Swarm intelligence
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
A Variant of Evolution Strategies for Vector Optimization
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
An effective use of crowding distance in multiobjective particle swarm optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
On solving permutation scheduling problems with ant colony optimization
International Journal of Systems Science
State probability of a series-parallel repairable system with two-types of failure states
International Journal of Systems Science
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
A Pareto archive particle swarm optimization for multi-objective job shop scheduling
Computers and Industrial Engineering
Engineering Applications of Artificial Intelligence
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Information Sciences: an International Journal
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The objective proposed is of environmental/economic dispatch (EED) taking into account the environmental impact to achieve simultaneously the minimisation of fuel costs and pollutant emissions, while satisfying the operational constraints of power systems. The multiarea environmental/economic dispatch (MEED) deals with the optimal power dispatch of multiple areas (or countries). In this investigation, EED/MEED is proposed to address the environmental issue during the economic dispatch. In this article, the EED/MEED problem is first formulated and then a proposed Pareto archive multiobjective particle swarm optimisation (PAMPSO) algorithm is developed to derive a set of Pareto-optimal solutions. Its aim is to dispatch the power among different areas by simultaneously minimising the operational costs and pollutant emissions. In the proposed PAMPSO, 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 optimisation process. Numerical studies based on a four-area test power generation system are carried out to demonstrate the validity of the proposed optimisation method as well as the results from different problem formulations. Comparative results of PAMPSO and three other competitive multiobjective evolutionary algorithms (MOEAs) are also presented.