Local manipulations of the fitness landscape mitigate stall in particle swarm optimization

Real-world optimization problems often exhibit multimodality, non-convexity, and stochasticity, which limit the effectiveness of classical algorithms. Gradient-free bio-inspired metaheuristics evolve a population of candidate solutions to address such challenges and find the global optimum. Particle Swarm Optimization (PSO) leverages a swarm of particles (i.e., candidate solutions) that move in the search space attracted by their personal best and the global best solutions. However, similar to other metaheuristics, PSO is prone to stalling, whereby particles fail to escape local minima and reach more promising basins of attraction. We address this issue through Local Bubble Dilation Functions (LBDFs), which locally reshape the landscape around any particle that might have lost its exploration capability. A velocity-based criterion first identifies the stalling particles, distinguishing them from the exploratory ones; then, an LBDF compresses the region around a stalling particle, pushing it away from the bubble center. We integrate this stall remedy—Particle Stall Avoidance through Landscape Manipulation (PSALM)—into Fuzzy Self-Tuning PSO (FST-PSO). We evaluate its effectiveness on the Rastrigin function and on the suite of benchmark functions CEC2017, showing that PSALM improves the search capabilities of FST-PSO by escaping overexploited—and possibly unpromising—areas of the search space, with statistically significant improvements in the best solutions found.