Abstract
In this paper, a new optimization methodology is proposed concerning to hybridization involving Genetic Algorithm and Particle Swarm Optimization (GA-PSO) with fuzzy adaptive inertial weight. In order to optimize multimodal problems with fast and non-premature convergence, hybridization is performed by combining the desirable features of GA and PSO. However, a slow and premature convergence can still occur due to the inefficient trade-off between global and local search. To this end, in this paper, a Mamdani fuzzy system is used for parametric adaptation of the inertial weight of the PSO, since through the size of the inertial weight it is possible to define whether the search will occur global or local manner.
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Noronha, R.P. (2022). A New GA-PSO Optimization Methodology with Fuzzy Adaptive Inertial Weight. In: Rayz, J., Raskin, V., Dick, S., Kreinovich, V. (eds) Explainable AI and Other Applications of Fuzzy Techniques. NAFIPS 2021. Lecture Notes in Networks and Systems, vol 258. Springer, Cham. https://doi.org/10.1007/978-3-030-82099-2_28
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DOI: https://doi.org/10.1007/978-3-030-82099-2_28
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