Fuzzy Galactic Swarm Optimization with Dynamic Adjustment of Parameters Based on Fuzzy Logic In this work, a fuzzy method for dynamic adjustment of parameters in galactic swarm optimization is presented. Galactic swarm optimization is based on the movement of stars and galaxies in the universe, as well as their attractive influence allowing the use of multiple cycles of exploration and exploitation to solve complex optimization problems. It has been observed in the literature that the utilization of fuzzy systems for dynamic adjustment of parameters in metaheuristic algorithms produces good results when compared to using fixed parameters in the algorithms. In this work, the adjustment of the c3 and c4 parameters is made through the use of fuzzy systems because these parameters have a significant role in the operation of galactic swarm optimization. We tested the fuzzy approach with a set of benchmark mathematical functions and with the fuzzy controller of the water tank problem to measure the performance. Finally, a comparison of the results is presented among the proposed method and other metaheuristics.

Dynamic Multiobjective Evolutionary Algorithm with Fuzzy Weighted Borda Count Method This paper introduces a novel dynamic multiobjective 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 algorithmu0027s 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 u0027Change Effectu0027 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.