Teaching-Learning-Based Optimization Algorithm in Dynamic Environments
In many real word problems, optimization problems are non-stationary and dynamic. Optimization of these dynamic optimization problems requires the optimization algorithms to be able to find and track the changing optimum efficiently over time. In this paper, for the first time, a multi-swarm teaching-learning-based optimization algorithm (MTLBO) is proposed for optimization in dynamic environment. In this method, all learners are divided up into several subswarms so as to track multiple peaks in the fitness landscape. Each learner is learning from the teacher and the mean of his or her corresponding subswarm instead of the teacher and the mean of the class in teaching phase, and then learners learn from interaction between themselves in their corresponding subswarm in learning phase. Moreover, all subswarms are regrouped periodically so that the information exchange is made with all the learners in the class to achieve proper exploration ability. The proposed MTLBO algorithm is evaluated on moving peaks benchmark problem in dynamic environments. The experimental results show the proper accuracy and convergence rate for the proposed approach in comparison with other well-known approaches.
KeywordsTeaching-Learning-Based Optimization Algorithm Dynamic Environments Multi-swarm Moving Peaks Benchmark problem
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