7,448 research outputs found
Modeling and Joint Optimization of Security, Latency, and Computational Cost in Blockchain-based Healthcare Systems
In the era of the Internet of Things (IoT), blockchain is a promising
technology for improving the efficiency of healthcare systems, as it enables
secure storage, management, and sharing of real-time health data collected by
the IoT devices. As the implementations of blockchain-based healthcare systems
usually involve multiple conflicting metrics, it is essential to balance them
according to the requirements of specific scenarios. In this paper, we
formulate a joint optimization model with three metrics, namely latency,
security, and computational cost, that are particularly important for
IoT-enabled healthcare. However, it is computationally intractable to identify
the exact optimal solution of this problem for practical sized systems. Thus,
we propose an algorithm called the Adaptive Discrete Particle Swarm Algorithm
(ADPSA) to obtain near-optimal solutions in a low-complexity manner. With its
roots in the classical Particle Swarm Optimization (PSO) algorithm, our
proposed ADPSA can effectively manage the numerous binary and integer variables
in the formulation. We demonstrate by extensive numerical experiments that the
ADPSA consistently outperforms existing benchmark approaches, including the
original PSO, exhaustive search and Simulated Annealing, in a wide range of
scenarios
A Discrete Particle Swarm Optimization Algorithm for Bi-Criteria Warehouse Location Problem
The uncapacitated warehouse location problem (UWLP) is one of the widely studied discrete location problems, in which the nodes (customers) are connected to a number (w) of warehouses in such a way that the total cost, yields from the dissimilarities (distances) and from the fixed costs of the warehouses is minimized. Despite w is considered as fixed integer number, the UWLP is NP-hard. If the UWLP has two or more objective functions and w is an integer variable, the UWLP becomes more complex. Large size of this kind of complex problems can be solved by using heuristic algorithms or artificial intelligent techniques. It’s shown that Particle Swarm Optimization (PSO) which is one of the technique of artificial intelligent techniques, has achieved a notable success for continuous optimization, however, PSO implementations and applications for combinatorial optimization are still active research area that to the best of our knowledge fewer studies have been carried out on this topic. In this study, the bi-criteria UWLP of minimizing the total distance and total opening cost of warehouses. is presented and it’s shown that promising results are obtained.Warehouse Location Problem, Particle Swarm Optimization, Discrete Location Problems, Bi-criteria.
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
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