A competitive co-evolutionary Multi-Objective Genetic Algorithm (cc-MOGA) was used to approximate a Pareto front of efficient silvicultural regimes for Eucalyptus fastigata. The three objectives to be maximised includ...A competitive co-evolutionary Multi-Objective Genetic Algorithm (cc-MOGA) was used to approximate a Pareto front of efficient silvicultural regimes for Eucalyptus fastigata. The three objectives to be maximised included, sawlog, pulpwood and carbon sequestration payment. Three carbon price scenarios (3CPS), i.e. NZ $25, NZ $50 and NZ $100 for a tonne of CO2 sequestered, were used to assess the impact on silvicultural regimes, against a fourth non-carbon Pareto set of efficient regimes (nonCPS), determined from a cc-MOGA with two objectives, i.e. competing sawlog and pulpwood productions. Carbon prices included in stand valuation were found to influence the silvicultural regimes by increasing the rotation length and lowering the final crop number before clearfell. However, there were no significant changes in the frequency, timing, and intensity of thinning operations amongst all the four Pareto sets of solutions. However, the 3CPS were not significantly different from each other, which meant that these silvicultural regimes were insensitive to the price of carbon. This was because maximising carbon sequestration was directly related to the biological growth rate. As such an optimal mix of frequency, intensity, and timing of thinning maintained maximum growth rate for as long as possible for any one rotation.展开更多
In this paper, we study the interconnect buffer and wiresizing optimization problem under a distributed RLC model to optimize not just area and delay, but also crosstalk for RLC circuit with non-monotone signal respon...In this paper, we study the interconnect buffer and wiresizing optimization problem under a distributed RLC model to optimize not just area and delay, but also crosstalk for RLC circuit with non-monotone signal response. We present a new multiobjective genetic algorithm（MOGA） which uses a single objective sorting（SOS） method for constructing the non-dominated set to solve this multi-objective interconnect optimization problem. The MOGA/SOS optimal algorithm provides a smooth trade-off among signal delay, wave form, and routing area. Furthermore, we use a new method to calculate the lower bound of crosstalk. Extensive experimental results show that our algorithm is scalable with problem size. Furthermore, compared to the solution based on an Elmore delay model, our solution reduces the total routing area by up to 30%, the delay to the critical sinks by up to 25%, while further improving crosstalk up to 25.73% on average.展开更多
文摘A competitive co-evolutionary Multi-Objective Genetic Algorithm (cc-MOGA) was used to approximate a Pareto front of efficient silvicultural regimes for Eucalyptus fastigata. The three objectives to be maximised included, sawlog, pulpwood and carbon sequestration payment. Three carbon price scenarios (3CPS), i.e. NZ $25, NZ $50 and NZ $100 for a tonne of CO2 sequestered, were used to assess the impact on silvicultural regimes, against a fourth non-carbon Pareto set of efficient regimes (nonCPS), determined from a cc-MOGA with two objectives, i.e. competing sawlog and pulpwood productions. Carbon prices included in stand valuation were found to influence the silvicultural regimes by increasing the rotation length and lowering the final crop number before clearfell. However, there were no significant changes in the frequency, timing, and intensity of thinning operations amongst all the four Pareto sets of solutions. However, the 3CPS were not significantly different from each other, which meant that these silvicultural regimes were insensitive to the price of carbon. This was because maximising carbon sequestration was directly related to the biological growth rate. As such an optimal mix of frequency, intensity, and timing of thinning maintained maximum growth rate for as long as possible for any one rotation.
基金Supported by the National Natural Science Foundation of China (90307017)
文摘In this paper, we study the interconnect buffer and wiresizing optimization problem under a distributed RLC model to optimize not just area and delay, but also crosstalk for RLC circuit with non-monotone signal response. We present a new multiobjective genetic algorithm（MOGA） which uses a single objective sorting（SOS） method for constructing the non-dominated set to solve this multi-objective interconnect optimization problem. The MOGA/SOS optimal algorithm provides a smooth trade-off among signal delay, wave form, and routing area. Furthermore, we use a new method to calculate the lower bound of crosstalk. Extensive experimental results show that our algorithm is scalable with problem size. Furthermore, compared to the solution based on an Elmore delay model, our solution reduces the total routing area by up to 30%, the delay to the critical sinks by up to 25%, while further improving crosstalk up to 25.73% on average.
