Solving combined economic emission dispatch problems using multiobjective hybrid evolutionary-barnacles mating optimization

Our daily life IS always related to realmulti-objective problems. We want to maximize or minimize many objectives, but it is impossible to improve the objectives without worsening other objectives. This paper presents the development of the Multi-objective Optimization Hybrid Evolutionary-Barnacles Mating Optimizer (MOHEBMO) algorithm in solving two objectives simultaneously to find the best trade-off using the weighted sum method. The MOHEBMO optimization algorithm is formulated based on the hybridization of Evolutionary Programming (EP) and Barnacles Mating Optimizer (BMO). The developed algorithm has been implemented in IEEE 30 Bus RTS which consists of six generators to solve the total generation cost and total emission. Two case studies have been selected to assess the efficiency of proposed MOHEBMO. To prove the capability of the suggested algorithm, it is compared with the results obtained from the existing algorithm MOEP, and MOBMO to solve the non-convex multiobjective combined economic emission dispatch (MOCEED) problems. The results obtained from MOHEBMO outperformed MOBMO and MOEP implying its superiority in determining the lowest optimal solution for the total generation cost and total emission.