An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

The Self-Organizing Map (SOM) algorithm has attracted a great deal of interest among researches and practitioners in a wide variety of fields. The SOM has been analyzed extensively, a number of variants have been developed and, perhaps most notably, it has been applied extensively within fields ranging from engineering sciences to medicine, biology, and economics. We have collected a comprehensive list of 5384 scientific papers that use the algorithms, have benefited from them, or contain analyses of them. The list is intended to serve as a source for literature surveys. The present addendum contains 2092 new articles, mainly from the years 1998-2002. We have provided a keyword index to help finding articles of interest, and additionally a modern automatically constructed variant of a thematic index: a WEBSOM interface to the whole article collection of years 1981-2000. The SOM of SOMs is available at http://websom.hut.fi/websom/somref/search.cgi for browsing and searching the collection.

Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum

In this paper, we present a new method, called the genetic simulated annealing ant colony system with particle swarm optimization techniques, for solving the traveling salesman problem. We also make experiments using the 25 data sets obtained from the TSPLIB (http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/) and compare the experimental results of the proposed method with the methods of Angeniol, Vaubois, and Texier (1988), Somhom, Modares, and Enkawa (1997), Masutti and Castro (2009) and Pasti and Castro (2006). The experimental results show that both the average solution and the percentage deviation of the average solution to the best known solution of the proposed method are better than the methods of Angeniol et al. (1988), Somhom et al. (1997), Masutti and Castro (2009) and Pasti and Castro (2006).

Solving the traveling salesman problem based on the genetic simulated annealing ant colony system with particle swarm optimization techniques

A self-organizing neural network using ideas from the immune system to solve the traveling salesman problem

We consider a noise-limited wireless sensor network that consists of battery-operated nodes which can route information to a mobile sink in a multi-hop fashion. The problem of maximizing the network's lifetime, defined as the period of time during which the network can route a feasible flow to each sink location subject to power/energy constraints, is cast into a linear program, reduced into a simpler equivalent form and solved via dual decomposition. The unknowns are the sink sojourn times and the routing flow vector for each sink location. The presence of a mobile sink presents new challenges but the problem structure can still be exploited to find the optimal solution. A distributed algorithm based on the subgradient method and using the sink as leader is proposed and its performance is evaluated through simulation for random networks. The algorithm's requirements in memory are also provided.

http://ieeexplore.ieee.org/iel5/5757977/5757978/05758168.pdf

A Distributed Algorithm for Maximum Lifetime Routing in Sensor Networks with Mobile Sink

The Kohonen network incorporating explicit statistics and its application to the travelling salesman problem

In this work we are concerned with the Clarke–Wright savings method for the classical capacitated vehicle routing problem. This is an NP-hard problem and numerous heuristic solution methods have be...

https://link.springer.com/content/pdf/10.1057%2Fpalgrave.jors.2601916.pdf

A new enhancement of the Clarke and wright savings heuristic for the capacitated vehicle routing problem

In this paper, we consider the capacitated multi-facility Weber problem with rectilinear distance This problem is concerned with locating m capacitated facilities in the Euclidean plane to satisfy the demand of n customers with the minimum total transportation cost The demand and location of each customer are known a priori and the transportation cost between customers and facilities is proportional to the rectilinear distance separating them We first give a new mixed integer linear programming formulation of the problem by making use of a well-known necessary condition for the optimal facility locations We then propose new heuristic solution methods based on this formulation Computational results on benchmark instances indicate that the new methods can provide very good solutions within a reasonable amount of computation time

https://link.springer.com/content/pdf/10.1057%2Fpalgrave.jors.2602262.pdf

Efficient heuristics for the rectilinear distance capacitated multi-facility Weber problem

In this paper we consider the differentiated coverage problem for heterogeneous sensor networks over a finite planning horizon consisting of discrete time intervals. The goal is to determine optimal locations of sensors and sinks, activity schedules of the deployed sensors, and sensor-to-sink data routes subject to coverage, flow conservation, energy consumption and budget constraints with the objective of maximizing the network lifetime. We first give a mixed-integer linear programming formulation. Since this formulation is difficult to be solved exactly even for small instances, we propose a heuristic that first finds connected sensor sets with minimum cost satisfying the coverage constraint, and then determines optimal sensor-to-sink data routes with optimal flow quantities maximizing the lifetime of the sensor sets. Computational experiments performed on various test instances indicate that the heuristic is very efficient and quite accurate.

I K Altınel

Papers

The Kohonen network incorporating explicit statistics and its application to the travelling salesman problem

A new enhancement of the Clarke and wright savings heuristic for the capacitated vehicle routing problem

Efficient heuristics for the rectilinear distance capacitated multi-facility Weber problem

An efficient heuristic for placement, scheduling and routing in wireless sensor networks

Influence Maximization With Deactivation In Social Networks