Combined Heat and Power Economic Emission Dispatch Using Hybrid NSGA II-MOPSO Algorithm Incorporating an Effective Constraint Handling Mechanism

This research work proposes a synergistic hybrid metaheuristic algorithm a merger of Nondominated Sorting Genetic Algorithm II and Multiobjective Particle Swarm Optimization algorithm for solving the highly complicated combined heat and power economic emission dispatch problem to operate the power system economically and to reduce the impact of environmental pollution. During the iteration, based on ranking, the population is divided into two halves. The exploration is carried out by Nondominated Sorting Genetic Algorithm II using the upper half of the population. The modification of Multiobjective Particle Swarm Optimization to effectively exploit the lower half of the population is done by increasing the personal learning coefficient, decreasing the global learning coefficient and by using an adaptive mutation operator. To satisfy the linear, nonlinear constraints, and to ensure the populations always lie in the Feasible Operating Region of the cogeneration plant, an effective constraint handling mechanism is developed. The proposed hybrid algorithm with an effective constraint handling mechanism enhances the searching capability by effective information interchange. The algorithm is applied to standard test functions and test systems while considering the valve point effects of the thermal plants, transmission power losses, bounds of the units and feasible operating region of the cogeneration units. The hybrid algorithm can obtain a well spread and diverse Pareto optimal solution and also can converge to the actual Pareto optimal front faster than some of the existing algorithms. The statistical analysis reveals that the proposed hybrid algorithm is a viable alternative to solve this complicated and vital problem.