Title: DA-APSO: A Hybrid Bio-Inspired Algorithm for Localization in Wireless Sensor Network
Journal: Swarm and Evolutionary Computation
Dear Professor Kim,
Thank you for submitting your manuscript to Swarm and Evolutionary Computation. I regret to inform you that reviewers have advised against publishing your manuscript, and we must therefore reject it.
Please refer to the comments listed at the end of this letter for details of why I reached this decision.
We appreciate your submitting your manuscript to this journal and for giving us the opportunity to consider your work.
Swarm and Evolutionary Computation
Comments from the editors and reviewers:
The authors proposed hybridization of Dragonfly and Algorithm with Accelerated Particle swarm optimization to localize unknown node. In the experiment, the authors present some extensive numerical studies to evaluate the performance of the proposed algorithm with the comparison of other algorithms.
The structure of this manuscript is clear. The written English is good and easy to understand. But the figures in paper are not clear and the text in figure is not complete. In general, I think this manuscript should be addressed following questions.
1. Please number all the formulas in the paper in order according to the uniform format. And the references in the paper are too old.
2. The major work of manuscript had done in previous research. It is lack of novelty in this work.
3. Please give explanation why DA-APSO has bigger localization errors than DA in discussion. And the same question in empirical CDF comparison with DA.
4. Please give the clear and logical data experiment and explanation what contributes to DA-APSO¡¯s excellent performance rather than combination of two algorithms¡¯ advantage.
The abstract should be improved to show the contributions of this paper clearly.
There are so many different algorithms. Why the authors propose a new algorithm DA?
The proposed algorithm should be described in detail and clearly.
Based on the computation time, the proposed algorithm does not have competitiveness.
Future work or directions must be shown in the last section.
Some newest publications must be cited, the current references are too old.
The linguistic must be improved throughout the manuscripts.
I have completed my initial read of this review. I think that the current version is still far from perfect. Some major issues should be addressed as follows.
(1) What are the novelties of your work? Authors should present their contributes clearly.
(2) There are many metaheuristics such as DE, GA, and ACO. Why do authors propose a hybridization of dragonfly algorithm and accelerated PSO?
(3) In the experiment, statistic test like t-test should be conducted to validate the significance difference between difference metaheuristics due to the statistical probability characteristics of these metaheuristics. Moreover, the deep reason why the proposal is better than its counterparts should be stated more clearly in the result analysis.
(4) Conclusion strengthen and explain more.
In wireless sensor network (WSN), accurate location of each sensor node is crucial to ensure its data integrity. By using properties of signal from beacon nodes, each unknown node can calculate its distance to each beacon node and localize itself. In some cases, the deployment of beacon nodes cannot completely cover all unknown nodes and make it difficult to get accurate localization and ensure data integrity. This paper proposes the hybridization of Dragonfly Algorithm (DA) with Accelerated Particle Swarm Optimization (APSO) to localize unknown node accurately and efficiently.
I think the innovation of this paper is not prominent enough. The authors only combine two existing algorithms (Dragonfly Algorithm (DA) and Accelerated Particle Swarm Optimization (APSO) to solve the location problem of sensor nodes. Despite the authors proposed some reasons for doing so, its rationality remains to be further discussed. In addition, these two algorithms have not been merged, but be used in different phases (The exploration phase is handled by DA and exploitation phase is handled by APSO).
I suggest that the author give more innovative results.