Browsing by Subject "Evolutionary algorithm"

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Open Access
Binary sequences with low aperiodic autocorrelation for synchronization purposes
(IEEE, 2003) Kocabaş, Ş. E.; Atalar, Abdullah
An evolutionary algorithm is used to find three sets of binary sequences of length 49-100 suitable for the synchronization of digital communication systems. Optimization of the sets are done by taking into consideration the type of preamble used in data frames and the phase-lock mechanism of the communication system. The preamble is assumed to be either a pseudonoise (PN) sequence or a sequence of 1's. There may or may not be phase ambiguity in detection. With this categorization, the first set of binary sequences is optimized with respect to aperiodic autocorrelation which corresponds to the random (PN) preamble without phase ambiguity case. The second and third sets are optimized with respect to a modified aperiodic autocorrelation for different figures of merit corresponding to the predetermined preamble (sequence of 1's) with and without phase ambiguity cases.
Open Access
Genetic algorithm applications for the vehicle routing problem with roaming delivery locations
(2021-06) Turhan, Serkan
The recent innovations in the e-commerce industry developed a new delivery option where the orders of the customers can be delivered to the trunks of their cars. Compared to the conventional home-delivery, this option is not only able to decrease the total distance traveled but also increase the customer satisfaction by decreasing the number of failed deliveries. The problem introduced by this option is called the vehicle routing problem with roaming delivery locations. This thesis proposes a new, time-eﬃcient solution construction strategy for the problem. The construction strategy is able to represent any feasible solution for the problem and has a complexity linearly increasing with the number of delivery nodes in the problem. Based on the constructor, a new genetic algorithm to ﬁnd and improve solutions to near-optimal within polynomial time is proposed. Furthermore, a separate, new ﬁne-tuning algorithm to improve the parameters of the genetic algorithm for a given set of problem instances is proposed. The most notable feature of the proposed genetic algorithm is the time-eﬃciency as it is able to construct a solution within milliseconds for the largest problem instance available in the literature and the computation time scales with the problem size linearly. Parallel computing can be implemented in both the ﬁne-tuning and the genetic algorithm, which allows better results in a shorter processing time. Within 5 minutes of computation time, the ﬁne-tuned genetic algorithm found optimal solutions in 8 out of 19 instances with known optimal solutions, moreover, it was able to ﬁnd solutions better than the previous solution methodologies in 12 out of 60 instances used in our experiments. Gaps between the results of the proposed genetic algorithm and the best solution found by a commercial solver (CPLEX) are between (0.0%, 26.2%) and (-6.0%, 16.3%) in small-medium and large instances respectively.