This paper proposes a Hierarchical Evolutionary-Deterministic Algorithm (HEDA) for designing squa... more This paper proposes a Hierarchical Evolutionary-Deterministic Algorithm (HEDA) for designing square grounding grids. This algorithm performs the design by means of a hierarchical coupling of a real coded evolutionary algorithm and the Hooke-Jeeves algorithm. The design of the grounding grid is here formalized as a min-max problem. The maximization part is the search of the most dangerous point for a given topological structure, the minimization part is the optimization of the topological parameter (compression ratio) of the grounding grid. The solution of the system of partial differential equations related to the spatial distribution of the current field is carried out by the Galerkin method. The program gives good results in terms of accuracy and computational complexity.
This paper proposes a novel adaptive local search algorithm for tackling real-valued (or continuo... more This paper proposes a novel adaptive local search algorithm for tackling real-valued (or continuous) dynamic optimization problems. The proposed algorithm is a simple single-solution based metaheuristic that perturbs the variables separately to select the search direction for the following step and adapts its step size to the gradient. The search directions that appear to be the most promising are rewarded by a step size increase while the unsuccessful moves attempt to reverse the search direction with a reduced step size. When the environment is subject to changes, a new solution is sampled and crosses over the best solution in the previous environment. Furthermore, the algorithm makes use of a small archive where the best solutions are saved. Experimental results show that the proposed algorithm, despite its simplicity, is competitive with complex population-based algorithms for tested dynamic optimization problems.
This paper proposes a neural network based approach for solving the resource discovery problem in... more This paper proposes a neural network based approach for solving the resource discovery problem in Peer to Peer (P2P) networks and an Adaptive Global Local Memetic Algorithm (AGLMA) for performing the training of the neural network. This training is very challenging due to the large number of weights and noise caused by the dynamic neural network testing. The AGLMA is a memetic algorithm consisting of an evolutionary framework which adaptively employs two local searchers having different exploration logic and pivot rules. Furthermore, the AGLMA makes an adaptive noise compensation by means of explicit averaging on the fitness values and a dynamic population sizing which aims to follow the necessity of the optimization process. The numerical results demonstrate that the proposed computational intelligence approach leads to an efficient resource discovery strategy and that the AGLMA outperforms two classical resource discovery strategies as well as a popular neural network training algorithm.
Implications in Business, Science, and Engineering, 2010
Chapter 1 Nature that Breeds Solutions Raymond Chiong Swinburne University of Technology (Sarawak... more Chapter 1 Nature that Breeds Solutions Raymond Chiong Swinburne University of Technology (Sarawak Campus), Malaysia Ferrante Neri University of Jyväskylä, Finland Robert I. McKay Seoul National University, Korea ABSTRACT Nature has always been a source of ...
... Ville Tirronen, Ferrante Neri, Tommi Karkkainen, Kirsi Majava, and Tuomo Rossi ... Morgan Kau... more ... Ville Tirronen, Ferrante Neri, Tommi Karkkainen, Kirsi Majava, and Tuomo Rossi ... Morgan Kaufmann / Elsevier (2004) 11. Caponio, A., Cascella, GL, Neri, F., Salvatore, N., Sumner, M.: A fast adaptive memetic algorithm for on-line and off-line control design of PMSM drives. ...
The authors propose a hierarchical evolutionary algorithm (HEA) to solve structural optimization ... more The authors propose a hierarchical evolutionary algorithm (HEA) to solve structural optimization problems. The HEA is composed by a lower level evolutionary algorithm (LLEA) and a higher level evolutionary algorithm (HLEA). The HEA has been applied to the design of grounding grids for electrical safety. A compact representation to describe the topology of the grounding grid is proposed. An analysis
EUROCON 2005 - The International Conference on "Computer as a Tool", 2005
In this paper the Authors suggest a software based on the genetic algorithms to optimize the desi... more In this paper the Authors suggest a software based on the genetic algorithms to optimize the design of a road lighting installation so as to reduce the use of the energy per year.
EUROCON 2005 - The International Conference on "Computer as a Tool", 2005
... 39-080-5714648; fax +39-080-5714655; e-mail: a-tecnica.unibaJ A. Covitti, A. Fusco, F. Lerari... more ... 39-080-5714648; fax +39-080-5714655; e-mail: a-tecnica.unibaJ A. Covitti, A. Fusco, F. Lerario and F. Neri are with ... OPTIMIZATION OF THE GROUNDING GRID DESIGN OF AN ELECTRIC POWER SUBSTATION HV/MV The method proposed by the Authors has been used to ...
