A novel swarm intelligence optimization approach: sparrow search algorithm

Xue, Jiankai; Shen, Bo

doi:10.1080/21642583.2019.1708830

Cited by 2,281 publications

(1,012 citation statements)

References 29 publications

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“…According to the results of previous research, the original SSA has better robustness and rapid convergence speed [27][28][29][30][31]. However, some shortcomings still exist in SSA, such as being easily trapped into local optimal solutions and lower solution precision.…”

Section: Proposed Modified Sparrow Search Algorithm (Cassa)mentioning

confidence: 99%

“…This algorithm was put forward by Ref. [ 27 ] in 2020 and has drawn great attention recently. During the foraging process of sparrows, the colony is divided into finders and entrants.…”

Section: Details Of Optimization Techniquesmentioning

confidence: 99%

“…Inspired by the predatory behavior of sparrows, the sparrow search algorithm (SSA) is firstly proposed in Ref. [ 27 ]. As a heuristic algorithm, the SSA imitates the predatory activity and the escape activity of a sparrow group, which has better convergence performance than some other swarm-intelligence algorithms.…”

Section: Introductionmentioning

confidence: 99%

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A Modified Sparrow Search Algorithm with Application in 3d Route Planning for UAV

Liu

Shu

Liang

et al. 2021

Sensors

180

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The unmanned aerial vehicle (UAV) route planning problem mainly centralizes on the process of calculating the best route between the departure point and target point as well as avoiding obstructions on route to avoid collisions within a given flight area. A highly efficient route planning approach is required for this complex high dimensional optimization problem. However, many algorithms are infeasible or have low efficiency, particularly in the complex three-dimensional (3d) flight environment. In this paper, a modified sparrow search algorithm named CASSA has been presented to deal with this problem. Firstly, the 3d task space model and the UAV route planning cost functions are established, and the problem of route planning is transformed into a multi-dimensional function optimization problem. Secondly, the chaotic strategy is introduced to enhance the diversity of the population of the algorithm, and an adaptive inertia weight is used to balance the convergence rate and exploration capabilities of the algorithm. Finally, the Cauchy–Gaussian mutation strategy is adopted to enhance the capability of the algorithm to get rid of stagnation. The results of simulation demonstrate that the routes generated by CASSA are preferable to the sparrow search algorithm (SSA), particle swarm optimization (PSO), artificial bee colony (ABC), and whale optimization algorithm (WOA) under the identical environment, which means that CASSA is more efficient for solving UAV route planning problem when taking all kinds of constraints into consideration.

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Section: Proposed Modified Sparrow Search Algorithm (Cassa)mentioning

confidence: 99%

“…This algorithm was put forward by Ref. [ 27 ] in 2020 and has drawn great attention recently. During the foraging process of sparrows, the colony is divided into finders and entrants.…”

Section: Details Of Optimization Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

A Modified Sparrow Search Algorithm with Application in 3d Route Planning for UAV

Liu

Shu

Liang

et al. 2021

Sensors

180

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“…These algorithms are BSA [ 7 ], DE [ 32 ], DMSDL-PSO [ 31 ], and DMSDL-BSA. Another 5 popular intelligence algorithms, such as grey wolf optimizer (GWO) [ 38 ], whale optimization algorithm (WOA) [ 39 ], sine cosine algorithm (SCA) [ 40 ], grasshopper optimization algorithm (GOA) [ 41 ], and sparrow search algorithm (SSA) [ 42 ], are used to compare with DMSDL-QBSA. These algorithms represented state-of-the-art can be used to better verify the performance of DMSDL-QBSA in a more comprehensively manner.…”

Section: Bird Swarm Algorithm and Its Improvementmentioning

confidence: 99%

Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model

Zhang

Xia

et al. 2020

Computational Intelligence and Neuroscience

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Bird swarm algorithm is one of the swarm intelligence algorithms proposed recently. However, the original bird swarm algorithm has some drawbacks, such as easy to fall into local optimum and slow convergence speed. To overcome these short-comings, a dynamic multi-swarm differential learning quantum bird swarm algorithm which combines three hybrid strategies was established. First, establishing a dynamic multi-swarm bird swarm algorithm and the differential evolution strategy was adopted to enhance the randomness of the foraging behavior’s movement, which can make the bird swarm algorithm have a stronger global exploration capability. Next, quantum behavior was introduced into the bird swarm algorithm for more efficient search solution space. Then, the improved bird swarm algorithm is used to optimize the number of decision trees and the number of predictor variables on the random forest classification model. In the experiment, the 18 benchmark functions, 30 CEC2014 functions, and the 8 UCI datasets are tested to show that the improved algorithm and model are very competitive and outperform the other algorithms and models. Finally, the effective random forest classification model was applied to actual oil logging prediction. As the experimental results show, the three strategies can significantly boost the performance of the bird swarm algorithm and the proposed learning scheme can guarantee a more stable random forest classification model with higher accuracy and efficiency compared to others.

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“…It consists of five variables, three equality constraints and six inequality constraints. The specific forms are as follows: Several researchers have employed different algorithms to solve this problem, such as SSA [45], RW -GWO [46], hybrid GSA -GA [47], AQPSO [48], fb_TSA [49], and DGWO [50]. Table 8 shows the best results obtained from the CISO method compared to the previously mentioned methods.…”

Section: B Himmelblau's Optimization Problemmentioning

confidence: 99%

Cicada Swarm Optimization: A New Method for Optimizing Persistent Problems

Akkar¹,

Salman²

2020

IJIES

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This paper proposes a new meta-heuristic swarm optimization algorithm called Cicada Swarm Optimization (CISO) algorithm, which mimics the behavior of bio-inspired swarm optimization methods. The CISO algorithm is tested with 23 benchmark functions and taken two problems engineering design, pressure vessel problem and himmelblau’s problem. The performance of CISO algorithm is compared with meta-heuristic well-known and recently proposed algorithms (Cockroach Swarm Optimization (CSO), Grasshopper Optimization algorithm (GOA) and Particle Swarm Optimization (PSO)). The obtained results showed that the proposed algorithm succeeded in improving the test functions and solved engineering design problems that could not be improved by other algorithms according to the chosen parameters and the limits of the research space, also showed that CISO has a faster convergence with the minimum number of iterations and also have an accurate calculation efficiency Satisfactory compared to other optimization algorithms.

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A novel swarm intelligence optimization approach: sparrow search algorithm

Cited by 2,281 publications

References 29 publications

A Modified Sparrow Search Algorithm with Application in 3d Route Planning for UAV

A Modified Sparrow Search Algorithm with Application in 3d Route Planning for UAV

Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model

Cicada Swarm Optimization: A New Method for Optimizing Persistent Problems

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