Leveraging an Improved Particle Swarm Optimization Algorithm to Solve Combined Heat and Power Economic Dispatch Problems

Combined heat and power economic dispatch (CHPED) problems are non-convex and non-linear, posing significant challenges that traditional numerical methods struggle to solve. In this paper, a novel adaptive particle swarm optimization (PSO) algorithm called dynamical nonlinear inertial weight particle swarm optimization (DNIWPSO) is proposed, and the DNIWPSO algorithm is applied to tackle CHPED problems. First, the DNIWPSO algorithm is proposed. The DNIWPSO algorithm avoids convergence to local optima and enhances robustness. The numerical simulations of benchmark functions demonstrate that the DNIWPSO algorithm outperforms the common conventional PSO algorithm and the linearly decreasing inertia weight particle swarm optimization (LDIWPSO) algorithm. Second, the DNIWPSO algorithm is applied to CHPED problems, and thus a DNIWPSO-based approach for CHPED is presented. Third, the effectiveness and superiority of the DNIWPSO-based methodology for solving CHPED problems are verified by a series of CHPED experiments.