Beetle swarm optimization algorithm: Theory and application

Wang, Tiantian; Yang, Long; Liu, Qiang

doi:10.2298/fil2015121w

Cited by 55 publications

(40 citation statements)

References 38 publications

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“…In order to increase the experimental convincing, it is necessary to compare the particle swarm algorithm (PSO), gray wolf algorithm (GWO), beetle swarm optimization (BSO), SSA, CSSA, and the proposed algorithm optimization effect. Among them, CSSA is an improved sparrow search algorithm in the literature [5], and BSO [21] is a new Scientific Programming algorithm which combines beetle antennae search algorithm (BAS) [22] and PSO, and the research on this algorithm is very popular. e complexity and parameters of all algorithms are shown in Table 2.…”

Section: Algorithm Performance Testmentioning

confidence: 99%

Adaptive Spiral Flying Sparrow Search Algorithm

Ouyang

Qiu

Zhu

2021

Scientific Programming

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The sparrow search algorithm is a new type of swarm intelligence optimization algorithm with better effect, but it still has shortcomings such as easy to fall into local optimality and large randomness. In order to solve these problems, this paper proposes an adaptive spiral flying sparrow search algorithm (ASFSSA), which reduces the probability of getting stuck into local optimum, has stronger optimization ability than other algorithms, and also finds the shortest and more stable path in robot path planning. First, the tent mapping based on random variables is used to initialize the population, which makes the individual position distribution more uniform, enlarges the workspace, and improves the diversity of the population. Then, in the discoverer stage, the adaptive weight strategy is integrated with Levy flight mechanism, and the fusion search method becomes extensive and flexible. Finally, in the follower stage, a variable spiral search strategy is used to make the search scope of the algorithm more detailed and increase the search accuracy. The effectiveness of the improved algorithm ASFSSA is verified by 18 standard test functions. At the same time, ASFSSA is applied to robot path planning. The feasibility and practicability of ASFSSA are verified by comparing the algorithms in the raster map planning routes of two models.

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Section: Algorithm Performance Testmentioning

confidence: 99%

Adaptive Spiral Flying Sparrow Search Algorithm

Ouyang

Qiu

Zhu

2021

Scientific Programming

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“…To achieve that purpose, a collaborative form of BAS is developed in this paper. This collaborative BAS is completely different from the Beetle Swarm Optimization (BSO) developed using the PSO approach (Wang, Yang, and Liu 2018). Do the beetles share information with each other?…”

Section: Beetle Antennae Search Algorithmmentioning

confidence: 99%

A Collaborative Beetle Antennae Search Algorithm Using Memory Based Adaptive Learning

Ghosh

Martinsen

2021

Applied Artificial Intelligence

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Recently developed Beetle Antennae Search algorithm (BAS) mimics the odor sensing mechanism of the longhorn beetles. The beetles have many species and many of these are advantageous to the nature as well as the mankind. Excepting the odor sensing activity, the beetles are naturally strong insects, and some of them have storage memory for adaptive learning and showcase social behavior. These natural mechanisms make them intelligent enough to perform the routine tasks for existence. This article proposes a novel Storage (Memory) Adaptive Collaborative BAS (SACBAS) algorithm, which incorporates the memory stored adaptive learning. This helps exploit the Group Extreme Value (GEV) instead of the Individual Extreme Values in swarm for faster convergence. Further, the SACBAS uses the reference points based on non-dominated sorting to diversify the state space. To test the data-driven performance of SACBAS, the Support Vector Machine (SVM) algorithm with linear kernel is used in this study. First, the SACBAS algorithm is tested on the multi-objective ZDT and DTLZ test-suites and compared with two recent techniques, the reference points based Nondominated Sorting Genetic Algorithm (NSGA III) and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D). Second, the data-driven SACBAS is tested with realworld cases based on offline data. The proposed SACBAS is shown to handle the offline data efficiently and obtains promising results. The Friedman Test is carried out to differentiate the SACBAS from other two techniques and the Post Hoc Test confirms that the SACBAS obtains better HyperVolume indicator scores and outperforms the NSGA III and MOEA/D.

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“…In this paper, a new heuristic optimization algorithm called BSO is used for trajectory planning. This algorithm was first proposed in [49]. It combines the PSO algorithm with another new heuristic algorithm BAS, which is being applied to various practical problems [50], [51].…”

Section: Algorithm Frameworkmentioning

confidence: 99%

A New Trajectory-Planning Beetle Swarm Optimization Algorithm for Trajectory Planning of Robot Manipulators

Wang

Lin

et al. 2019

IEEE Access

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Based on a heuristic optimization algorithm, this paper proposes a new algorithm named trajectory-planning beetle swarm optimization (TPBSO) algorithm for solving trajectory planning of robots, especially robot manipulators. Firstly, two specific manipulator trajectory planning problems are presented as the practical application of the algorithm, which are point-to-point planning and fixed-geometric-path planning. Then, in order to verify the effectiveness of the algorithm, this paper develops a control model and conducts numerical experiments on two planning tasks. Moreover, it compares with existing algorithms to show the superiority of our proposed algorithm. Finally, the results of numerical comparisons show that algorithm has a relatively faster computational speed and better control performance without increasing computational complexity.

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Beetle swarm optimization algorithm: Theory and application

Cited by 55 publications

References 38 publications

Adaptive Spiral Flying Sparrow Search Algorithm

Adaptive Spiral Flying Sparrow Search Algorithm

A Collaborative Beetle Antennae Search Algorithm Using Memory Based Adaptive Learning

A New Trajectory-Planning Beetle Swarm Optimization Algorithm for Trajectory Planning of Robot Manipulators

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