An Advanced Hybrid Algorithm (haDEPSO) for Engineering Design Optimization Integrating Novel Strategies for Enhanced Performance

Abstract

This research presents haDEPSO, a pioneering hybrid technique for engineering design optimization. Combining the strengths of Differential Evolution (DE) and Particle Swarm Optimization (PSO), haDEPSO offers a versatile answer to the difficulties of contemporary optimization settings. The methodology combines a precise integration of DE's robust exploration capabilities with PSO's efficient exploitation tactics, ensuring adaptability across diverse problem environments. Through 10 trials, performance measures such as fitness function value, convergence speed, and diversity meter reveal haDEPSO's consistent optimization power. Scalability testing reveals the algorithm's effectiveness in addressing situations of varying sizes, yet challenges occur in particularly massive instances. These findings contribute to a deep knowledge of haDEPSO's strengths and restrictions, driving subsequent advancements for better applicability in engineering design optimization.