4 research outputs found
Quantile Approximation of the Erlang Distribution using Differential Evolution Algorithm
Erlang distribution is a particular case of the gamma
distribution and is often used in modeling queues, traffic
congestion in wireless sensor networks, cell residence duration
and finding the optimal queueing model to reduce the
probability of blocking. The application is limited because of
the unavailability of closed-form expression for the quantile
(inverse cumulative distribution) function of the distribution.
The problem is primarily tackled using approximation since
the inversion method cannot be applied. This paper extended a
six parameter quantile model earlier proposed to the
Nakagami distribution to the Erlang distributions.
Consequently, the established relationship between the two
distributions is now extended to their quantile functions. The
quantile model was used to fit the machine (R software) values
with their corresponding quartiles in two ways. Firstly,
artificial neural network (ANN) was used to establish that a
curve fitting can be achieved. Lastly, differential evolution
(DE) algorithm was used to minimize the errors obtained from
the curve fitting and hence estimate the values of the six
parameters of the quantile model that will ensure the best
possible fit, for different values of the parameters that
characterize Erlang distribution. Hence, the problem is
constrained optimization in nature and the DE algorithm was
able to find the different values of the parameters of the
quantile model. The simulation result corroborates theoretical
findings. The work is a welcome result for the quest for a
universal quantile model that can be applied to different
distributions
Differential Evolution in Wireless Communications: A Review
Differential Evolution (DE) is an evolutionary computational
method inspired by the biological processes of evolution and mutation. DE has
been applied in numerous scientific fields. The paper presents a literature review
of DE and its application in wireless communication. The detailed history,
characteristics, strengths, variants and weaknesses of DE were presented. Seven
broad areas were identified as different domains of application of DE in wireless
communications. It was observed that coverage area maximisation and energy
consumption minimisation are the two major areas where DE is applied.
Others areas are quality of service, updating mechanism where candidate positions
learn from a large diversified search region, security and related field applications.
Problems in wireless communications are often modelled as multiobjective
optimisation which can easily be tackled by the use of DE or hybrid of
DE with other algorithms. Different research areas can be explored and DE will
continue to be utilized in this contex
Optimization of Wireless Sensor Networks Based on Differential Evolution Algorithm
To study the optimization problem of wireless sensor network (WSN) based on differential evolution, the single objective differential evolution algorithm is applied and combined with the advantages and disadvantages crossover strategy. Firstly, the path optimization problem in WSN is analyzed, and the optimization model is established. Then, the differential evolution algorithm is used as the search tool to solve the minimum energy consumption in the path optimization model, that is, the optimal path problem. Finally, the comparison experiment is carried out on the classical algorithm genetic algorithm (GA), particle swarm optimization (PSO) and standard differential evolution (DE) algorithm. The results show that the performance of differential evolution algorithm based on crossover strategy is superior to or not worse than that of several contrast algorithms. It can be seen that the differential evolution algorithm based on advantage and disadvantage crossover strategy has good effectiveness
Optimization of Wireless Sensor Networks Based on Differential Evolution Algorithm
To study the optimization problem of wireless sensor network (WSN) based on differential evolution, the single objective differential evolution algorithm is applied and combined with the advantages and disadvantages crossover strategy. Firstly, the path optimization problem in WSN is analyzed, and the optimization model is established. Then, the differential evolution algorithm is used as the search tool to solve the minimum energy consumption in the path optimization model, that is, the optimal path problem. Finally, the comparison experiment is carried out on the classical algorithm genetic algorithm (GA), particle swarm optimization (PSO) and standard differential evolution (DE) algorithm. The results show that the performance of differential evolution algorithm based on crossover strategy is superior to or not worse than that of several contrast algorithms. It can be seen that the differential evolution algorithm based on advantage and disadvantage crossover strategy has good effectiveness