Survey on applications of biased-random key genetic algorithms for solving optimization problems

Prasetyo, Hari; Fauza, Gusti; Amer, Yousef; Lee, Sang‐Heon

doi:10.1109/ieem.2015.7385771

Cited by 14 publications

(7 citation statements)

References 59 publications

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“…Compared with standard GA methods, BRKGA offers more flexibility in encoding solutions [25] and produces as good or better solutions [26]. BRKGA has shown competitive performance on a series of optimization problems [27], including the satellite scheduling problem [28], which is very similar to the problem studied in this article. The ALNS algorithm was first proposed by Pisinger and Ropke [29].…”

Section: Related Workmentioning

confidence: 98%

Order acceptance and scheduling with sequence-dependent setup times: A new memetic algorithm and benchmark of the state of the art

Guijt

Weerdt

et al. 2019

Computers & Industrial Engineering

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The Order Acceptance and Scheduling (OAS) problem describes a class of real-world problems such as in smart manufacturing and satellite scheduling.This problem consists of simultaneously selecting a subset of orders to be processed as well as determining the associated schedule. A common generalization includes sequence-dependent setup times and time windows. A novel memetic algorithm for this problem, called Sparrow, comprises a hybridization of biased random key genetic algorithm (BRKGA) and adaptive large neighbourhood search (ALNS). Sparrow integrates the exploration ability of BRKGA and the exploitation ability of ALNS. On a set of standard benchmark instances, this algorithm obtains better-quality solutions with runtimes comparable to stateof-the-art algorithms. To further understand the strengths and weaknesses of these algorithms, their performance is also compared on a set of new benchmark instances with more realistic properties. We conclude that Sparrow is distinguished by its ability to solve difficult instances from the OAS literature, and that the hybrid steady-state genetic algorithm (HSSGA) performs well on large instances in terms of optimality gap, although taking more time than Sparrow.Adaptive large neighbourhood search, Order acceptance and scheduling, Sequence-dependent setup times 2. We compare Sparrow with state-of-the-art algorithms on a set of standard benchmark instances from the literature. The proposed algorithm obtains better-quality solutions with comparable running time.3. We study the correlation of the problem properties and the algorithm performance and find that the congestion ratio, the length of time windows, and the correlation of processing time and revenue of orders are highly related to the difficulty of the problem. 2 4. We further generate new instances that are more representative of real problem instances in satellite scheduling (more congestion), commerce (high correlation between revenue and processing time), and the travelling repairman problem (short processing times and long time windows), and compare the performance of multiple state-of-the-art algorithms on these new instances. The remainder of this article is summarized as follows: Section 2 provides 1 This algorithm combines a population-based genetic algorithm with adaptive large neighbourhood search. Each individual in a "swarm" thus has a bird's eye view of the search space -hence Sparrow. 2 These terms are defined in Section 4.3. Min Avg Max Min Avg Max Min Avg Max Min Avg Max Min Avg Max Min Avg Max Min Avg Max 0.10 0.10

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Section: Related Workmentioning

confidence: 98%

Order acceptance and scheduling with sequence-dependent setup times: A new memetic algorithm and benchmark of the state of the art

Guijt

Weerdt

et al. 2019

Computers & Industrial Engineering

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“…The parameters p max and p min are set at 1000 and 100, respectively. These values are recommended in the literature (Gonçalves and Resende 2016;Prasetyo et al 2015). The parameter γ is selected from the set {0.999, 0.998, 0.997} and depends on the available running time.…”

Section: Adaptive Brkga (A-brkga)mentioning

confidence: 99%

“…15, g k is the number of the current generation. According to the literature, the range of the number of elite individuals should vary between 10 and 25% of the population (Gonçalves and Resende 2016;Prasetyo et al 2015). The number of elite individuals is lower in the initial generations when the average quality of the individuals is low and this number increases in the subsequent generations.…”

Section: Adaptive Brkga (A-brkga)mentioning

confidence: 99%

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The multicommodity traveling salesman problem with priority prizes: a mathematical model and metaheuristics

Silva¹,

Chaves²,

Yanasse³

et al. 2019

Comp. Appl. Math.

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The classic Traveling Salesman Problem (TSP) only considers the costs involved in the routes and does not differentiate products or customers. Logistic companies face conflict between operational costs, customers with different categories of products, and customer satisfaction, which is directly related to delivery time. This paper presents a new mathematical model for a TSP with variable costs and priority prizes, taking into account the customer's product and preference values. This problem is denoted as the Multicommodity Traveling Salesman Problem with Priority Prizes (MTSPPP). Two versions of the Biased Random-Key Genetic Algorithm (BRKGA) are proposed to solve medium and large instances of the MTSPPP. Computational tests were performed, using modified instances based on classical TSP instances. The proposed methods have proved to be efficient in solving the MTSPPP.

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“…One GA in particular, the Biased Random Key Genetic Algorithm (BRKGA), introduced by Gonçalves and Resende [78], had many successful applications in telecommunications problems [7,79,80,81]. However, it has not yet been used for graph coloring applications.…”

Section: Other Remarksmentioning

confidence: 99%

The Rsi Allocation Problem: Exact and Heuristic Methods

LONDE¹

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Survey on applications of biased-random key genetic algorithms for solving optimization problems

Cited by 14 publications

References 59 publications

Order acceptance and scheduling with sequence-dependent setup times: A new memetic algorithm and benchmark of the state of the art

Order acceptance and scheduling with sequence-dependent setup times: A new memetic algorithm and benchmark of the state of the art

The multicommodity traveling salesman problem with priority prizes: a mathematical model and metaheuristics

The Rsi Allocation Problem: Exact and Heuristic Methods

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