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Modified collective decision optimization algorithm with application in trajectory planning of UAV

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Abstract

Recently, a new heuristic method called collective decision optimization algorithm (CDOA) was proposed. This paradigm is inspired from the decision-making behaviour of human beings, including the different factors influencing the decisions, such as experience, opinion of others, group thinking, opinion of the leader, and innovation. However, the original version of the algorithm concentrated only on a fixed evolution order. This study introduces an extended version of the CDOA (ECDOA) for the evolution mechanism without making any major conceptual change to its architecture. In ECDOA, all the agents break the bonds of the original operator sequence. With the exception of the innovation operator, all the other operators are initially stored in an external archive, and then each agent combines at least one randomly selected operator from this archive with an innovation operator to create new update orders. This method not only provides more calculation sequences in each iteration, but also generates more promising candidate solutions. In addition, several operators are further modified to improve the optimization abilities. A comprehensive set of modern benchmark functions and UAV path planning are required to verify the effectiveness of the ECDOA thoroughly. The results of the series-simulation comparison, which simultaneously consider both the convergence and accuracy, indicate that ECDOA is more effective and feasible than the other state-of-art optimization paradigms.

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Acknowledgments

The authors express their sincere thanks to Dr. Xin-she Yang for providing the codes. The authors would also like to thank Dr. Q.Y. Jiang and the anonymous reviewers for their free-handed assistance and construction suggestions. This work is partly supported by the National Natural Science Foundation of China under Project code(61075032)and the Anhui Provincial Natural Science Foundation under Project code (J2014AKZR0055).

Author information

Authors and Affiliations

  1. School of Computer and Information, Hefei University of Technology, Hefei, 230009, China

    Qingyang Zhang, Ronggui Wang, Juan Yang, Kai Ding, Yongfu Li & Jiangen Hu

Authors
  1. Qingyang Zhang
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  2. Ronggui Wang
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  3. Juan Yang
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  4. Kai Ding
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  5. Yongfu Li
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Corresponding author

Correspondence to Qingyang Zhang.

Appendix: The brief descriptions of the CEC2014 benchmark functions

Appendix: The brief descriptions of the CEC2014 benchmark functions

Test function

Types

Optimum

f 1 : Rotated High Conditioned Elliptic Function

Unimodal

100

f 2 : Rotated Bent Cigar Function

 

200

f 3 : Rotated Discus Function

 

300

f 4 : Shifted and Rotated Rosenbrock’s Function

Simple Multimodal

400

f 5 : Shifted and Rotated Ackley’s Function

 

500

f 6 : Shifted and Rotated Weierstrass Function

 

600

f 7 : Shifted and Rotated Griewanks Function

 

700

f 8 : Shifted Rastrigins Function

 

800

f 9 : Shifted and Rotated Rastrigins Function

 

900

f 10 : Shifted Schwefels Function

 

1000

f 11 : Shifted and Rotated Schwefels Function

 

1100

f 12 : Shifted and Rotated Katsuura Function

 

1200

f 13 : Shifted and Rotated HappyCat Function

 

1300

f 14 : Shifted and Rotated HGBat Function

 

1400

f 15 : Shifted and Rotated Expanded Griewank’s plus Rosenbrock’s Function

 

1500

f 16 : Shifted and Rotated Expanded Scaffer’s F6 Function

 

1600

f 17 : Hybrid Function 1 (N = 3)

Hybrid

1700

f 18 : Hybrid Function 2 (N = 3)

 

1800

f 19 : Hybrid Function 3 (N = 4)

 

1900

f 20 : Hybrid Function 4 (N = 4)

 

2000

f 21 : Hybrid Function 5 (N = 5)

 

2100

f 22 : Hybrid Function 6 (N = 5)

 

2200

f 23 : Composition Function 1 (N = 5)

Composition

2300

f 24 : Composition Function 2 (N = 3)

 

2400

f 25 : Composition Function 3 (N = 3)

 

2500

f 26 : Composition Function 4 (N = 5)

 

2600

f 27 : Composition Function 5 (N = 5)

 

2700

f 28 : Composition Function 6 (N = 5)

 

2800

f 29 : Composition Function 7 (N = 3)

 

2900

f 30 : Composition Function 8 (N = 3)

 

3000

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Zhang, Q., Wang, R., Yang, J. et al. Modified collective decision optimization algorithm with application in trajectory planning of UAV. Appl Intell 48, 2328–2354 (2018). https://doi.org/10.1007/s10489-017-1082-1

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