A Populated Iterated Greedy Algorithm with Inver-Over Operator for Traveling Salesman Problem

Taşgetiren, M. Fatih; Büyükdağlı, Özge; Kızılay, Damla; Karabulut, Korhan

doi:10.1007/978-3-319-03753-0_1

Cited by 5 publications

(3 citation statements)

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“…The combination of operators in GA-TSP4 reflects a better computational performance than operators recently reported in the literature. For this comparison, the following operators were considered: GSTM [ 63 ], MIO [ 65 ], MA_IO [ 66 ], and IGP_IO [ 67 ]. The comparisons are shown in Table 12 .…”

Section: Resultsmentioning

confidence: 99%

Automatic Combination of Operators in a Genetic Algorithm to Solve the Traveling Salesman Problem

Contreras‐Bolton

Parada

2015

PLoS ONE

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Genetic algorithms are powerful search methods inspired by Darwinian evolution. To date, they have been applied to the solution of many optimization problems because of the easy use of their properties and their robustness in finding good solutions to difficult problems. The good operation of genetic algorithms is due in part to its two main variation operators, namely, crossover and mutation operators. Typically, in the literature, we find the use of a single crossover and mutation operator. However, there are studies that have shown that using multi-operators produces synergy and that the operators are mutually complementary. Using multi-operators is not a simple task because which operators to use and how to combine them must be determined, which in itself is an optimization problem. In this paper, it is proposed that the task of exploring the different combinations of the crossover and mutation operators can be carried out by evolutionary computing. The crossover and mutation operators used are those typically used for solving the traveling salesman problem. The process of searching for good combinations was effective, yielding appropriate and synergic combinations of the crossover and mutation operators. The numerical results show that the use of the combination of operators obtained by evolutionary computing is better than the use of a single operator and the use of multi-operators combined in the standard way. The results were also better than those of the last operators reported in the literature.

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Section: Resultsmentioning

confidence: 99%

Automatic Combination of Operators in a Genetic Algorithm to Solve the Traveling Salesman Problem

Contreras‐Bolton

Parada

2015

PLoS ONE

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“…For the small‐scale instances, 500 different runs with the selected parameters (in Table ) were performed for each of the benchmark instances. The solutions for the same instances of CEO and three other algorithms, honey‐bee mating optimization (HBMO) (Marinakis, Marinaki, and Dounias ), greedy subtour mutation (GSTM) (Albayrak and Allahverdi, ), and populated iterated greedy algorithm with inver‐over operator (IGP_IO) (Tasgetiren et al ), are shown in Table . From Table (the optimal solution has been highlighted), the following could be observed: CEO and HBMO can always find the optimal solution in a short time for all the instances, while GSTM and IGP_IO cannot. The HBMO is a competitive algorithm with all the best solution equaling optimum; however, the running time of CEO is almost half of that of HBMO.…”

Section: Simulations and Comparisonsmentioning

confidence: 99%

Crystal Energy Optimization Algorithm

Feng

2014

Computational Intelligence

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Nature has always been a muse for those who dream in art or science. As it goes, optimization algorithms inspired by nature have been widely used to solve various scientific and engineering problems because of their intelligence and simplicity. As a novel nature-inspired algorithm, the crystal energy optimizer (CEO) is proposed in this article. The proposed CEO is motivated by the following general observation on lake freezing in nature: the dynamics of crystals have possession of parallelism, openness, local interactivity, and self-organization. It stimulates us to extend a crystal dynamic model in physics to a generalized crystal energy optimizer for traveling salesman problems, so as to exploit the advantages of crystal dynamic system and to realize the aforementioned purposes. The proposed CEO has these advantages: (1) it has the ability to perform large-scale distributed parallel optimization; (2) it can converge and avoid local optimum; and (3) it is flexible and easy to adapt to a wide range of optimization problems.

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“…The IG algorithm is initially proposed by Feo and Resende [26]. It has been widely used to optimise numerous optimisation problems across various fields, such as combinatorial optimisation problems [27][28][29][30], feature selection, and data mining [31]. The IG algorithm is also used in manufacturing systems to solve flowshop scheduling problems, including flowshop [32], hybrid flowshop [33], flexible job shop (FJS) [34], and distributed flowshop [35].…”

mentioning

confidence: 99%

Ensemble evolutionary algorithms equipped with Q‐learning strategy for solving distributed heterogeneous permutation flowshop scheduling problems considering sequence‐dependent setup time

Liu,

Gao,

et al. 2024

IET Collab Intel Manufact

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A distributed heterogeneous permutation flowshop scheduling problem with sequence‐dependent setup times (DHPFSP‐SDST) is addressed, which well reflects real‐world scenarios in heterogeneous factories. The objective is to minimise the maximum completion time (makespan) by assigning jobs to factories, and sequencing them within each factory. First, a mathematical model to describe the DHPFSP‐SDST is established. Second, four meta‐heuristics, including genetic algorithms, differential evolution, artificial bee colony, and iterated greedy (IG) algorithms are improved to optimally solve the concerned problem compared with the other existing optimisers in the literature. The Nawaz‐Enscore‐Ham (NEH) heuristic is employed for generating an initial solution. Then, five local search operators are designed based on the problem characteristics to enhance algorithms' performance. To choose the local search operators appropriately during iterations, Q‐learning‐based strategy is adopted. Finally, extensive numerical experiments are conducted on 72 instances using 5 optimisers. The obtained optimisation results and comparisons prove that the improved IG algorithm along with Q‐learning based local search selection strategy shows better performance with respect to its peers. The proposed algorithm exhibits higher efficiency for scheduling the concerned problems.

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A Populated Iterated Greedy Algorithm with Inver-Over Operator for Traveling Salesman Problem

Cited by 5 publications

References 50 publications

Automatic Combination of Operators in a Genetic Algorithm to Solve the Traveling Salesman Problem

Automatic Combination of Operators in a Genetic Algorithm to Solve the Traveling Salesman Problem

Crystal Energy Optimization Algorithm

Ensemble evolutionary algorithms equipped with Q‐learning strategy for solving distributed heterogeneous permutation flowshop scheduling problems considering sequence‐dependent setup time

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