Less is more: Basic variable neighborhood search for minimum differential dispersion problem

Mladenović, Nenad; Todosijević, Raca; Urošević, Dragan

doi:10.1016/j.ins.2015.07.044

Cited by 86 publications

(42 citation statements)

References 26 publications

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“…In this paper, we follow the recent heuristic approach, that is, less is more approach (LIMA), to solve optimization problems (Mladenović et al., ; Brimberg et al., ). LIMA's main idea is to find the minimum number of search ingredients when solving a particular optimization problem, which would make some heuristic more efficient than the currently best in the literature.…”

Section: Introductionmentioning

confidence: 99%

Less is more approach: basic variable neighborhood search for the obnoxious p‐median problem

Mladenović

Alkandari

Pei

et al. 2019

Int Trans Operational Res

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The goal of the less is more approach (LIMA) for solving optimization problems that has recently been proposed in Mladenović et al. (2016) is to find the minimum number of search ingredients that make a heuristic more efficient than the currently best. In this paper, LIMA is successfully applied to solve the obnoxious p‐median problem (OpMP). More precisely, we developed a basic variable neighborhood search for solving the OpMP, where the single search ingredient, the interchange neighborhood structure, is used. We also propose a new simple local search strategy for solving facility location problems, within the interchange neighborhood structure, which is in between the usual ones: first improvement and best improvement strategies. We call it facility best improvement local search. On the basis of experiments, it appeared to be more efficient and effective than both first and best improvement. According to the results obtained on the benchmark instances, our heuristic turns out to be highly competitive with the existing ones, establishing new state‐of‐the‐art results. For example, four new best‐known solutions and 133 ties are claimed in testing the set with 144 instances.

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Section: Introductionmentioning

confidence: 99%

Less is more approach: basic variable neighborhood search for the obnoxious p‐median problem

Mladenović

Alkandari

Pei

et al. 2019

Int Trans Operational Res

Self Cite

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“…Without using any compiler flag, running the well-known DIMACS machine benchmark procedure dfmax.c 2 on our machine requires respectively 0.44, 2.63 and 9.85 seconds to solve the benchmark graphs r300.5, r400.5 and r500.5. Given its stochastic nature, ILS MinDiff was independently executed, like [13,30], forty times with different random seeds on each test instance. Each run stops if the running time reaches the cut-off time limit (t max ).…”

Section: Experimental Settingsmentioning

confidence: 99%

“…Each run stops if the running time reaches the cut-off time limit (t max ). Following the literature [13,30], we set the time limit t max to n, where n is the number of elements in the considered test instance. To run the ILS MinDiff algorithm, there are three parameters to be determined, including search depth nbr max , weak perturbation strength p w in the local optima exploring phase and strong perturbation coefficient α in the local optima escaping phase.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…For medium and large instances, heuristic and meta-heuristic algorithms are often preferred to solve the problem approximately. In recent years, several heuristic approaches have been proposed in the literature [32,13,30]. In particular, in 2015, based on greedy randomized adaptive search procedure (GRASP), variable neighborhood search (VNS) and exterior path relinking (EPR), Duarte et al proposed several effective hybrid heuristics [13].…”

Section: Introductionmentioning

confidence: 99%

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An iterated local search algorithm for the minimum differential dispersion problem

Zhou

Hao

2017

Knowledge-Based Systems

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Given a set of n elements separated by a pairwise distance matrix, the minimum differential dispersion problem (Min-Diff DP) aims to identify a subset of m elements (m < n) such that the difference between the maximum sum and the minimum sum of the inter-element distances between any two chosen elements is minimized. We propose an effective iterated local search (denoted by ILS MinDiff) for Min-Diff DP. To ensure an effective exploration and exploitation of the search space, the proposed ILS MinDiff algorithm iterates through three sequential search phases: a fast descent-based neighborhood search phase to find a local optimum from a given starting solution, a local optima exploring phase to visit nearby high-quality solutions around a given local optimum, and a local optima escaping phase to move away from the current search region. Experimental results on six data sets of 190 benchmark instances demonstrate that ILS MinDiff competes favorably with the state-of-the-art algorithms by finding 130 improved best results (new upper bounds).

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“…Variable Neighborhood Search (VNS) is a metaheuristic for solving combinatorial and global optimization problems, proposed by Mladenovic and Hansen [21,22]. The main idea of this framework is the systematic neighborhood change in order to achieve an optimal (or a close-to-optimal) solution [23]. VNS and its extensions have proven their efficiency in solving many combinatorial and global optimization problems [24].…”

Section: Variable Neighborhood Search (Vns)mentioning

confidence: 99%

Quantum Inspired General Variable Neighborhood Search (qGVNS) for Dynamic Garbage Collection

Papalitsas¹,

Karakostas²,

Andronikos³

et al. 2017

Preprint

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General Variable Neighborhood Search (GVNS) is a well known and widely used metaheuristic for efficiently solving many NP-hard combinatorial optimization problems. Quantum General Variable Neighborhood Search (qGVNS) is a novel, quantum inspired extension of the conventional GVNS. Its quantum nature derives from the fact that it takes advantage and incorporates tools and techniques from the field of quantum computation. Travelling Salesman Problem (TSP) is a well known NP-Hard problem which has broadly been used for modelling many real life routing cases. As a consequence, TSP can be used as a basis for modelling and finding routes for Geographical Systems (GPS). In this paper, we examine the potential use of this method for the GPS system of garbage trucks. Specifically, we provide a thorough presentation of our method accompanied with extensive computational results. The experimental data accumulated on a plethora of symmetric TSP instances (symmetric in order to faithfully simulate GPS problems), which are shown in a series of figures and tables, allow us to conclude that the novel qGVNS algorithm can provide an efficient solution for this type of geographical problems.

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Less is more: Basic variable neighborhood search for minimum differential dispersion problem

Cited by 86 publications

References 26 publications

Less is more approach: basic variable neighborhood search for the obnoxious p‐median problem

Less is more approach: basic variable neighborhood search for the obnoxious p‐median problem

An iterated local search algorithm for the minimum differential dispersion problem

Quantum Inspired General Variable Neighborhood Search (qGVNS) for Dynamic Garbage Collection

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