Comparison of parallel metaheuristics for solving the TSP

Lazarova, Milena; Borovska, Plamenka

doi:10.1145/1500879.1500899

Cited by 13 publications

(6 citation statements)

References 11 publications

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“…By comparing the effectiveness of Ant Colony Optimization algorithm (ACO), Genetic Algorithm (GA), and Simulated Annealing (SA) in seeking near-optimal solutions in the traveling salesman problem ( TSP), it is concluded that ACO provided a better performance regarding speed and solution quality [23]. Also, ACO has been compared with GA, Particle Swarm Optimization (PSO) in dealing with site layout problems.…”

Section: Model Development and Assessmentmentioning

confidence: 99%

Optimization of construction site layout using dynamic hybrid bacterial and ant colony algorithm

Son

2021

STCE

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The efficient plan of site arrangement during the construction phase has been considered a vital duty to successful project performance due to the productivity enhancement as well as safety in executions. The optimization of the Construction Site Layout Problem (CSLP) is a combinatorial complexity that regards numerous objectives and considerable growth of scale as increasing of constraints and facilities. The rearrangement on site may thus need to be had dynamic plannings at several interval schedules as construction evolves to accommodate site needs. To resolve the complexity of this problem, many studies based on the Meta-heuristic approach have been done, there are however always drawbacks and should be improved to be more optimal. This research proposes a new Hybrid Meta-heuristic model which is a combination of Ant Colony Optimization algorithm (ACO), Bacterial foraging algorithm (BFA), and Pair-Wise Exchange Heuristic algorithm (PWEH). The proposed algorithm is named Dynamic Hybrid Ant Colony Algorithm (DHACA) model that can enhance local and global searches simultaneously. In addition, parameter values are optimized to create a better solution. This research also demonstrates the effectiveness of DHACA compared with the previous studies such as Multi-objectives Genetic Algorithm (MOGA), Simulated Annealing Algorithm based Multi-objectives Genetic Algorithm (SA-based MOGA) on the CSLP. DHACA supports the construction site dynamic planning with constraints on facilities to improve work efficiency.

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Section: Model Development and Assessmentmentioning

confidence: 99%

Optimization of construction site layout using dynamic hybrid bacterial and ant colony algorithm

Son

2021

STCE

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“…Tabu search [Rudek, 2014, Jin et al, 2012, Bozejko et al, 2017, Bozejko et al, 2013, Czapinski and Barnes, 2011, James et al, 2009, Czapiński, 2013, Bukata et al, 2015, Cordeau and Maischberger, 2012, Wei et al, 2017, Janiak et al, 2008, Shylo et al, 2011, Jin et al, 2014, Bożejko et al, 2016, Jin et al, 2011, Maischberger and Cordeau, 2011, Van Luong et al, 2013, Dai et al, 2009] Simulated annealing [Thiruvady et al, 2016, Rudek, 2014, Defersha, 2015, Mu et al, 2016, Ferreiro et al, 2013, Lou and Reinitz, 2016, Banos et al, 2016, 2016, Lazarova and Borovska, 2008 Variable neigborhood search [Yazdani et al, 2010, Lei and Guo, 2015, Davidović and Crainic, 2012, Quan and Wu, 2017, Menendez et al, 2017, Eskandarpour et al, 2013, Coelho et al, 2016, Polat, 2017, Tu et al, 2017, Aydin and Sevkli, 2008, Polacek et al, 2008 (Greedy randomized)…”

Section: Algorithm Typementioning

confidence: 99%

Parallel computational optimization in operations research: A new integrative framework, literature review and research directions

Schryen

2020

European Journal of Operational Research

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Solving optimization problems with parallel algorithms has a long tradition in OR. Its future relevance for solving hard optimization problems in many fields, including finance, logistics, production and design, is leveraged through the increasing availability of powerful computing capabilities. Acknowledging the existence of several literature reviews on parallel optimization, we did not find reviews that cover the most recent literature on the parallelization of both exact and (meta)heuristic methods. However, in the past decade substantial advancements in parallel computing capabilities have been achieved and used by OR scholars so that an overview of modern parallel optimization in OR that accounts for these advancements is beneficial. Another issue from previous reviews results from their adoption of different foci so that concepts used to describe and structure prior literature differ. This heterogeneity is accompanied by a lack of unifying frameworks for parallel optimization across methodologies, application fields and problems, and it has finally led to an overall fragmented picture of what has been achieved and still needs to be done in parallel optimization in OR. This review addresses the aforementioned issues with three contributions: First, we suggest a new integrative framework of parallel computational optimization across optimization problems, algorithms and application domains. The framework integrates the perspectives of algorithmic design and computational implementation of parallel optimization. Second, we apply the framework to synthesize prior research on parallel optimization in OR, focusing on computational studies published in the period 2008-2017. Finally, we suggest research directions for parallel optimization in OR.Keywords computing science · parallel optimization · computational optimization · literature review * I am grateful for the support provided by Abdullah Burak, Philip Empl, Constanze Hilmer, Gerhard Rauchecker, Richard Schuster, Henning Siemes, and Melih Yilmaz, who supported me substantially in searching and coding research articles.2 Impressive computational results of applying parallelization to the traveling salesman problem (TSP) are reported by Crainic et al. [2006, p.2]. arXiv:1910.03028v1 [cs.DC] 3 Oct 2019Parallel computational optimization in operations research is challenging in general from both the algorithmic and the computational perspective, and ii) a viable alternative to parallelizing algorithms has been the exploitation of ongoing increases of clock speed of single CPUs of modern microprocessors. But this growth process reached a limit already several years ago due to heat dissipation and energy consumption issues . This development makes parallelization efforts (not only in optimization) much more important than it was in earlier times.Fortunately, the need for parallelization has been acknowledged and accompanied by an increased availability of parallel computing resources. This availability is rooted in two phenomena: a) the rapid development of para...

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“…Specific meta-heuristics used for solving the TSP include simulated annealing [15], genetic algorithms [16], tabu search [17], ant colony optimization [18], iterated local search [19], particle swarm optimization [20], nested partitions [21], and neural networks [22]. There are many variants and hybrids of these metaheuristics designed to solve the TSP [23].…”

Section: Traveling Salesman Problem and Solution Techniquesmentioning

confidence: 99%

Solving large batches of traveling salesman problems with parallel and distributed computing

Ozden

Smith

Gue

2017

Computers & Operations Research

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In this paper, we describe and compare serial, parallel, and distributed solver implementations for large batches of Traveling Salesman Problems using the Lin-Kernighan Heuristic (LKH) and the Concorde exact TSP Solver. Parallel and distributed solver implementations are useful when many medium to large size TSP instances must be solved simultaneously. These implementations are found to be straightforward and highly efficient compared to serial implementations. Our results indicate that parallel computing using hyper-threading for solving 150-and 200-city TSPs can increase the overall utilization of computer resources up to 25 percent compared to single thread computing. The resulting speed-up/physical core ratios are as much as ten times better than a parallel and concurrent version of the LKH heuristic using SPC 3 in the literature. For variable TSP sizes, a longest processing time first heuristic performs better than an equal distribution rule. We illustrate our approach with an application in the design of order picking warehouses.

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Comparison of parallel metaheuristics for solving the TSP

Cited by 13 publications

References 11 publications

Optimization of construction site layout using dynamic hybrid bacterial and ant colony algorithm

Optimization of construction site layout using dynamic hybrid bacterial and ant colony algorithm

Parallel computational optimization in operations research: A new integrative framework, literature review and research directions

Solving large batches of traveling salesman problems with parallel and distributed computing

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