Ant Colony Optimization: A Component-Wise Overview

López-Ibáñez, Manuel; Stützle, Thomas; Dorigo, Marco; Martı́, Rafael; Pardalos, Pãnos M.; Resende, Maurício G. C.

doi:10.1007/978-3-319-07124-4_21

Cited by 27 publications

(22 citation statements)

References 104 publications

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“…It has been successfully applied in solving many NP-hard problems, including the Traveling Salesman Problem (TSP), the Quadratic Assignment Problem, and the Sequential Ordering Problem [40,43,44]. Being metaheuristic, the ACO does not guarantee finding an optimum solution, however it is often able to offer satisfactory approximate solutions within an acceptable time compared to exact methods [28]. However, even metaheuristics can be prohibitively time consuming if faced with a large enough problem instance.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

Implementing a GPU-based parallel MAX–MIN Ant System

Skinderowicz

2020

Future Generation Computer Systems

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“…First, the pheromone values are restricted to the interval of [0.01, 0.99] to prevent stagnation, so that there will not be too large pheromone values that some tasks tend to be assigned to the same workstation, and there will not be too small pheromone values that some tasks tend to avoid being assigned to a workstation. Thus, stagnation can be prevented [32]. Also, there is an evaporation process when update the pheromone values, so it is discouraged to assign one task to the same position.…”

Section: Discussionmentioning

confidence: 99%

“…Consequently, the solution space is small, and this may lead to bad quality of the solutions generated. Thus, the pheromone values are restricted to the interval [ min , max ] to prevent stagnation [32], and min = 0.01 and max = 0.99. If a pheromone value is larger than max after updating, it will be replaced by max ; if the value is smaller than min after updating, it is replaced by min .…”

Section: Aco-bs Algorithmmentioning

confidence: 99%

Assembly Line Balancing Based on Beam Ant Colony Optimisation

Huo

Wang

Chan

et al. 2018

Mathematical Problems in Engineering

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We use a hybrid approach which executes ant colony algorithm in combination with beam search (ACO-BS) to solve the Simple Assembly Line Balancing Problem (SALBP). The objective is to minimise the number of workstations for a given fixed cycle time, in order to improve the solution quality and speed up the searching process. The results of 269 benchmark instances show that 95.54% of the problems can reach their optimal solutions within 360 CPU time seconds. In addition, we choose order strength and time variability as indicators to measure the complexity of the SALBP instances and then generate 27 instances with a total of 400 tasks (the problem size being much larger than that of the largest benchmark instance) randomly, with the order strength at 0.2, 0.6 and 0.9 three levels and the time variability at 5-15, 65-75, and 135-145 levels. However, the processing times are generated following a unimodal or a bimodal distribution. The comparison results with solutions obtained by priority rule show that ACO-BS makes significant improvements on the quality of the best solutions.

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“…To improve the global search capability and convergence performance of GAs, Wang et al [20] proposed four types of improved GAs, namely, hierarchic GAs, simulated annealing GAs, simulated annealing hierarchic GAs, and adaptable GAs; these methods can overcome the defects of traditional GAs by combining GAs with simulated annealing algorithms and modifying various coding methods. In terms of ACO, the main features of its improvement involve mechanisms to intensify the search involving high-quality solutions and preserve a sufficient search space [21]. Niu et al [22] stated that as a typical greedy heuristic algorithm, ACO tends to become trapped in local optima.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Fused Optimization Algorithm of Genetic Algorithm and Ant Colony Optimization

Zhao

Yao

Luan

et al. 2016

Mathematical Problems in Engineering

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A novel fused algorithm that delivers the benefits of both genetic algorithms (GAs) and ant colony optimization (ACO) is proposed to solve the supplier selection problem. The proposed method combines the evolutionary effect of GAs and the cooperative effect of ACO. A GA with a great global converging rate aims to produce an initial optimum for allocating initial pheromones of ACO. An ACO with great parallelism and effective feedback is then served to obtain the optimal solution. In this paper, the approach has been applied to the supplier selection problem. By conducting a numerical experiment, parameters of ACO are optimized using a traditional method and another hybrid algorithm of a GA and ACO, and the results of the supplier selection problem demonstrate the quality and efficiency improvement of the novel fused method with optimal parameters, verifying its feasibility and effectiveness. Adopting a fused algorithm of a GA and ACO to solve the supplier selection problem is an innovative solution that presents a clear methodological contribution to optimization algorithm research and can serve as a practical approach and management reference for various companies.

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Ant Colony Optimization: A Component-Wise Overview

Cited by 27 publications

References 104 publications

Implementing a GPU-based parallel MAX–MIN Ant System

Implementing a GPU-based parallel MAX–MIN Ant System

Assembly Line Balancing Based on Beam Ant Colony Optimisation

A Novel Fused Optimization Algorithm of Genetic Algorithm and Ant Colony Optimization

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