Deep convolutional neural networks (CNNs) have exhibited exceptional performance in a range of computer vision tasks. However, these deep CNNs typically demand significant computational resources, which not only hinders their practical deployment but also contributes to a considerable carbon footprint. To tackle this issue, several filter pruning methods based on evolutionary algorithms have been proposed to provide significant memory and energy savings during CNN inference. However, due to the curse of high dimensionality in the structure of deep CNNs, the search space expands dramatically, presenting significant challenges for these methods. This paper proposes a novel algorithm called BPSO-FPruner for CNN filter pruning. BPSO-FPruner utilizes a constrained binary particle swarm optimization algorithm for filter pruning, incorporating a new initialization strategy based on filter weighting information and a reduced search space strategy. Extensive validation using VGG, ResNet, DenseNet, and MobileNetv2 architectures on the CIFAR-10, CIFAR-100, and Tiny ImageNet datasets demonstrates the effectiveness of BPSO-FPruner in reducing model computational costs and carbon footprint emissions while maintaining or improving performance.