Different methodologies have been proposed to solve

these types of multiobjective environmental/economic dispatch problem. In 4

the problem is reduced to a single objective problem by considering the

emission as a constraint with a permissible limit condition. These

multi-objective environmental/economic dispatch problems have different

difficulties during dispatch solution to get correct relation between the cost

and emission. So fuel cost and emissions are to be treated as objective

functions. In paper 5, the linear programming based optimization technique in

which both emission and fuel cost functions as objective function. But by

considering both these functions as objective functions, the

environmental/economic (EED) problem becomes a complicated nonlinear

optimization problem. So many conventional methodologies are available in order

to obtain global optimal solution but all those methods can’t attain the global

solution. Many techniques based on analytic methods having differential

objective functions having the advantage to simplify the above formulated On

the other hand, many mathematical assumptions such as analytic and differential

objective functions have to be given to simplify the above mentioned

environmental/economic (EED) problem. There are so many methods are now

available to consider these two cost function and emission function together

into a single objective function using the

linear combination of two objectives as

a weighted sum which is explained 6 and 7. A Pareto optimal solution is

obtained by above mentioned weighted sum method by varying the weights. But

much iteration is required to obtain this Pareto optimal global solution. All

these methods can’t use to obtain the global solution due to non convex Pareto

optimal front. So in 8 and 9, a method is explained to overcome the above

difficulty which is the ?-constraint method for the solution of multiobjective

optimization. This method is based on getting most optimized solution of

multiobjective objective function with some constraints having permissible limits.

Recently some modern techniques such as

genetic algorithm, evolutionary programming, artificial bee colony, tabu search

algorithm, ant colony, bacterial foraging, flower pollination technique,

efficient particle swarm optimization technique, genetic algorithm with

simulated annealing process have been emerged to solve the complex non linear

problem. During the dispatch we can face some problems of

handling non smooth fuel cost function and emission function. So an effective

and efficient optimization technique is required to solve multiobjective

function. Here this paper proposes the use of Particle Swarm Optimization (PSO)

to solve the bi-objective function which is a global searching technique.