A tabu search procedure based on a random Roulette diversification for the weighted maximal planar graph problem

2006 ◽  
Vol 33 (9) ◽  
pp. 2526-2546 ◽  
Author(s):  
Ibrahim H. Osman
Keyword(s):  
Tabu Search ◽  
Planar Graph ◽  
Search Procedure ◽  
Maximal Planar Graph ◽  
Graph Problem

An algorithm and upper bounds for the weighted maximal planar graph problem

2015 ◽  
Vol 66 (8) ◽  
pp. 1399-1412 ◽  
Author(s):  
Amir Ahmadi-Javid ◽  
Amir Ardestani-Jaafari ◽  
Leslie R Foulds ◽  
Hossein Hojabri ◽  
Reza Zanjirani Farahani
Keyword(s):  
Planar Graph ◽  
Upper Bounds ◽  
Maximal Planar Graph ◽  
Graph Problem

Facility Layout Planning

2013 ◽  
pp. 212-223
Author(s):  
Hossein Hojabri ◽  
Elnaz Miandoabchi
Keyword(s):  
Mathematical Models ◽  
Planar Graph ◽  
Facility Layout ◽  
Layout Design ◽  
Simple Graph ◽  
Layout Planning ◽  
Adjacency Graph ◽  
Manufacturing Plants ◽  
Maximal Planar Graph ◽  
Facilities Layout

The weighted maximal planar graph (WMPG) appears in many applications. It is currently used to design facilities layout in manufacturing plants. Given an edge-weighted complete simple graph G, the WMPG involves finding a sub-graph of G that is planar in the sense that it could be embedded on the plane such that none of its edges intersect, and is maximal in the sense that no more edges can be added to it unless its planarity is violated. Finally, it is optimal in the sense that the resulting maximal planar graph holds the maximum sum of edge weights. In this chapter, the aim is to explain the application of planarity in facility layout design. The mathematical models and the algorithms developed for the problem so far are explained. In the meanwhile, the corollaries and theorems needed to explain the algorithms and models are briefly given. In the last part, an explanation on how to draw block layout from the adjacency graph is given.

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

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Dieudonné Nijimbere ◽  
Songzheng Zhao ◽  
Haichao Liu ◽  
Bo Peng ◽  
Aijun Zhang
Keyword(s):  
Tabu Search ◽  
Probability Model ◽  
Estimation Of Distribution Algorithm ◽  
Search Procedure ◽  
Hybrid Metaheuristic ◽  
Benchmark Instances ◽  
Conditional Preference ◽  
Estimation Of Distribution ◽  
Dispersion Problem ◽  
Move Operator

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.

Improving the structure of deep frozen and chilled food chain with tabu search procedure

2003 ◽  
Vol 60 (1) ◽  
pp. 67-79 ◽  
Author(s):  
Guimei Zhang ◽  
Walter Habenicht ◽  
Walter Ernst Ludwig Spieß
Keyword(s):  
Tabu Search ◽  
Food Chain ◽  
Search Procedure

A Filter-and-Fan Metaheuristic for the 0-1 Multidimensional Knapsack Problem

2012 ◽  
Vol 3 (4) ◽  
pp. 43-63 ◽  
Author(s):  
Mahdi Khemakhem ◽  
Boukthir Haddar ◽  
Khalil Chebil ◽  
Saïd Hanafi
Keyword(s):  
Tabu Search ◽  
Knapsack Problem ◽  
Search Algorithm ◽  
Neighborhood Search ◽  
Benchmark Problems ◽  
Tree Search ◽  
Search Procedure ◽  
Multidimensional Knapsack Problem ◽  
Multidimensional Knapsack ◽  
Adaptive Neighborhood

This paper proposes a new hybrid tree search algorithm to the Multidimensional Knapsack Problem (MKP) that effectively combines tabu search with a dynamic and adaptive neighborhood search procedure. The authors’ heuristic, based on a filter-and-fan (F&F) procedure, uses a Linear Programming-based Heuristic to generate a starting solution to the F&F process. A tabu search procedure is used to try to enhance the best solution value provided by the F&F method that generates compound moves by a strategically truncated form of tree search. They report the first application of the F&F method to the MKP. Experimental results obtained on a wide set of benchmark problems clearly demonstrate the competitiveness of the proposed method compared to the state-of-the-art heuristic methods.

scholarly journals The maximum Wiener index of maximal planar graphs

2020 ◽  
Vol 40 (4) ◽  
pp. 1121-1135
Author(s):  
Debarun Ghosh ◽  
Ervin Győri ◽  
Addisu Paulos ◽  
Nika Salia ◽  
Oscar Zamora
Keyword(s):  
Planar Graph ◽  
Connected Graph ◽  
Planar Graphs ◽  
Wiener Index ◽  
Maximal Planar Graph

Abstract The Wiener index of a connected graph is the sum of the distances between all pairs of vertices in the graph. It was conjectured that the Wiener index of an n-vertex maximal planar graph is at most $$\lfloor \frac{1}{18}(n^3+3n^2)\rfloor $$ ⌊ 1 18 ( n 3 + 3 n 2 ) ⌋ . We prove this conjecture and determine the unique n-vertex maximal planar graph attaining this maximum, for every $$ n\ge 10$$ n ≥ 10 .

scholarly journals Financial applications of a Tabu search variable selection model

2001 ◽  
Vol 5 (4) ◽  
pp. 215-234 ◽  
Author(s):  
Zvi Drezner ◽  
George A. Marcoulides ◽  
Mark Hoven Stohs
Keyword(s):  
Tabu Search ◽  
Variable Selection ◽  
Credit Scoring ◽  
Bankruptcy Prediction ◽  
Selection Model ◽  
Search Procedure ◽  
Corporate Bankruptcy ◽  
Explanatory Variables ◽  
Selection Of Variables ◽  
Search Variable

We illustrate how a comparatively new technique, a Tabu search variable selection model [Drezner, Marcoulides and Salhi (1999)], can be applied efficiently within finance when the researcher must select a subset of variables from among the whole set of explanatory variables under consideration. Several types of problems in finance, including corporate and personal bankruptcy prediction, mortgage and credit scoring, and the selection of variables for the Arbitrage Pricing Model, require the researcher to select a subset of variables from a larger set. In order to demonstrate the usefulness of the Tabu search variable selection model, we: (1) illustrate its efficiency in comparison to the main alternative search procedures, such as stepwise regression and the Maximum R2 procedure, and (2) show how a version of the Tabu search procedure may be implemented when attempting to predict corporate bankruptcy. We accomplish (2) by indicating that a Tabu Search procedure increases the predictability of corporate bankruptcy by up to 10 percentage points in comparison to Altman's (1968) Z-Score model.

scholarly journals A 1-tough nonhamiltonian maximal planar graph

1980 ◽  
Vol 30 (3) ◽  
pp. 305-307 ◽  
Author(s):  
Takao Nishizeki
Keyword(s):  
Planar Graph ◽  
Maximal Planar Graph
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