Particle swarm optimization method in multiobjective problems
Proceedings of the 2002 ACM symposium on Applied computing
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multiobjective optimization using dynamic neighborhood particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
MOPSO: a proposal for multiple objective particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
IEEE Transactions on Evolutionary Computation
On the role of population size and niche radius in fitness sharing
IEEE Transactions on Evolutionary Computation
Environmentally constrained economic dispatch using Pareto archive particle swarm optimisation
International Journal of Systems Science
Particle swarm optimization for solving combined economic and emission dispatch problems
EE'10 Proceedings of the 5th IASME/WSEAS international conference on Energy & environment
Economic load dispatch using improved harmony search
WSEAS Transactions on Systems and Control
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Reserve Constrained Multi-Area Economic Dispatch Employing Evolutionary Approach
International Journal of Applied Evolutionary Computation
Particle swarm optimization with increasing topology connectivity
Engineering Applications of Artificial Intelligence
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The objective of economic dispatch (ED) is to minimize the total operational cost while satisfying the operational constraints of power systems. Multiarea economic dispatch (MAED) deals with the optimal power dispatch of multiple areas. In this investigation, multiarea environmental/economic dispatch (MAEED) is proposed to address the environmental issue during the ED. Its target is to dispatch the power among different areas by simultaneously minimizing the operational costs and pollutant emissions. In this paper, the MAEED problem is first formulated and then an improved multiobjective particle swarm optimization (MOPSO) algorithm is developed to derive a set of Pareto-optimal solutions. In the proposed version of MOPSO, local search is used to increase its search efficiency. To ensure the system security, tie-line transfer limits between different areas are incorporated as a set of constraints in the optimization process. Moreover, the reserve-sharing scheme is applied to ensure that each area is able to fulfill its reserve requirement. Numerical studies based on a four-area test power generation system are carried out to demonstrate the validity of the proposed optimization method as well as the results from different problem formulations. Comparative results with respect to other optimization methods are also presented.