A HYBRID EVOLUTIONARY ALGORITHM FOR MULTIPLE-DESTINATIONS ROUTING PROBLEM

2004 ◽  
Vol 04 (04) ◽  
pp. 337-353 ◽  
Author(s):  
SALAH AL-SHARHAN ◽  
FAWAZ AL-ANZI
Keyword(s):  
Evolutionary Algorithm ◽  
Incremental Learning ◽  
Heuristic Algorithms ◽  
Learning Algorithm ◽  
Population Based ◽  
Multicast Tree ◽  
Routing Problem ◽  
Hybrid Evolutionary Algorithm ◽  
Np Complete ◽  
Qos Metrics

This paper presents a hybrid evolutionary algorithm for constrained multiple destinations routing problem. The problem can be formulated as minimising tree cost under several constraints or QoS metrics. Computing such constrained multicast tree has been proven to be NP-complete. The proposed hybrid algorithm is based on a population based incremental learning algorithm and a constrained distance network heuristic (or CKMB) algorithm. In the proposed algorithm, CKMB is utilised as a decoding scheme. Experimental results show that, in most cases, the proposed algorithm yields better solutions than other heuristic algorithms proposed in the literature including the best known one BSMA.

A FAST HYBRID ALGORITHM FOR MULTICAST ROUTING IN WIRELESS NETWORKS

2007 ◽  
Vol 16 (01) ◽  
pp. 45-68 ◽  
Author(s):  
S. AL-SHARHAN ◽  
W. GUEAIEB
Keyword(s):  
Wireless Networks ◽  
Hybrid Algorithm ◽  
Heuristic Algorithms ◽  
Multicast Routing ◽  
Learning Algorithm ◽  
Population Based ◽  
Multicast Tree ◽  
Recent Developments ◽  
Real Challenge

This paper tackles the issue of constrained multicast routing in wireless networks using a hybrid soft computing-based algorithm. Recent developments in multimedia applications and the dynamic and rapidly changed environment of the wireless networks make the constrained multicast routing a real challenge. The problem can be formulated as minimizing a multicast tree cost under several constraints or Quality of Service (QoS) metrics. This problem has been proven to be NP-complete. The proposed hybrid algorithm is based on a population based incremental learning algorithm that combines in an efficient way the features of genetic algorithms and competitive learning. Experimental results show that, in most cases, the proposed algorithm yields better solutions than other heuristic algorithms proposed in the literature.

scholarly journals On the restrictive channel thickness estimation

2007 ◽  
Vol 20 (3) ◽  
pp. 499-506
Author(s):  
Iskandar Karapetyan
Keyword(s):  
Positive Integer ◽  
Heuristic Algorithms ◽  
Clique Number ◽  
Directed Path ◽  
Channel Routing ◽  
Routing Problem ◽  
Thickness Estimation ◽  
Channel Thickness ◽  
Np Complete ◽  
Routing Method

Channel routing is an important phase of physical design of LSI and VLSI chips. The channel routing method was first proposed by Akihiro Hashimoto and James Stevens [1]. The method was extensively studied by many authors and applied to different technologies. At present there are known many effective heuristic algorithms for channel routing. A. LaPaugh [2] proved that the restrictive routing problem is NP-complete. In this paper we prove that for every positive integer k there is a restrictive channel C for which ?(C)>? (HG)+L(VG)+k, where ? (C) is the thickness of the channel, ?(HG) is clique number of the horizontal constraints graph HG and L(VG) is the length of the longest directed path in the vertical constraints graph VG.

A diversity maintaining population-based incremental learning algorithm

2008 ◽  
Vol 178 (21) ◽  
pp. 4038-4056 ◽  
Author(s):  
Mario Ventresca ◽  
Hamid R. Tizhoosh
Keyword(s):  
Incremental Learning ◽  
Learning Algorithm ◽  
Population Based

A Discrete Artificial Bee Colony Algorithm for the Reverse Logistics Location and Routing Problem

2017 ◽  
Vol 16 (05) ◽  
pp. 1339-1357 ◽  
Author(s):  
Kun Guo ◽  
Qishan Zhang
Keyword(s):  
Reverse Logistics ◽  
Vehicle Routing Problem ◽  
Artificial Bee Colony ◽  
Heuristic Algorithms ◽  
Routing Problem ◽  
Waste Products ◽  
Location Allocation ◽  
Bee Colony ◽  
Np Complete ◽  
Location Allocation Problem

Reverse logistics (RL) emerges as a hot topic in both research and business with the increasing attention on the collection and recycling of the waste products. Since Location and Routing Problem (LRP) in RL is NP-complete, heuristic algorithms, especially those built upon swarm intelligence, are very popular in this research. In this paper, both Vehicle Routing Problem (RP) and Location Allocation Problem (LAP) of RL are considered as a whole. First, the features of LRP in RL are analyzed. Second, a mathematical model of the problem is developed. Then, a novel discrete artificial bee colony (ABC) algorithm with greedy adjustment is proposed. The experimental results show that the new algorithm can approach the optimal solutions efficiently and effectively.

A Hybrid Population-Based Incremental Learning Algorithm for M-Machine Reentrant Permutation Flow-Shop Scheduling

2013 ◽  
Vol 655-657 ◽  
pp. 1636-1641
Author(s):  
Zuo Cheng Li ◽  
Bin Qian ◽  
Rong Hu ◽  
Xiao Hong Zhu
Keyword(s):  
Incremental Learning ◽  
Flow Shop ◽  
Learning Algorithm ◽  
Population Based ◽  
Flow Shop Scheduling ◽  
Hybrid Population ◽  
Neighborhood Search ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

In this paper, a hybrid population-based incremental learning algorithm (HPBIL) is proposed for solving the m-machine reentrant permutation flow-shop scheduling problem (MRPFSSP). The objective function is to minimize the maximum completion time (i.e., makespan). In HPBIL, the PBIL with a proposed Insert-based mutation is used to perform global exploration, and an Interchange-based neighborhood search with first move strategy is designed to enhance the local exploitation ability. Computational experiments and comparisons demonstrate the effectiveness of the proposed HPBIL.

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