The author proposes a nested evolutionary method making use of the potentialities of the genetic ... more The author proposes a nested evolutionary method making use of the potentialities of the genetic algorithms to design a grounding grid having a pre-arranged number of conductors. In all the designing steps the control is assigned to the maximum touch voltage whose value must be below the safety values referred to in IEEE standards. This maximum touch voltage is calculated
This paper proposes a Hierarchical Evolutionary-Deterministic Algorithm (HEDA) for designing squa... more This paper proposes a Hierarchical Evolutionary-Deterministic Algorithm (HEDA) for designing square grounding grids. This algorithm performs the design by means of a hierarchical coupling of a real coded evolutionary algorithm and the Hooke-Jeeves algorithm. The design of the grounding grid is here formalized as a min-max problem. The maximization part is the search of the most dangerous point for a given topological structure, the minimization part is the optimization of the topological parameter (compression ratio) of the grounding grid. The solution of the system of partial differential equations related to the spatial distribution of the current field is carried out by the Galerkin method. The program gives good results in terms of accuracy and computational complexity.
This paper proposes a novel adaptive local search algorithm for tackling real-valued (or continuo... more This paper proposes a novel adaptive local search algorithm for tackling real-valued (or continuous) dynamic optimization problems. The proposed algorithm is a simple single-solution based metaheuristic that perturbs the variables separately to select the search direction for the following step and adapts its step size to the gradient. The search directions that appear to be the most promising are rewarded by a step size increase while the unsuccessful moves attempt to reverse the search direction with a reduced step size. When the environment is subject to changes, a new solution is sampled and crosses over the best solution in the previous environment. Furthermore, the algorithm makes use of a small archive where the best solutions are saved. Experimental results show that the proposed algorithm, despite its simplicity, is competitive with complex population-based algorithms for tested dynamic optimization problems.
This paper proposes a neural network based approach for solving the resource discovery problem in... more This paper proposes a neural network based approach for solving the resource discovery problem in Peer to Peer (P2P) networks and an Adaptive Global Local Memetic Algorithm (AGLMA) for performing the training of the neural network. This training is very challenging due to the large number of weights and noise caused by the dynamic neural network testing. The AGLMA is a memetic algorithm consisting of an evolutionary framework which adaptively employs two local searchers having different exploration logic and pivot rules. Furthermore, the AGLMA makes an adaptive noise compensation by means of explicit averaging on the fitness values and a dynamic population sizing which aims to follow the necessity of the optimization process. The numerical results demonstrate that the proposed computational intelligence approach leads to an efficient resource discovery strategy and that the AGLMA outperforms two classical resource discovery strategies as well as a popular neural network training algorithm.
Implications in Business, Science, and Engineering, 2010
Chapter 1 Nature that Breeds Solutions Raymond Chiong Swinburne University of Technology (Sarawak... more Chapter 1 Nature that Breeds Solutions Raymond Chiong Swinburne University of Technology (Sarawak Campus), Malaysia Ferrante Neri University of Jyväskylä, Finland Robert I. McKay Seoul National University, Korea ABSTRACT Nature has always been a source of ...
... Ville Tirronen, Ferrante Neri, Tommi Karkkainen, Kirsi Majava, and Tuomo Rossi ... Morgan Kau... more ... Ville Tirronen, Ferrante Neri, Tommi Karkkainen, Kirsi Majava, and Tuomo Rossi ... Morgan Kaufmann / Elsevier (2004) 11. Caponio, A., Cascella, GL, Neri, F., Salvatore, N., Sumner, M.: A fast adaptive memetic algorithm for on-line and off-line control design of PMSM drives. ...
The authors propose a hierarchical evolutionary algorithm (HEA) to solve structural optimization ... more The authors propose a hierarchical evolutionary algorithm (HEA) to solve structural optimization problems. The HEA is composed by a lower level evolutionary algorithm (LLEA) and a higher level evolutionary algorithm (HLEA). The HEA has been applied to the design of grounding grids for electrical safety. A compact representation to describe the topology of the grounding grid is proposed. An analysis
EUROCON 2005 - The International Conference on "Computer as a Tool", 2005
In this paper the Authors suggest a software based on the genetic algorithms to optimize the desi... more In this paper the Authors suggest a software based on the genetic algorithms to optimize the design of a road lighting installation so as to reduce the use of the energy per year.
EUROCON 2005 - The International Conference on "Computer as a Tool", 2005
... 39-080-5714648; fax +39-080-5714655; e-mail: a-tecnica.unibaJ A. Covitti, A. Fusco, F. Lerari... more ... 39-080-5714648; fax +39-080-5714655; e-mail: a-tecnica.unibaJ A. Covitti, A. Fusco, F. Lerario and F. Neri are with ... OPTIMIZATION OF THE GROUNDING GRID DESIGN OF AN ELECTRIC POWER SUBSTATION HV/MV The method proposed by the Authors has been used to ...
The author proposes a nested evolutionary method making use of the potentialities of the genetic ... more The author proposes a nested evolutionary method making use of the potentialities of the genetic algorithms to design a grounding grid having a pre-arranged number of conductors. In all the designing steps the control is assigned to the maximum touch voltage whose value must be below the safety values referred to in IEEE standards. This maximum touch voltage is calculated
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