Clonal Selection Algorithm Sample Clauses

Clonal Selection Algorithm. ‌ A second proposed algorithm, ‘CLONALG’, is created by Xxxxx-Xxxxxxx and Xxxx (2015) and has generated most of the best-known solutions for the time-relaxed NLx instances 9,10. Xxxxx-Xxxxxxx and Xxxx (2015) introduce new components to the Clonal Selection Algorithm (CLONALG), an artificial immune algorithm based on clonal selection principle. The new version of CLONALG is called RAIST T P (artificial immune system for relaxed TTP). It can handle hard constraints that define the search space and soft constraints. The algorithm uses a penalty and fitness function to find solutions that satisfy the constraints. The penalty function consists of the average distance among teams and a penalty factor associated to the violation of a constraint. The fitness function is defined by the total travelled distance of all teams and the number of violated atmost and no repeat constraints in scheduling candidates. To make changes to the candidate solution, the five basic transformations proposed by Xxxxxxxxxxxxxxx et al. (2006) are used. Two additional move operators, ‘SwapByesMatches’ and ‘GroupingAwayUsingByes’ (cf. supra), are proposed. These move operators are only applicable to the RTTP problem since they focus on the byes of a team. 9xxxxx://xxx.xxxxxx.xxx.edu/TOURN/ 10xxxx://xxx.xxxxxxxxxxxxxxx.xxxxx.xx/RobinX/travelRepo.php‌ The RAIST T P algorithm is constructed as follows. First a single round-xxxxx schedule is created using the polygon method, as described by Xxxxxxx and Xxxxxxx (2007). To obtain the double round-xxxxx the single round-xxxxx is mirrored and venues are assigned randomly. After this initial schedule is constructed, the algorithm will follow a loop until the maximum of iterations is reached. An iteration starts with calculating the fitness of all available solutions. Next, the clonation rate is calculated to determine which percentage of the available solutions will be used and which percentage will be discarded. The remaining solutions will have the best fitness values and are therefore the closest to an optimal value. Next, a hypermutation is performed on all remaining solutions using the seven transformations. This will generate some new solutions that are potentially closer to an optimal objective function value. Next, a diversity selection is performed in order to select the most different solutions, in terms of home/away pattern, from the best ones. This step avoids that algorithm gets stuck in a local optimum. The last step of each iteratio...
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Related to Clonal Selection Algorithm

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