A tabu search based memetic algorithm for the max-mean dispersion problem

Lai, Xiangjing; Hao, Jin‐Kao

doi:10.1016/j.cor.2016.02.016

Cited by 33 publications

(37 citation statements)

References 24 publications

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“…Due to zero known results in literature for same dataset, the improved tabu search (ITS) proposed by Dai et al [54], who discussed the MS-RCPSP under step deterioration, and a path relinking algorithm (PR) [55], based on the population path relinking framework, are programmed as reference algorithms. Table 3 reports the computational results achieved by the ITS, PR, GVNS-MA, GVNS-MA ℎ , and GVNS-MA ℎ on the set of 45 benchmark instances.…”

Section: Experimental Results With Linear Deteriorationmentioning

confidence: 99%

“…Our memetic algorithm rests upon only three parameters: the population size , the depth of general variable neighborhood search , and the price for a violation to precedence constraint . For and , we follow Lai and Hao [55] and set = 10, = 50000 while the parameter is set at 1000 for the first experimental group and 1 × 10 7 for the second.…”

Section: Parameter Settings and Experiments Protocol Our Gvns-mentioning

confidence: 99%

“…Summary and comparison on the set of 45 new generated instances with = 100, , and the TS heuristic[54] and PR[55]. Best results are…”

mentioning

confidence: 99%

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A Memetic Algorithm for Multiskill Resource‐Constrained Project Scheduling Problem under Linear Deterioration

Dai

2019

Mathematical Problems in Engineering

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This paper proposes a general variable neighborhood search-based memetic algorithm (GVNS-MA) for solving the multiskill resource-constrained project scheduling problem under linear deterioration. Integrating a solution recombination operator and a local optimization procedure, the proposed GVNS-MA is assessed on two sets of instances and achieves highly competitive results. One set of benchmark instances is commonly used in the literature where the capability of the proposed algorithm to find high quality solutions is demonstrated, compared with the state-of-the-art algorithms in the literature. The other set revises the former through incorporating the linear deterioration effect. Two key components of the proposed algorithm are investigated to confirm their critical role to the success of the proposed method.

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Section: Experimental Results With Linear Deteriorationmentioning

confidence: 99%

Section: Parameter Settings and Experiments Protocol Our Gvns-mentioning

confidence: 99%

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A Memetic Algorithm for Multiskill Resource‐Constrained Project Scheduling Problem under Linear Deterioration

Dai

2019

Mathematical Problems in Engineering

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“…The meme is a cultural gene, which can be modified before being included into the next generation. Due to the trade-off between the diversification of the EA and the intensification of the LSP, the MA has proven to be both more effective and efficient than the conventional EAs in some optimization domains [31,32,33]. As a result, it has gained the wide attention and acceptance in the TSP [34,35,36].…”

Section: Memetic Algorithmmentioning

confidence: 99%

Memetic algorithm based on sequential variable neighborhood descent for the minmax multiple traveling salesman problem

Wang

Chen

Lin

2017

Computers & Industrial Engineering

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In this paper, we consider the multiple traveling salesman problem (MTSP) with the minmax objective, which includes more than one salesman to serve a set of cities while minimizing the maximum distance traveled by any salesman. For this problem, we have proposed a novel memetic algorithm, which integrates with a sequential variable neighborhood descent that is a powerful local search procedure to exhaustively search the areas near the high-quality solutions. However, there are some inefficient neighborhoods in the existing sequential variable neighborhood descent for the minmax MTSP, which could restrict the search performance. Therefore, we have redefined a new neighborhood sequence where only the neighborhoods that move cities from one tour to another unidirectionally are considered. Computational experiments on a wide range of benchmark problems within an acceptable time limit show that compared with six existing algorithms, the proposed algorithm is better than the other algorithms in terms of three aspects, including the precision, the robustness and the convergence speed. Meanwhile, we have also investigated the total distance traveled by all the salesmen when optimizing the minmax objective, and the results show that in comparison with the six existing algorithms, the proposed algorithm has a better or at least competitive capacity to maintain the total distance as short as possible. Furthermore, two kinds of statistical tests are utilized to examine the significance of the presented results, indicating the superiority of the proposed algorithm over the other algorithms on the minmax objective.

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“…The max-mean dispersion problem (Max-Mean DP) has received extensive attention in recent years [1,9]. It is proved to be strong NP-hard and has many important applications including architectural space planning, sentiment analysis, social networks, pollution control, and web pages ranks.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Metaheuristic of Integrating Estimation of Distribution Algorithm with Tabu Search for the Max‐Mean Dispersion Problem

Nijimbere

Zhao

Liu

et al. 2019

Mathematical Problems in Engineering

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This paper presents a hybrid metaheuristic that combines estimation of distribution algorithm with tabu search (EDA-TS) for solving the max-mean dispersion problem. The proposed EDA-TS algorithm essentially alternates between an EDA procedure for search diversification and a tabu search procedure for search intensification. The designed EDA procedure maintains an elite set of high quality solutions, based on which a conditional preference probability model is built for generating new diversified solutions. The tabu search procedure uses a fast 1-flip move operator for solution improvement. Experimental results on benchmark instances with variables ranging from 500 to 5000 disclose that our EDA-TS algorithm competes favorably with state-of-the-art algorithms in the literature. Additional analysis on the parameter sensitivity and the merit of the EDA procedure as well as the search balance between intensification and diversification sheds light on the effectiveness of the algorithm.

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A tabu search based memetic algorithm for the max-mean dispersion problem

Cited by 33 publications

References 24 publications

A Memetic Algorithm for Multiskill Resource‐Constrained Project Scheduling Problem under Linear Deterioration

A Memetic Algorithm for Multiskill Resource‐Constrained Project Scheduling Problem under Linear Deterioration

Memetic algorithm based on sequential variable neighborhood descent for the minmax multiple traveling salesman problem

A Hybrid Metaheuristic of Integrating Estimation of Distribution Algorithm with Tabu Search for the Max‐Mean Dispersion Problem

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