Population-Based Incremental Learning Algorithm for a Serial Colored Traveling Salesman Problem

2018 ◽  
Vol 48 (2) ◽  
pp. 277-288 ◽  
Author(s):  
Xianghu Meng ◽  
Jun Li ◽  
MengChu Zhou ◽  
Xianzhong Dai ◽  
Jianping Dou
Keyword(s):  
Traveling Salesman Problem ◽  
Incremental Learning ◽  
Learning Algorithm ◽  
Population Based ◽  
Traveling Salesman

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

High-Order Entropy-Based Population Diversity Measures in the Traveling Salesman Problem

2020 ◽  
Vol 28 (4) ◽  
pp. 595-619
Author(s):  
Yuichi Nagata
Keyword(s):  
Markov Model ◽  
Traveling Salesman Problem ◽  
Population Based ◽  
Population Diversity ◽  
High Order ◽  
Traveling Salesman ◽  
Variable Order ◽  
Diversity Measures ◽  
The Traveling Salesman Problem ◽  
Permutation Problems

To maintain the population diversity of genetic algorithms (GAs), we are required to employ an appropriate population diversity measure. However, commonly used population diversity measures designed for permutation problems do not consider the dependencies between the variables of the individuals in the population. We propose three types of population diversity measures that address high-order dependencies between the variables to investigate the effectiveness of considering high-order dependencies. The first is formulated as the entropy of the probability distribution of individuals estimated from the population based on an [Formula: see text]-th--order Markov model. The second is an extension of the first. The third is similar to the first, but it is based on a variable order Markov model. The proposed population diversity measures are incorporated into the evaluation function of a GA for the traveling salesman problem to maintain population diversity. Experimental results demonstrate the effectiveness of the three types of high-order entropy-based population diversity measures against the commonly used population diversity measures.

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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