Metaheuristic approaches for optimal broadcasting design in ... - NEO
Metaheuristic approaches for optimal broadcasting design in ... - NEO
Metaheuristic approaches for optimal broadcasting design in ... - NEO
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ES<br />
Evolution Strategy<br />
Introduction<br />
Problem Def<strong>in</strong>ition<br />
Multiobjective Algorithms<br />
Experiments<br />
Conclusions and Future Work<br />
Evolutionary Algorithms<br />
Scatter Search<br />
Particle Swarm Optimization<br />
Hybrid algorithm between ES and NSGA-II<br />
NSGA-II selection scheme: rank<strong>in</strong>g and crowd<strong>in</strong>g<br />
No crossover, only mutation is applied to generate the offspr<strong>in</strong>g<br />
The variance of the mutation is updated depend<strong>in</strong>g on the successful<br />
replacements <strong>in</strong> the last n generations<br />
The new variation is used <strong>for</strong> generat<strong>in</strong>g the new offspr<strong>in</strong>g<br />
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