasing inertia weight particle swarm optimization (LDWPSO) [4] was introduced by Shi and Eberhart to overcome the lack of velocity control in standard PSO; the inertia weight of the algorithm decreases from a large value to a small one with increasing of evolution
An improved Teaching-Learning-Based Optimization (TLBO)-PSO algorithm which is considering the difference between the solutions of the best individual and the individual that want to be renewed is designed in the paper
Summary
Global optimization is a concerned research area in science and engineering. Many real-world optimization applications can be formulated as global optimization problems. Particle swarm optimization (PSO) algorithm [2] plays very important role in solving global optimization problems. Adaptive particle swarm optimization algorithm was introduced by Zhan to improve the performance of PSO, many operators were proposed to help the swarm jump out of the local optima and the algorithms have been evaluated on 12 benchmark functions [10, 11]. To improve the performance of TLBO algorithm for solving global optimization problems, an improved TLBO algorithm with combining of the social character of PSO, named TLBOPSO, is proposed.
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