Chaotic particle swarm optimization algorithm with constraint handling and its application in combined bidding model. (October 2021) - Peng, Feixiang; Hu, Shubo; Gao, Zhengnan; Zhou, Wei; Sun, Hui; Yu, Peng
Highlights: 1 A dual fitness value chaotic particle swarm optimization algorithm with equality constraints handling is proposed. 2 Equality constraints are solved by parametric equations and inequality constraints are handled by dual fitness evaluation criteria. 3 The chaotic-Cat-mapping-based mutation is implemented to ensure the diversity of particles. 4 Several mathematical test functions are utilized to illustrate the effectiveness of the proposed algorithm. 5 An application in a combined bidding model is implemented. Abstract: Particle swarm optimization (PSO) is a widely used intelligent optimization algorithm. The premature convergence and the constraint handling, however, remain problems for the global optimization searching. In this paper, a dual fitness value chaotic PSO algorithm with equality constraint handling (EDFC-PSO) is proposed. The particle mutation process based on the chaotic Cat mapping is introduced to increase the diversity of particles. Equality constraints are solved by parametric equations, and inequality constraints are considered using the dual fitness value. The proposed EDFC-PSO algorithm is tested by several test functions and then compared with other algorithms. Further, the performance of the proposed algorithm is validated by a combined bidding model of a power system. Our EDFC-PSO algorithm has equality constraints satisfied and enhances the global searching capability in both the test functions and the engineering model. Graphical abstract: Image, graphical abstract .