TTSA Sample Clauses

TTSA. A first simulated annealing heuristic is proposed by Xxxxxxxxxxxxxxx et al. (2006). This algorithm improved the solutions for the NL12 , NL14 and NL16 instances. Based on existing simulated annealing techniques, the travelling tournament simulated annealing (TTSA) algorithm was developed. Some main design characteristics define this new method and lead to high-quality solutions. The first characteristic is the separation of constraints into two groups: hard constraints, which are always satisfied by the configurations, and soft constraints, which can be violated. A schedule that violates one or more soft constraints is called an infeasible schedule. There are basically two soft constraints for the TTP. The ‘no repeat’ constraint states that teams should not play two consecutive matches against the same opponent. The ‘atmost’ constraint limits the number of consecutive home or away games (Xxx Xxxxxxxxxx & Vergados, 2007). Due to the implementation of soft constraints, TTSA can explore both the feasible and the infeasible region which seems particularly important for this problem (Anagnostopoulos et al., 2006). The second characteristic arises from the selection of the move operators. The set of move operators ensures that the neighbourhood of any schedule is as large as possible. This large neighbourhood provides an extensive number of possible schedules that can be obtained from any given schedule. Attention 8xxxxx://xxx.xxxxxxxxxxxxxxxxxxxxxxxxxx.xxx/definition/american_english/anneal − should be paid to move operators that only perform a swap between two teams. These operators may require the change of 4(n 2) entries in a schedule to make it part of the search space again. A similar concept of ejection chains is also used in the xxxx search approach of Xx Xxxxxxx and Xxxxxxx (2007) (cf. Section 3.3.2.1). A third characteristic of TTSA is the implementation of a strategic oscillation strategy. By dynamically adjusting the objective function, the time spent in the feasible and infeasible region is balanced. This is done by adjusting the weight factor w, which penalises each violation of soft constraints with a specific weight, whenever a new schedule is found. After spending a long time in the infeasible region, w will become large and the penalty for violations increases with it. This will drive the search towards the feasible region. A last design characteristic is the usage of reheats in order to facilitate the escape from local minima in search of the ...
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