Recent advancements in artificial intelligence especially in evolutionary algorithms have enabled much efficient way to solve the constrained optimization problems in various fields of engineering. In this paper we explain the development of the optimization techniques from single criterion to multi criterion. We also address the Combined Economic and Emissions Dispatch (CEED) problem in thermal power stations. We use the aggregate objective function approach to solve the multiobjective CEEED problem using firefly optimization technique. Then we simulate the proposed approach on a test system and formulate the pareto optimal front for three different load demands. This proves that the algorithm can be effectively used to solve such multiobjective problems.

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Global Warming is the greatest challenge that the world is facing today. It is very important to take preventive measures before this problem poses a serious threat to the mankind. Power Plants play a major role in green house gas emissions. Nearly 21.3% of green house gases are emitted by these power plants alone. In this paper we try to propose a computational approach to reduce these carbon dioxide emissions by using Fire Fly Optimization Technique. The proposed optimization technique is better than other conventional optimization techniques in terms of computational time and number of iterations. It also shows that the method is practically applicable in real time. The technique was applied to a three generator and six generator systems for different load demands for validation. To check the quality of the solution the results are being compared with the conventional optimization algorithms.