Parallel Optimization Algorithm for Competitive Facility Location

Lančinskas, Algirdas; Ortigosa, Pilar M.; Žilinskas, Julius

doi:10.3846/13926292.2015.1088903

Cited by 8 publications

(4 citation statements)

References 23 publications

(25 reference statements)

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“…Problem-specific heuristics, other heuristics, matheuristics, and multi-search algorithms: Problem-specific heuristics have been parallelized for a variety of optimization problems, including a graph theory problem [Dobrian et al, 2011], TSPs [Ozden et al, 2017, Ismail et al, 2011, a FSSP [Bożejko, 2009], a facility location problem [Lancinskas et al, 2015], a mixed integer linear program [Koc and Mehrotra, 2017], and several other problems [Redondo et al, 2016, Hemmelmayr, 2015, Benedicic et al, 2014, Gomes et al, 2008, Baumelt et al, 2016, Luo et al, 2015. We found four studies which parallelize heuristics that differ from all types described above: an agent-based heuristic [Benedicic et al, 2014], an auction-based heuristic [Sathe et al, 2012], a Monte Carlo simulation inside a heuristic-randomization process [Juan et al, 2013], and a random search algorithm [Sancı and İşler, 2011].…”

Section: Population-based Metaheuristicsmentioning

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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Section: Population-based Metaheuristicsmentioning

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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“…2 pav.). Po kiekvienu vaizdu pateikiama santykinės paklaidos reikšmė [11,12]. Čia atvaizduoti ligandai, pritaikius genetinį algoritmą daugiamatėms skalėms.…”

Section: Eksperimentinis Tyrimasunclassified

Evoliucinis požiūris optimizavimo procese

Dzidolikaitė

2016

JMD

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Vilniaus universiteto Matematikos ir informatikos institutas ĮvadasĮvairiose žmogaus veiklos srityse nuolat susiduriama su daugiamačiais duomenimis. Tikriausiai nerastume tokios veiklos, kurioje apsieinama be daugiamačių duomenų. Vykstant techniniam progresui, tokių duomenų kiekis labai greitai didėja [1].Medicinoje, technikoje ir kitose srityse tenka analizuoti daugiamačius duomenis. Šių duomenų apimtys vis didėja. Jiems nagrinėti ir kaupti pasitelkiama naujausia techninė ir programinė įranga.Kaip jau minėta, duomenų apimtys auga didžiuliu greičiu, todėl tenka spręsti tam tikrus uždavinius, t. y. kaip tuos duomenis suprasti, komentuoti ir gauti reikiamą informaciją atsisakant nereikšmingų faktų. Dažniausiai kyla poreikis nuosekliau suvokti tokių duomenų sandarą: panašių objektų grupes (klasterius) ir ryškiai išsiskiriančius objektus (taškus atsiskyrėlius), objektų panašumą arba skirtingumą. Daugiamačius duomenis sunku suprasti, nes jie apibrėžia sudėtingą reiškinį ar objektą, turintį daug parametrų (rodiklių, ypatybių). Šie parametrai būna ne tik skaitiniai, bet ir loginiai, tekstiniai ar kitokie [2].Kuo didesnės dimensijos duomenys yra, tuo sunkiau juos suvokti. Daugiamačiai duomenys pateikiami tokia forma, kad tyrėjui būtų lengviau suprasti duomenų sandarą, susidariusias grupes, tarpusavio ryšius ir pan. Toks duomenų pateikimo būdas galėtų būti vizualizavimas. Vizualizavimas -tai grafinis daugiamačių duomenų vaizdavimas. Grafinius duomenis žmogui lengviau suvokti. Daugiamačių duomenų vizualizavimas leidžia tirti ir atvaizduoti informaciją žmogui suprantamesne forma. Tyrėjas pats gali matyti duomenų grupavimosi tendencijas, nustatyti daugiamatės erdvės taškų tarpusavio artimumą ir priimti atitinkamus sprendimus.

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“…To address the shortcomings of the PPE algorithm, we attempt to use parallel method to improve [21,22]. Predecessors have done a lot of work in parallel optimization algorithm research over the years, such as parallel particle swarm optimization (PPSO) [23], parallel genetic algorithm (PGA) [24], parallel ant colony algorithm (PACO) [25][26][27], and parallel differential evolution algorithm (PDE) [28].…”

Section: Introductionmentioning

confidence: 99%

Mutigroup-Based Phasmatodea Population Evolution Algorithm with Mutistrategy for IoT Electric Bus Scheduling

Zhu

Yan

Pan

et al. 2022

Wireless Communications and Mobile Computing

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The Phasmatodea population evolution algorithm (PPE) is a novel metaheuristic algorithm proposed in recent years, which simulates the evolutionary trend of stick insect population. In this article, a multigroup-based Phasmatodea population evolution algorithm with mutistrategy (MPPE) is proposed to further improve the overall performance of PPE. During the initialization period, the stick insect population is divided into multiple groups, and the step factor of the flower pollen algorithm is introduced into the population growth model of no more than half of the groups. This makes the population evolution trend diversified and prevents the algorithm from falling into the local optimal solution to a certain extent. In terms of intergroup communication, two communication strategies are adopted to mutate and replace the inferior particles, respectively, which improves the convergence speed and search ability of the algorithm. In the MPPE performance test, we compared it with PPE, five standard algorithms, and other parallel algorithms in CEC 2013 Benchmark Suite. Finally, this algorithm is applied to the IoT based electric bus scheduling for urban waterlogging situation, and the excellent performance of MPPE is verified comprehensively.

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Parallel Optimization Algorithm for Competitive Facility Location

Cited by 8 publications

References 23 publications

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

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

Evoliucinis požiūris optimizavimo procese

Mutigroup-Based Phasmatodea Population Evolution Algorithm with Mutistrategy for IoT Electric Bus Scheduling

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