Jason Lohn and his workmates in NASA Ames Research Center have evolved an X-band antenna for NASA's Space Technology 5 (ST5) mission which is the first evolved hardware in space, However their approach did not Put...
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ISBN:
(纸本)9783540921363
Jason Lohn and his workmates in NASA Ames Research Center have evolved an X-band antenna for NASA's Space Technology 5 (ST5) mission which is the first evolved hardware in space, However their approach did not Put too much attention on the efficiency of the evolutionary algorithm. Owing to the flaw we employ gt algorithm to tackle constraints and balance multi-objective via normalization which makes evolution more efficient. Moreover we adopt a kind of linear real-values code to describe the structure of antenna so that it is easier to carry out genetic operations and control the size of the antenna. We have evolved a wire antenna successfully via this approach and all the targets have been meted.
Job shop is one of a well known NP-hard optimization problems. In this paper, extremal optimization is proposed for job shop scheduling. Extremal optimization is an evolutionary meta-heuristic method that consecutivel...
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ISBN:
(纸本)9781467357135;9781467357128
Job shop is one of a well known NP-hard optimization problems. In this paper, extremal optimization is proposed for job shop scheduling. Extremal optimization is an evolutionary meta-heuristic method that consecutively substitutes undesirable variables in current solution with a random value and evolves itself toward optimal solution. For EO, the quality of generated initial solution plays an important role in convergence rate and reaching global optimum;hence gt method is utilized for initial solution. This algorithm is implemented on several sample problems on LA datasets and show that optimal solution can be reached quickly on most of the datasets.
This paper presents a parallel two-level evolutionary algorithm based on domain decomposition for solving function optimization problem containing multiple solutions. By combining the characteristics of the global sea...
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This paper presents a parallel two-level evolutionary algorithm based on domain decomposition for solving function optimization problem containing multiple solutions. By combining the characteristics of the global search and local search in each sub-domain, the former enables individual to draw closer to each optima and keeps the diversity of individuals, while the latter selects local optimal solutions known as latent solutions in sub-domain. In the end, by selecting the global optimal solutions from latent solutions in each sub-domain, we can discover all the optimal solutions easily and quickly.
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