Due to emissions from power station using fossil fuels, the decrease of existing pollution as well as of operational costs should be
taken into consideration, when resolving environmental economic dispatch problems. In this research, we will evidence that
nonlinear constraints of generating units, forbidden regions, and ramp-rate of generating units will reduce operational costs and
environmental pollution, to achieve environmental economic dispatch effectiveness, by employing an improved multi-objective
optimization algorithm based on human social behavior. With reward and penalty learning factors leading to excellent particles
matting and optimization capability to achieve optimal solution, data transactions among particles have been conducted in the
suggested approach. To get a more effective comparable result from the recommended algorithm, we conducted simulation
experiments on IEEE 10-bus power systems in different load levels. Then we compared the outcomes with those other algorithms
that were validated. The results show that the proposed algorithm can achieve diverse Pareto optimal solutions, fast convergence
and high robustness, and unlikely to be trapped in local minima. It is revealed that the proposed technique is superior in terms of
accuracy and speed in solving power system complex problems over the other methods.