The simulated annealing optimization technique has been successfully applied to a number of electrical engineering problems, including transmission system expansion planning. The method is general in the sense that it does not assume any particular property of the problem being solved, such as linearity or convexity. Moreover, it has the ability to provide solutions arbitrarily close to an optimum (i. e. it is asymptotically convergent) as the cooling process slows down. The drawback of the approach is the computational burden: finding optimal solutions may be extremely expensive in some cases. This paper presents a Parallel Simulated Annealing, PSA, algorithm for solving the long term transmission network expansion planning problem. A stra...
The paper presents an extended genetic algorithm for solving the optimal transmission network expans...
We have investigated and extensively tested three families of non-convex optimization approaches for...
Initial Solutions (IS) are decisive in meta-heuristics based optimization problems since they impact...
The simulated annealing optimization technique has been successfully applied to a number of electric...
This paper presents a simulated annealing approach to the long term transmission expansion planning ...
The transmission network expansion planning problem is effectively solved by the improved simulated ...
The transmission network expansion planning problem is effectively solved by the improved simulated ...
The transmission network expansion planning problem is effectively solved by the improved simulated ...
Simulated Annealing (SA) is a powerful tool for optimization problems that have several local optima...
The paper presents an extended genetic algorithm for solving the optimal transmission network expans...
It is well known that the Transmission Expansion Planning (TEP) is a formidable combinatorial proble...
This paper proposes a Simulated Annealing (SA) based method for distribution network expansion inclu...
[[abstract]]This paper combines network window schema with a simulated annealing (SA) technique and ...
The optimal power flow problem has been widely studied in order to improve power systems operation a...
The paper presents an extended genetic algorithm for solving the optimal transmission network expans...
The paper presents an extended genetic algorithm for solving the optimal transmission network expans...
We have investigated and extensively tested three families of non-convex optimization approaches for...
Initial Solutions (IS) are decisive in meta-heuristics based optimization problems since they impact...
The simulated annealing optimization technique has been successfully applied to a number of electric...
This paper presents a simulated annealing approach to the long term transmission expansion planning ...
The transmission network expansion planning problem is effectively solved by the improved simulated ...
The transmission network expansion planning problem is effectively solved by the improved simulated ...
The transmission network expansion planning problem is effectively solved by the improved simulated ...
Simulated Annealing (SA) is a powerful tool for optimization problems that have several local optima...
The paper presents an extended genetic algorithm for solving the optimal transmission network expans...
It is well known that the Transmission Expansion Planning (TEP) is a formidable combinatorial proble...
This paper proposes a Simulated Annealing (SA) based method for distribution network expansion inclu...
[[abstract]]This paper combines network window schema with a simulated annealing (SA) technique and ...
The optimal power flow problem has been widely studied in order to improve power systems operation a...
The paper presents an extended genetic algorithm for solving the optimal transmission network expans...
The paper presents an extended genetic algorithm for solving the optimal transmission network expans...
We have investigated and extensively tested three families of non-convex optimization approaches for...
Initial Solutions (IS) are decisive in meta-heuristics based optimization problems since they impact...