1,896 research outputs found
On the use of biased-randomized algorithms for solving non-smooth optimization problems
Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory management, it is possible to consider stock-outs generated by unexpected demands; and in manufacturing processes and project management, it is frequent that some deadlines cannot be met due to delays in critical steps of the supply chain. However, capacity-, size-, and time-related limitations are included in many optimization problems as hard constraints, while it would be usually more realistic to consider them as soft ones, i.e., they can be violated to some extent by incurring a penalty cost. Most of the times, this penalty cost will be nonlinear and even noncontinuous, which might transform the objective function into a non-smooth one. Despite its many practical applications, non-smooth optimization problems are quite challenging, especially when the underlying optimization problem is NP-hard in nature. In this paper, we propose the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and non-smooth optimization problems in many practical applications. Biased-randomized algorithms extend constructive heuristics by introducing a nonuniform randomization pattern into them. Hence, they can be used to explore promising areas of the solution space without the limitations of gradient-based approaches, which assume the existence of smooth objective functions. Moreover, biased-randomized algorithms can be easily parallelized, thus employing short computing times while exploring a large number of promising regions. This paper discusses these concepts in detail, reviews existing work in different application areas, and highlights current trends and open research lines
An (MI)LP-based Primal Heuristic for 3-Architecture Connected Facility Location in Urban Access Network Design
We investigate the 3-architecture Connected Facility Location Problem arising
in the design of urban telecommunication access networks. We propose an
original optimization model for the problem that includes additional variables
and constraints to take into account wireless signal coverage. Since the
problem can prove challenging even for modern state-of-the art optimization
solvers, we propose to solve it by an original primal heuristic which combines
a probabilistic fixing procedure, guided by peculiar Linear Programming
relaxations, with an exact MIP heuristic, based on a very large neighborhood
search. Computational experiments on a set of realistic instances show that our
heuristic can find solutions associated with much lower optimality gaps than a
state-of-the-art solver.Comment: This is the authors' final version of the paper published in:
Squillero G., Burelli P. (eds), EvoApplications 2016: Applications of
Evolutionary Computation, LNCS 9597, pp. 283-298, 2016. DOI:
10.1007/978-3-319-31204-0_19. The final publication is available at Springer
via http://dx.doi.org/10.1007/978-3-319-31204-0_1
Towards the fast and robust optimal design of Wireless Body Area Networks
Wireless body area networks are wireless sensor networks whose adoption has
recently emerged and spread in important healthcare applications, such as the
remote monitoring of health conditions of patients. A major issue associated
with the deployment of such networks is represented by energy consumption: in
general, the batteries of the sensors cannot be easily replaced and recharged,
so containing the usage of energy by a rational design of the network and of
the routing is crucial. Another issue is represented by traffic uncertainty:
body sensors may produce data at a variable rate that is not exactly known in
advance, for example because the generation of data is event-driven. Neglecting
traffic uncertainty may lead to wrong design and routing decisions, which may
compromise the functionality of the network and have very bad effects on the
health of the patients. In order to address these issues, in this work we
propose the first robust optimization model for jointly optimizing the topology
and the routing in body area networks under traffic uncertainty. Since the
problem may result challenging even for a state-of-the-art optimization solver,
we propose an original optimization algorithm that exploits suitable linear
relaxations to guide a randomized fixing of the variables, supported by an
exact large variable neighborhood search. Experiments on realistic instances
indicate that our algorithm performs better than a state-of-the-art solver,
fast producing solutions associated with improved optimality gaps.Comment: Authors' manuscript version of the paper that was published in
Applied Soft Computin
A Trust Based Congestion Aware Hybrid Ant Colony Optimization Algorithm for Energy Efficient Routing in Wireless Sensor Networks (TC-ACO)
Congestion is a problem of paramount importance in resource constrained
Wireless Sensor Networks, especially for large networks, where the traffic
loads exceed the available capacity of the resources. Sensor nodes are prone to
failure and the misbehavior of these faulty nodes creates further congestion.
The resulting effect is a degradation in network performance, additional
computation and increased energy consumption, which in turn decreases network
lifetime. Hence, the data packet routing algorithm should consider congestion
as one of the parameters, in addition to the role of the faulty nodes and not
merely energy efficient protocols. Unfortunately most of the researchers have
tried to make the routing schemes energy efficient without considering
congestion factor and the effect of the faulty nodes. In this paper we have
proposed a congestion aware, energy efficient, routing approach that utilizes
Ant Colony Optimization algorithm, in which faulty nodes are isolated by means
of the concept of trust. The merits of the proposed scheme are verified through
simulations where they are compared with other protocols.Comment: 6 pages, 5 figures and 2 tables (Conference Paper
QoS multicast tree construction in IP/DWDM optical internet by bio-inspired algorithms
Copyright @ Elsevier Ltd. All rights reserved.In this paper, two bio-inspired Quality of Service (QoS) multicast algorithms are proposed in IP over dense wavelength division multiplexing (DWDM) optical Internet. Given a QoS multicast request and the delay interval required by the application, both algorithms are able to find a flexible QoS-based cost suboptimal routing tree. They first construct the multicast trees based on ant colony optimization and artificial immune algorithm, respectively. Then a dedicated wavelength assignment algorithm is proposed to assign wavelengths to the trees aiming to minimize the delay of the wavelength conversion. In both algorithms, multicast routing and wavelength assignment are integrated into a single process. Therefore, they can find the multicast trees on which the least wavelength conversion delay is achieved. Load balance is also considered in both algorithms. Simulation results show that these two bio-inspired algorithms can construct high performance QoS routing trees for multicast applications in IP/DWDM optical Internet.This work was supported in part ny the Program for New Century Excellent Talents in University, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1, the National Natural Science Foundation of China under Grant no. 60673159 and 70671020, the National High-Tech Reasearch and Development Plan of China under Grant no. 2007AA041201, and the Specialized Research Fund for the Doctoral Program of Higher Education under Grant no. 20070145017
An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks
Maximizing the lifetime of wireless sensor networks (WSNs) is a challenging problem. Although some methods exist to address the problem in homogeneous WSNs, research on this problem in heterogeneous WSNs have progressed at a slow pace. Inspired by the promising performance of ant colony optimization (ACO) to solve combinatorial problems, this paper proposes an ACO-based approach that can maximize the lifetime of heterogeneous WSNs. The methodology is based on finding the maximum number of disjoint connected covers that satisfy both sensing coverage and network connectivity. A construction graph is designed with each vertex denoting the assignment of a device in a subset. Based on pheromone and heuristic information, the ants seek an optimal path on the construction graph to maximize the number of connected covers. The pheromone serves as a metaphor for the search experiences in building connected covers. The heuristic information is used to reflect the desirability of device assignments. A local search procedure is designed to further improve the search efficiency. The proposed approach has been applied to a variety of heterogeneous WSNs. The results show that the approach is effective and efficient in finding high-quality solutions for maximizing the lifetime of heterogeneous WSNs
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