A Hybrid Smart Quantum Particle Swarm Optimization for Multimodal Electromagnetic Design Problems

Quantum behaved particle swarm optimization (QPSO) has been one of the most widely used algorithm in engineering world. Since its debut 2004, QPSO for resolving numerous complicated multimodal problems. Moreover, considering adaptability and versatility, it resolved a variety real-world test To tackle numerical problems, we introduce novel hybrid QPSODE. The integrates with differential evolution (DE) strategy. A crossover selection (influenced by DE) is QPSODE’s position updating mechanism. During process, Boltzmann operator applied to vectors two randomly chosen particles, not their individual optimum placements. Therefore, unlike QPSO, only relocated new if higher fitness value, implying application strategy across whole search space. Additionally, improved introducing proper parameters tuning, control parameter, path disparity. enhances algorithm’s performance speeding up convergence avoiding premature convergence, main flaw earlier algorithms. proposed put test, using 19 well-known benchmark functions problem superconducting magnetic energy storage (SMES). In terms quality resulting outputs, QPSODE outperforms various state-of-the-art approaches.