An artificial bee colony algorithm with a Modified Choice Function for the traveling salesman problem

Choong, Shin Siang; Wong, Li-Pei; Lim, Chee Peng

doi:10.1016/j.swevo.2018.08.004

Cited by 104 publications

(52 citation statements)

References 67 publications

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“…Table 9 and Tab. 10 present the obtained results and compare them with the results of two highperforming metaheuristics, namely the Effective Strategies+ACO (ESACO) [21] and the Artificial Bee Colony with a Modified Choice Function (MFC-ABC) [9] (respectively). The ESACO was chosen as it is the current state-of-the-art ACO-based TSP solver.…”

Section: Instancementioning

confidence: 99%

“…Each thread starts with its own reservoir that has a size of one (as only one node has to be selected) and the corresponding key (lines 3 and 4). Next, the elements of the tabu are processed by p threads in parallel (loop in lines [6][7][8][9][10][11][12][13][14]. A thread, t, processes every p-th element and selects its own (locally) maximum key, T t , and the corresponding element (node) R t .…”

Section: Weighted Reservoir Samplingmentioning

confidence: 99%

“…The second algorithm, the MFC-ABC, combines a modified ABC metaheuristic with the well-known Lin-Kernighan heuristic. Extensive comparisons with the existing state-of-the-art metaheuristic TSP solvers showed that the MFC-ABC is very competitive in terms of both computation time and quality of the results [9].…”

Section: Instancementioning

confidence: 99%

“…Still, the results obtained for the GPU-based MMAS can be considered satisfactory with the relative error being below 0.5% for all but three (fnl4461, pla7397 and rl11849 ) instances for which it reached 0.58%. Unfortunately, the O(n 2 ) memory complexity of the MMAS prevents solving the largest TSP instance (with 85,900 cities) considered in [9] as it would require more than 16 GB of RAM offered by the V100 GPU.…”

Section: Instancementioning

confidence: 99%

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Implementing a GPU-based parallel MAX–MIN Ant System

Skinderowicz

2020

Future Generation Computer Systems

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The MAX-MIN Ant System (MMAS) is one of the best-known Ant Colony Optimization (ACO) algorithms proven to be efficient at finding satisfactory solutions to many difficult combinatorial optimization problems. The slow-down in Moore's law, and the availability of graphics processing units (GPUs) capable of conducting general-purpose computations at high speed, has sparked considerable research efforts into the development of GPU-based ACO implementations. In this paper, we discuss a range of novel ideas for improving the GPU-based parallel MMAS implementation, allowing it to better utilize the computing power offered by two subsequent Nvidia GPU architectures. Specifically, based on the weighted reservoir sampling algorithm we propose a novel parallel implementation of the node selection procedure, which is at the heart of the MMAS and other ACO algorithms. We also present a memory-efficient implementation of another keycomponent -the tabu list structure -which is used in the ACO's solution construction stage. The proposed implementations, combined with the existing approaches, lead to a total of six MMAS variants, which are evaluated on a set of Traveling Salesman Problem (TSP) instances ranging from 198 to 3,795 cities. The results show that our MMAS implementation is competitive with state-of-the-art GPU-based and multi-core CPU-based parallel ACO implementations: in fact, the times obtained for the Nvidia V100 Volta GPU were up to 7.18x and 21.79x smaller, respectively. The fastest of the proposed MMAS variants is able to generate over 1 million candidate solutions per second when solving a 1,002-city instance. Moreover, we show that, combined with the 2-opt local search heuristic, the proposed parallel MMAS finds high-quality solutions for the TSP instances with up to 18,512 nodes.

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Section: Instancementioning

confidence: 99%

Section: Weighted Reservoir Samplingmentioning

confidence: 99%

Section: Instancementioning

confidence: 99%

Section: Instancementioning

confidence: 99%

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Implementing a GPU-based parallel MAX–MIN Ant System

Skinderowicz

2020

Future Generation Computer Systems

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“…ABC was developed for continuous optimization problems, but there are several versions for discrete problems, including the Discrete Artificial Bee Colony (DABC), proposed by Karaboga and Gorkemli to solve the Euclidian-TSP [33]. Since it has been proposed, the DABC has been used to solve the TSP [34], the Operational Scheduling Problem (OSP) [35], and the Manufacturing Cell Design Problem (MCDP) [36]. In [37] the performance of DABC was compared to SA in an OSP.…”

Section: Discreet Artificial Bee Colony Artificial Bee Colonymentioning

confidence: 99%

The Influence of Problem Specific Neighborhood Structures in Metaheuristics Performance

Santos

Madureira

Varela

2018

Journal of Mathematics

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Metaheuristics (MH) aptitude to move past local optimums makes them an attractive technique to approach complex computational problems, such as the Travelling Salesman Problem (TSP), but there is lack of information on the parameterization procedure and the appropriate parameters to improve MHs’ performance. In this paper the parameterization procedure of Simulated Annealing (SA) and Discrete Artificial Bee Colony (DABC) is addressed, with a focus on the Neighborhood Structure (NS). Numerous NS have been proposed for specific problems, which seem to indicate that the NS is a special parameter, whose optimization is independent of other parameters. The performance of eight NS was examined with SA and DABC under two optimization constraints, regarding computational time variation, to determine if there is one appropriate NS for the TSP problem, independent of the rest of the parameters of the optimization procedure. The computational study carried out for comparing the evaluation of the NS, including a statistical analysis, demonstrated a nonproportional increase in the performance of DABC with some NS. For SA the improvement of the solutions appeared to be more uniform with an almost nonexistent variance in improvement.

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A cloud service composition method using a trust‐based clustering algorithm and honeybee mating optimization algorithm

Zanbouri

Navimipour

2019

Int J Communication

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Summary One of the most critical issues in using service‐oriented technologies is the combination of services, which has become an important challenge in the present. There are some significant challenges in the service composition, most notable is the quality of service (QoS), which is more challenging due to changing circumstances in dynamic service environments. Also, trust value in the case of selection of more reliable services is another challenge in the service composition. Due to NP‐hard complexity of service composition, many metaheuristic algorithms have been used so far. Therefore, in this paper, the honeybee mating optimization algorithm as one of the powerful metaheuristic algorithms is used for achieving the desired goals. To improve the QoS, inspirations from the mating stages of the honeybee, the interactions between honeybees and queen bee mating and the selection of the new queen from the relevant optimization algorithm have been used. To address the trust challenge, a trust‐based clustering algorithm has also been used. The simulation results using C# language have shown that the proposed method in small scale problem acts better than particle swarm optimization algorithm, genetic algorithm, and discrete gbest‐guided artificial bee colony algorithm. With the clustering and reduction of the search space, the response time is improved; also, more trusted services are selected. The results of the simulation on a large‐scale problem have indicated that the proposed method is exhibited worse performance than the average results of previous works in computation time.

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An artificial bee colony algorithm with a Modified Choice Function for the traveling salesman problem

Cited by 104 publications

References 67 publications

Implementing a GPU-based parallel MAX–MIN Ant System

Implementing a GPU-based parallel MAX–MIN Ant System

The Influence of Problem Specific Neighborhood Structures in Metaheuristics Performance

A cloud service composition method using a trust‐based clustering algorithm and honeybee mating optimization algorithm

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