Particle swarm optimization (PSO) and differential evolution (DE) are two efficient meta-heuristic algorithms, achieving excellent performance in a wide variety of optimization problems. Unfortunately, when both algorithms are used to solve complex problems then they inevitably suffer from stagnation, premature convergence and unbalanced exploration–exploitation. Hybridization of PSO and DE may provide a platform to resolve these issues. Therefore, this paper proposes an innovative hybrid algorithm (ihPSODE) which would be more effective than PSO and DE. It integrated with suggested novel PSO (nPSO) and DE (nDE). Where in nPSO a new, inertia weight and acceleration coefficient as well as position update equation are familiarized, to escape stagnation. And in nDE a new, mutation strategy and crossover rate is introduced, to avoid premature convergence. In order to balance between global and local search capability, after calculation of ihPSODE population best half member has been identified and discard rest members. Further, in current population nPSO is employed to maintain exploration and exploitation, then nDE is used to enhance convergence accuracy. The proposed ihPSODE and its integrating component nPSO and nDE have been tested over 23 basic, 30 IEEE CEC2014 and 30 IEEE CEC2017 unconstrained benchmark functions plus 3 real life optimization problems. The performance of proposed algorithms compared with traditional PSO and DE, their existed variants/hybrids as well as some of the other state-of-the-art algorithms. The results indicate the superiority of proposed algorithms. Finally, based on overall performance ihPSODE is recommended for bound-unconstrained optimization problems in this present study.

粒子群优化（PSO）和差分进化（DE）是两种有效的元启发式算法，在各种优化问题中均具有出色的性能。不幸的是，当两种算法都用于解决复杂问题时，它们不可避免地会遭受停滞，过早收敛和不平衡的勘探开发之苦。PSO和DE的混合可以提供解决这些问题的平台。因此，本文提出了一种创新的混合算法（i hPSODE）比PSO和DE更有效。它与建议的新型PSO（nPSO）和DE（nDE）集成在一起。在nPSO中，要熟悉新的惯性权重和加速度系数以及位置更新方程，以免停滞。在nDE中，引入了新的变异策略和交叉率，以避免过早收敛。为了全局与局部搜索能力之间的平衡，我的计算后^ h PSODE人口一半最好的成员已经确定，并丢弃其余的成员。此外，在当前的人口中，使用nPSO来维持勘探和开发，然后使用nDE来提高收敛精度。提议我^ hPSODE及其集成组件nPSO和nDE已通过23种基本测试，30种IEEE CEC2014和30种IEEE CEC2017无约束基准测试以及3个实际优化问题进行了测试。与传统的PSO和DE，它们存在的变体/混合以及其他一些最新技术相比，所提出算法的性能。结果表明了所提出算法的优越性。最后，基于整体表现我^ h PSODE建议在本研究中的约束，无约束优化问题。