Abstract
This paper introduces a novel dynamic multi-objective optimization algorithm. When a change occurs, the optimization algorithm has to give a true response to change. One of the most popular approaches of responding to change is to select some solutions randomly and remove them from the population. Since random selection results in deterioration of the algorithm's performance, the wise selection is crucial. Recently, the Borda count method was applied to find the most appropriate solutions to be eliminated from the population. However, basic Borda suffers from the same weight of information before and after the change. In this paper, we propose a new weighted Borda count so that its parameters are tuned by fuzzy rules. Mamdani fuzzy rules have been employed to tune the weights and distinguish between information before and after the change. Finally, the 'Change Effect' is proposed to calculate the effect of the change on the solutions. The performance of the proposed algorithm is tested on standard functions and is compared with recent algorithms.
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