4,561 research outputs found
Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation
Combinatorial interaction testing is an important software testing technique
that has seen lots of recent interest. It can reduce the number of test cases
needed by considering interactions between combinations of input parameters.
Empirical evidence shows that it effectively detects faults, in particular, for
highly configurable software systems. In real-world software testing, the input
variables may vary in how strongly they interact, variable strength
combinatorial interaction testing (VS-CIT) can exploit this for higher
effectiveness. The generation of variable strength test suites is a
non-deterministic polynomial-time (NP) hard computational problem
\cite{BestounKamalFuzzy2017}. Research has shown that stochastic
population-based algorithms such as particle swarm optimization (PSO) can be
efficient compared to alternatives for VS-CIT problems. Nevertheless, they
require detailed control for the exploitation and exploration trade-off to
avoid premature convergence (i.e. being trapped in local optima) as well as to
enhance the solution diversity. Here, we present a new variant of PSO based on
Mamdani fuzzy inference system
\cite{Camastra2015,TSAKIRIDIS2017257,KHOSRAVANIAN2016280}, to permit adaptive
selection of its global and local search operations. We detail the design of
this combined algorithm and evaluate it through experiments on multiple
synthetic and benchmark problems. We conclude that fuzzy adaptive selection of
global and local search operations is, at least, feasible as it performs only
second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the
best mean test suite size, the fuzzy adaptation even outperforms DPSO
occasionally. We discuss the reasons behind this performance and outline
relevant areas of future work.Comment: 21 page
Discrete Particle Swarm Optimization for the minimum labelling Steiner tree problem
Particle Swarm Optimization is an evolutionary method inspired by the
social behaviour of individuals inside swarms in nature. Solutions of the problem are
modelled as members of the swarm which fly in the solution space. The evolution is
obtained from the continuous movement of the particles that constitute the swarm
submitted to the effect of the inertia and the attraction of the members who lead the
swarm. This work focuses on a recent Discrete Particle Swarm Optimization for combinatorial optimization, called Jumping Particle Swarm Optimization. Its effectiveness is
illustrated on the minimum labelling Steiner tree problem: given an undirected labelled
connected graph, the aim is to find a spanning tree covering a given subset of nodes,
whose edges have the smallest number of distinct labels
Particle swarm optimization for two-connected networks with bounded rings
The Two-Connected Network with Bounded Ring (2CNBR) problem is a network design
problem addressing the connection of servers to create a survivable network with
limited redirections in the event of failures. Particle Swarm Optimization (PSO) is a
stochastic population-based optimization technique modeled on the social behaviour
of flocking birds or schooling fish. This thesis applies PSO to the 2CNBR problem.
As PSO is originally designed to handle a continuous solution space, modification
of the algorithm was necessary in order to adapt it for such a highly constrained
discrete combinatorial optimization problem. Presented are an indirect transcription
scheme for applying PSO to such discrete optimization problems and an oscillating
mechanism for averting stagnation
Variable neighbourhood search for the minimum labelling Steiner tree problem
We present a study on heuristic solution approaches to the minimum labelling Steiner
tree problem, an NP-hard graph problem related to the minimum labelling spanning tree
problem. Given an undirected labelled connected graph, the aim is to find a spanning
tree covering a given subset of nodes of the graph, whose edges have the smallest number
of distinct labels. Such a model may be used to represent many real world problems in
telecommunications and multimodal transportation networks. Several metaheuristics are
proposed and evaluated. The approaches are compared to the widely adopted Pilot Method
and it is shown that the Variable Neighbourhood Search metaheuristic is the most effective
approach to the problem, obtaining high quality solutions in short computational running
times
Recommended from our members
Variable neighbourhood search for the minimum labelling Steiner tree problem
We present a study on heuristic solution approaches to the minimum labelling Steiner tree problem, an NP-hard graph problem related to the minimum labelling spanning tree problem. Given an undirected labelled connected graph, the aim is to find a spanning tree covering a given subset of nodes of the graph, whose edges have the smallest number of distinct labels. Such a model may be used to represent many real world problems in telecommunications and multimodal transportation networks. Several metaheuristics are proposed and evaluated. The approaches are compared to the widely adopted Pilot Method and it is shown that the Variable Neighbourhood Search that we propose is the most effective metaheuristic for the problem, obtaining high quality solutions in short computational running time
- …