A constrained cooperative adaptive multi-population differential evolutionary algorithm for economic load dispatch problems

Many engineering optimization problems are characterized by large scale and complex constraints. High optimization efficiency and reliable constraint handling are two major challenges. The traditional optimization methods hard to obtain practical and feasible solutions in reasonable time. To get better solutions and enhance the global search capability, a constrained cooperative adaptive multi-population differential evolutionary (CCAM-PDE) algorithm is proposed in this paper. The main contributions of this paper are in three aspects. First, a hyperspace dynamic constraint handling region between feasible region and infeasible region is proposed. Second, according to the feasible rate of population, a “one to one” or “one to many” subpopulation generation scheme is adopted for improving the global searching ability. Third, the selection operation of differential evolution algorithm is replaced by the elimination mechanism through the constraint handling technology. Eight economic load dispatch problems and CEC2017 Benchmark test functions are used to testify the performance of the CCAM-PDE algorithm. The experimental results shown that the CCAM-PDE algorithm has a strong constraint-handling efficiency and better global searching ability, its search accuracy and the speed of convergence against the other state-of-the-art algorithms.