Harmony Search for Generalized Orienteering Problem: Best Touring in China

Geem, Zong Woo; Tseng, Chung‐Li; Park, Yong‐Jin

doi:10.1007/11539902_91

Cited by 166 publications

(74 citation statements)

References 12 publications

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“…HS has been very successful in a wide variety of optimization problems [17,18,19,32], presenting several advantages over traditional optimization techniques such as: (a) HS algorithm imposes fewer mathematical requirements and does not require initial value settings for decision variables, (b) as the HS algorithm uses stochastic random searches, derivative information is also unnecessary, and (c) the HS algorithm generates a new vector, after considering all of the existing vectors, whereas methods such as GA only consider the two parent vectors. These three features increase the flexibility of the HS algorithm.…”

Section: Introductionmentioning

confidence: 99%

Efficient stochastic algorithms for document clustering

Forsati

Mahdavi

Shamsfard

et al. 2013

Information Sciences

111

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Clustering has become an increasingly important and highly complicated research area for targeting useful and relevant information in modern application domains such as the World Wide Web. Recent studies have shown that the most commonly used partitioning-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm may generate a local optimal clustering. In this paper, we present novel document clustering algorithms based on the Harmony Search (HS) optimization method. By modeling clustering as an optimization problem, we first propose a pure HS based clustering algorithm that finds near-optimal clusters within a reasonable time. Then, harmony clustering is integrated with the K-means algorithm in three ways to achieve better clustering by combining the explorative power of HS with the refining power of the K-means. Contrary to the localized searching property of K-means algorithm, the proposed algorithms perform a globalized search in the entire solution space. Additionally, the proposed algorithms improve K-means by making it less dependent on the initial parameters such as randomly chosen initial cluster centers, therefore, making it more stable. The behavior of the proposed algorithm is theoretically analyzed by modeling its population variance as a Markov chain. We also conduct an empirical study to determine the impacts of various parameters on the quality of clusters and convergence behavior of the algorithms. In the experiments, we apply the proposed algorithms along with K-means and a Genetic Algorithm (GA) based clustering algorithm on five different document datasets. Experimental results reveal that the proposed algorithms can find better clusters and the quality of clusters is comparable based on F-measure, Entropy, Purity, and Average Distance of Documents to the Cluster Centroid (ADDC).

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Section: Introductionmentioning

confidence: 99%

Efficient stochastic algorithms for document clustering

Forsati

Mahdavi

Shamsfard

et al. 2013

Information Sciences

111

View full text Add to dashboard Cite

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“…Let us start by briefly sketching the fundamentals of Harmony Search, which was first coined by Zong et al in [27] and thereafter applied to a wide number of applications and problems, such as: the Combined Heat and Power Economic Dispatch problem (CHPED) [28], the Traveling Salesperson Problem (TSP) [29], tour routing [30], Sudoku puzzle solving [31], distribution of 24h energency units [32] and Grouping problems [33], [34], among others. This paper elaborates further on the multi-objective view of the problem and presents a two-objective Harmony Search algorithm that attempts at simultaneously minimizing two (possibly conflicting) fitness functions: Investment Cost (IC) and Global Warming Potential (GWP).…”

Section: Proposed Multi-objective Harmony Search Algorithmmentioning

confidence: 99%

A Multi-objective Harmony Search Algorithm for Optimal Energy and Environmental Refurbishment at District Level Scale

Manjarres

Mabe

Oregi

et al. 2017

Advances in Intelligent Systems and Computing

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Nowadays municipalities are facing an increasing commitment regarding the energy and environmental performance of cities and districts. The multiple factors that characterize a district scenario, such as: refurbishment strategies' selection, combination of passive, active and control measures, the surface to be refurbished and the generation systems to be substituted will highly influence the final impacts of the refurbishment solution. In order to answer this increasing demand and consider all above-mentioned district factors, municipalities need optimisation methods supporting the decision making process at district level scale when defining cost-effective refurbishment scenarios. Furthermore, the optimisation process should enable the evaluation of feasible solutions at district scale taking into account that each district and building has specific boundaries and barriers. Considering these needs, this paper presents a multi-objective approach allowing a simultaneous environmental and economic assessment of refurbishment scenarios at district scale. With the aim at demonstrating the effectiveness of the proposed approach, a real scenario of Gros district in the city of Donostia-San Sebastian (North of Spain) is presented. After analysing the baseline scenario in terms of energy performance, environmental and economic impacts, the multi-objective Harmony Search algorithm has been employed to assess the goal of reducing the environmental impacts in terms of Global Warming Potential (GWP) and minimizing the investment cost obtaining the best ranking of economic and environmental refurbishment scenarios for the Gros district.

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“…Thus, because of the search-space size of the problem. Adopting the idea that existing evolutionary or meta-heuristic algorithms are found in the paradigm of natural processes, a new algorithm can be conceptualized from a musical performance process (say, a jazz trio) in [21,22] involving searching for a better harmony. Musical performance seeks a best state (fantastic harmony) determined by aesthetic estimation, as the optimization process seeks a best state (global optimum: minimum cost; minimum error; maximum benefit; or maximum efficiency) determined by objective function evaluation.…”

Section: The Harmony Search Approachmentioning

confidence: 99%

Efficient harmony search optimization for preventive-maintenance-planning for nuclear power systems

Rami

Hamdaoui

Sayah

et al. 2014

Int. J. Simul. Multidisci. Des. Optim.

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-This paper combines the universal generating function UGF with harmony search (HSO) meta-heuristic optimization method to solve a preventive maintenance (PM) problem for series-parallel system. In this work, we consider the situation where system and its components have several ranges of performance levels. Such systems are called multi-state systems (MSS). To enhance system availability or (reliability), possible schedule preventive maintenance actions are performed to equipments and affect strongly the effective age. The MSS measure is related to the ability of the system to satisfy the demand. The objective is to develop an algorithm to generate an optimal sequence of maintenance actions providing system working with the desired level of availability or (reliability) during its lifetime with minimal maintenance cost rate. To evaluate the MSS system availability, a fast method based on UGF is suggested. The harmony search approach can be applied as an optimization technique and adapted to this PM optimization problem.

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Harmony Search for Generalized Orienteering Problem: Best Touring in China

Cited by 166 publications

References 12 publications

Efficient stochastic algorithms for document clustering

Efficient stochastic algorithms for document clustering

A Multi-objective Harmony Search Algorithm for Optimal Energy and Environmental Refurbishment at District Level Scale

Efficient harmony search optimization for preventive-maintenance-planning for nuclear power systems

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