School of Petroleum Engineering, Northeast Petroleum University, Daqing 163318, China.
Comput Intell Neurosci. 2018 Feb 20;2018:6094685. doi: 10.1155/2018/6094685. eCollection 2018.
Particle swarm optimization (PSO) and fireworks algorithm (FWA) are two recently developed optimization methods which have been applied in various areas due to their simplicity and efficiency. However, when being applied to high-dimensional optimization problems, PSO algorithm may be trapped in the local optima owing to the lack of powerful global exploration capability, and fireworks algorithm is difficult to converge in some cases because of its relatively low local exploitation efficiency for noncore fireworks. In this paper, a hybrid algorithm called PS-FW is presented, in which the modified operators of FWA are embedded into the solving process of PSO. In the iteration process, the abandonment and supplement mechanism is adopted to balance the exploration and exploitation ability of PS-FW, and the modified explosion operator and the novel mutation operator are proposed to speed up the global convergence and to avoid prematurity. To verify the performance of the proposed PS-FW algorithm, 22 high-dimensional benchmark functions have been employed, and it is compared with PSO, FWA, stdPSO, CPSO, CLPSO, FIPS, Frankenstein, and ALWPSO algorithms. Results show that the PS-FW algorithm is an efficient, robust, and fast converging optimization method for solving global optimization problems.
粒子群优化 (PSO) 和烟花算法 (FWA) 是两种最近开发的优化方法，由于其简单性和效率，已被应用于各个领域。然而，当应用于高维优化问题时，PSO 算法由于缺乏强大的全局探索能力而可能陷入局部最优，而烟花算法在某些情况下由于其对非核心烟花的相对较低的局部开发效率而难以收敛。在本文中，提出了一种称为 PS-FW 的混合算法，其中嵌入了 FWA 的修改算子到 PSO 的求解过程中。在迭代过程中，采用放弃和补充机制来平衡 PS-FW 的探索和开发能力，并提出了改进的爆炸算子和新颖的突变算子，以加快全局收敛速度并避免早熟。为了验证所提出的 PS-FW 算法的性能，使用了 22 个高维基准函数，并将其与 PSO、FWA、stdPSO、CPSO、CLPSO、FIPS、Frankenstein 和 ALWPSO 算法进行了比较。结果表明，PS-FW 算法是一种用于解决全局优化问题的高效、鲁棒、快速收敛的优化方法。
