PPT-CMPE536 METAHEURISTICS
Author : elise | Published Date : 2023-10-29
Asst Prof Dr Ahmet ÜNVEREN 20 20 20 21 FALL Dr Ünveren 1 Practical Issues The lecturer Asst Prof Dr Ahmet ÜNVEREN Email ahmet unverenemuedutr
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CMPE536 METAHEURISTICS: Transcript
Asst Prof Dr Ahmet ÜNVEREN 20 20 20 21 FALL Dr Ünveren 1 Practical Issues The lecturer Asst Prof Dr Ahmet ÜNVEREN Email ahmet unverenemuedutr. Hazem Ali, Borislav Nikoli. ć,. Kostiantyn Berezovskyi, Ricardo Garibay Martinez, Muhammad Ali Awan. Outline. Introduction. Non-Population Metaheuristics. Population Metaheuristics. Genetic Algorithims (GA). January 3. rd. , 2011. Metaheuristics: GRASP. Group 1:. Clara . Gouveia. Daniel Oliveira. . Fabrício. . Sperandio. . Filipe Sousa . [Presenter]. FEUP | PDEEC | Decision Support. Metaheuristics: GRASP. Single State Methods. Big Assumptions:. you can compute the first derivative. or even the second derivative. These assumptions are valid only if the function is:. well-formed and well-understood. However, in most cases:. NP-Hard. Conflicting objectives. Flow shop with both minimum . makespan. and tardiness objective. TSP problem with minimum distance, time and cost objective. Container management – balancing volume, weight and value. Nature-inspired. Initialize a population. A new population of solutions is generated. Integrate the new population into the current one using one these methods – by replacement which is a selection process from the new and current solutions.
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