Sun Ying, Shi Wanyuan, Gao Yuelin
North Minzu University, Collaborative Innovation Center of Scientific Computing and Intelligent Processing in Ningxia, Yinchuan, Ningxia, China.
North Minzu University, School of Mathematics and Information Sciences, Yinchuan, Ningxia, China.
PeerJ Comput Sci. 2022 Dec 12;8:e1178. doi: 10.7717/peerj-cs.1178. eCollection 2022.
To solve the nonlinear constrained optimization problem, a particle swarm optimization algorithm based on the improved Deb criterion (CPSO) is proposed. Based on the Deb criterion, the algorithm retains the information of 'excellent' infeasible solutions. The algorithm uses this information to escape from the local best solution and quickly converge to the global best solution. Additionally, to further improve the global search ability of the algorithm, the DE strategy is used to optimize the personal best position of the particle, which speeds up the convergence speed of the algorithm. The performance of our method was tested on 24 benchmark problems from IEEE CEC2006 and three real-world constraint optimization problems from CEC2020. The simulation results show that the CPSO algorithm is effective.
为解决非线性约束优化问题，提出了一种基于改进的Deb准则的粒子群优化算法（CPSO）。该算法基于Deb准则，保留了“优秀”不可行解的信息。算法利用这些信息逃离局部最优解，快速收敛到全局最优解。此外，为进一步提高算法的全局搜索能力，采用差分进化（DE）策略优化粒子的个体最优位置，加快了算法的收敛速度。我们的方法在IEEE CEC2006的24个基准问题以及CEC2020的三个实际约束优化问题上进行了测试。仿真结果表明，CPSO算法是有效的。
