An Improved PSO Algorithm for Interval Multi-Objective Optimization Systems

Abstract

SUMMARYConsidering an uncertain multi-objective optimization system with interval coefficients, this letter proposes an interval multiobjective particle swarm optimization algorithm. In order to improve its performance, a crowding distance measure based on the distance and the overlap degree of intervals, and a method of updating the archive based on the acceptance coefficient of decision-maker, are employed. Finally, results show that our algorithm is capable of generating excellent approximation of the true Pareto front.

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“…To address this imbalance, two standard methods are used: the data-level approach, which adjusts the class imbalance ratio to balance the class distribution, and the algorithm-level approach, which improves the learning process for the minority class [21]. However, the algorithm-level method is ineffective when the imbalance ratio is high, so the data-level approach is preferred and involves modifying the class composition of the data [22], [23]. One commonly used method is resampling, which increases the minority class by removing samples from the majority class and adding samples from the minority class.…”