An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path Planning

Wang, Ruo-Bin; Wang, Weifeng; Xu, Lin; Pan, Jeng‐Shyang; Chu, Shu-Chuan

doi:10.1155/2021/3606895

Cited by 32 publications

(14 citation statements)

References 39 publications

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“…Therefore, the parallel technique is employed to avoid the defects. Parallelism [49][50][51][52][53][54], from a macro perspective, is that multiple groups operate simultaneously. Communication and exchange of information among groups can be realized by parallelism, and its common idea uses better solutions from some groups to replace worse solutions from other groups.…”

Section: Parallel Techniquesmentioning

confidence: 99%

Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

Wang

et al. 2022

Wireless Netw

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Improving localization performance is one of the critical issues in Wireless Sensor Networks (WSN). As a range-free localization algorithm, Distance Vector-Hop(DV-Hop) is well-known for its simplicity but is hindered by its low accuracy and poor stability. Therefore, it is necessary to improve DV-Hop to achieve a competitive performance. However, the comprehensive performance of WSN is limited by computing and storage capabilities of sensor nodes. In this paper, we propose an algorithm with parallel and compact techniques based on Whale Optimization Algorithm (PCWOA) to improve DV-Hop performance. The compact technique saves memory consumption by reducing the original population. The parallel techniques enhance the ability to jump out of local optimization and improve the solution accuracy. The proposed algorithm is tested on CEC2013 benchmark functions and compared with some popular algorithms and compact algorithms. Experimental results show that the improved algorithm achieves competitive results over compared algorithms. Finally, simulation research is conducted to verify the localization performance of our proposed algorithm.

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Section: Parallel Techniquesmentioning

confidence: 99%

Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

Wang

et al. 2022

Wireless Netw

Self Cite

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“…Wang et al [105] introduced an adaptive parallel arithmetic optimization algorithm (APAOA) with a unique parallel communication strategy to address the problem of robot path planning with the aim of searching a collision-free optimal motion path in an environment containing obstacles. The fitness value of the particles serves as the foundation for the suggested adaptive equation.…”

Section: Other Utilized Strategies To Improve Aoamentioning

confidence: 99%

A Comprehensive Survey on Arithmetic Optimization Algorithm

Dhal

Sasmal

Das

et al. 2023

Arch Computat Methods Eng

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Arithmetic Optimization Algorithm (AOA) is a recently developed population-based nature-inspired optimization algorithm (NIOA). AOA is designed under the inspiration of the distribution behavior of the main arithmetic operators in mathematics and hence, it also belongs to mathematics-inspired optimization algorithm (MIOA). MIOA is a powerful subset of NIOA and AOA is a proficient member of it. AOA is published in early 2021 and got a massive recognition from research fraternity due to its superior efficacy in different optimization fields. Therefore, this study presents an up-to-date survey on AOA, its variants, and applications.

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“…To further enhance AOA performance, researchers have proposed a variety of stochastic operators such as opposition-based learning (Abualigah et al, 2022;Yang et al, 2022), and Gaussian mutation mechanism and sinusoidal chaotic map (Xu et al, 2021) to address such issues. In another enhancement, such as in (Wang et al, 2021) AOA population is divided among multiple groups, where each group operates independently and exchanges information among randomly selected groups after fixed number of iterations. However, it was not implemented on a parallel computing system.…”

Section: Literature Reviewmentioning

confidence: 99%

A Spark-Based Parallel Implementation of Arithmetic Optimization Algorithm

AlJame

Al-Noori

Alfailakawi

et al. 2023

International Journal of Applied Metaheuristic Computing

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Arithmetic optimization algorithm (AOA) is a recent population-based metaheuristic widely used for solving optimization problems. However, the emerging large-scale optimization problems pose a great challenge for AOA due to its prohibitive computational cost to traverse the huge solution space effectively. This article proposes a parallel Spark-AOA using Scala on Apache Spark computing platform. Spark-AOA leverages the intrinsic parallel nature of the population-based AOA and the native iterative in-memory computation support of Spark through resilient distributed datasets (RDD) to accelerate the optimization process. Spark-AOA divides the solutions population into several subpopulations that are distributed into multiple RDD partitions and manipulated concurrently. Simulation experiments on different benchmark functions with up to 1,000-dimension and three engineering design problems demonstrate that Spark-AOA outperforms considerably standard AOA and Spark-based implementations of two recent metaheuristics both in terms of run-time and solution quality.

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An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path Planning

Cited by 32 publications

References 39 publications

Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

A Comprehensive Survey on Arithmetic Optimization Algorithm

A Spark-Based Parallel Implementation of Arithmetic Optimization Algorithm

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