Authors - Murat Aydın Abstract - Combining Particle Swarm Optimization (PSO) with gradientbased local search enhances efficiency in solving complex optimization problems. Existing hybrids often use fixed switching rules, causing premature convergence orwastedcomputation.We present an adaptive PSO–gradient descent method where stagnation detection triggers local refinement only when needed. Adam is employed for local search without extra parameters. Tests on seven benchmark functions show the approach achieves strong or competitive results on challenging cases while ensuring robust convergence on simpler ones.
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