A Two-Stage Cooperative Multi-objective Evolutionary Differential Algorithm for Combined Heat and Power Economic Emission Dispatch

Combined heat and power economic emission dispatch (CHPEED) can obtain good economic and environmental benefits, but the dispatch problem presents non-convex, nonlinear, multi-constrained and multi-objective characteristics. Thus, a two-stage cooperative multi-objective differential evolutionary algorithm (TCADEA) is proposed in this paper. The algorithm uses a two-stage framework: the first stage uses a two-population strategy to divide the population into an elite population and an ordinary population, where the elite population is used to obtain better target values and the ordinary population is used to search the target space to ensure the diversity of the population and update the two populations by different adaptive differential operators. In addition, the ε constraint processing technique is used to handle the constraints. The second stage combines two populations into one and generates offspring through the constrained dominance principle (CDP) and adaptive differential evolution to maintain well-distributed population. The actual case results show that the TCADEA algorithm reduces $0.034 × 106, $0.008 × 106, $0.07 × 106 and 113 × 105 lb, 102 × 105 lb, 155 × 105 lb in fuel cost and emissions compared to MODE, NSGA-II, and TOP, respectively.