A Hybrid Lagrangian Relaxation and Tabu Search Method for Interdependent-Choice Network Design Problems

Hybridization offers a promising approach in designing and developing improved metaheuristic methods for a variety of complex combinatorial optimization problems. This chapter presents a hybrid Lagrangian relaxation and tabu search method for a class of discrete network design problems with complex interdependent-choice constraints. This method takes advantage of Lagrangian relaxation for problem decomposition and complexity reduction while its algorithmic logic is designed based on the principles of tabu search. The algorithmic advance and solution performance of the method are illustrated by implementing it for solving a network design problem with lane reversal and crossing elimination strategies, arising from urban evacuation planning.

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Optimization of Steel Plate Cutting Sequence Using the Tabu Search Method

Journal of Ship Production ◽

10.5957/jsp.2005.21.2.134 ◽

2005 ◽

Vol 21 (02) ◽

pp. 134-139

Author(s):

Yasuhisa Okumoto ◽

Ryutaro Iseki

Keyword(s):

Tabu Search ◽

Field Data ◽

Steel Plate ◽

Optimization Problems ◽

Idle Time ◽

Search Method ◽

Combinatorial Problems ◽

Steel Plates ◽

Cutting Sequence ◽

Tabu Search Method

This study concerns the cutting scheduling of steel plates by numerically controlled (NC) machines in shipyards. The combinatorial optimization of the cutting sequence to minimize the idle time of machines has been examined for processing many plates by multiple cutting machines. As an algorithm of the optimization, the tabu search method, which is a kind of metaheuristic technique and has been widely applied recently for such optimization problems, was adopted. The procedure was programmed on a personal computer, and calculations were carried out for test data as well as field data. It was confirmed that the idle time to wait for the next process decreased remarkably compared with the initial planned sequence, and the tabu search method was a simple and effective method to solve combinatorial problems.

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Multi-Objective Evolutionary Algorithms for Sensor Network Design

Multi-Objective Optimization in Computational Intelligence ◽

10.4018/978-1-59904-498-9.ch008 ◽

2011 ◽

pp. 208-238 ◽

Cited By ~ 3

Author(s):

Ramesh Rajagopalan ◽

Chilukuri K. Mohan ◽

Kishan G. Mehrotra ◽

Pramod K. Varshney

Keyword(s):

Evolutionary Algorithms ◽

Network Design ◽

Sensor Network ◽

Optimization Problems ◽

Multiple Objectives ◽

Design Problems ◽

Multi Objective ◽

Natural Approach ◽

Sensor Network Design ◽

Network Design Problems

Many sensor network design problems are characterized by the need to optimize multiple conflicting objectives. However, existing approaches generally focus on a single objective (ignoring the others), or combine multiple objectives into a single function to be optimized, to facilitate the application of classical optimization algorithms. This restricts their ability and constrains their usefulness to the network designer. A much more appropriate and natural approach is to address multiple objectives simultaneously, applying recently developed multi-objective evolutionary algorithms (MOEAs) in solving sensor network design problems. This chapter describes and illustrates this approach by modeling two sensor network design problems (mobile agent routing and sensor placement), as multi-objective optimization problems, developing the appropriate objective functions and discussing the tradeoffs between them. Simulation results using two recently developed MOEAs, viz., EMOCA (Rajagopalan, Mohan, Mehrotra, & Varshney, 2006) and NSGA-II (Deb, Pratap, Agarwal, & Meyarivan, 2000), show that these MOEAs successfully discover multiple solutions characterizing the tradeoffs between the objectives.

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A GENETIC ALGORITHM FOR THE DESIGN OF MINIMUM-COST TWO-CONNECTED NETWORKS WITH BOUNDED RINGS

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026805001611 ◽

2005 ◽

Vol 05 (02) ◽

pp. 267-281 ◽

Cited By ~ 2

Author(s):

MARIO VENTRESCA ◽

BEATRICE M. OMBUKI

Keyword(s):

Genetic Algorithm ◽

Tabu Search ◽

Network Design ◽

Branch And Bound ◽

Minimum Cost ◽

Numerical Results ◽

Solution Quality ◽

Design Problems ◽

Network Design Problems

This paper presents a genetic algorithm for designing minimum-cost two-connected networks such that the shortest cycle to which each edge belongs to does not exceed a given length. We provide numerical results based on randomly generated graphs found in the literature and compare the solution quality with that of tabu search and branch and bound. The results demonstrate the effectiveness of our algorithm and show promise for tackling ring-based network design problems. This paper is among the first to document the implementation of a genetic algorithm for the design of two-connected networks with the added constraint of bounded rings.

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