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A new improved whale optimization algorithm with joint search mechanisms for high-dimensional global optimization problems

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Abstract

Similar to other swarm-based algorithms, the recently developed whale optimization algorithm (WOA) has the problems of low accuracy and slow convergence. It is also easy to fall into local optimum. Moreover, WOA and its variants cannot perform well enough in solving high-dimensional optimization problems. This paper puts forward a new improved WOA with joint search mechanisms called JSWOA for solving the above disadvantages. First, the improved algorithm uses tent chaotic map to maintain the diversity of the initial population for global search. Second, a new adaptive inertia weight is given to improve the convergence accuracy and speed, together with jump out from local optimum. Finally, to enhance the quality and diversity of the whale population, as well as increase the probability of obtaining global optimal solution, opposition-based learning mechanism is used to update the individuals of the whale population continuously during each iteration process. The performance of the proposed JSWOA is tested by twenty-three benchmark functions of various types and dimensions. Then, the results are compared with the basic WOA, several variants of WOA and other swarm-based intelligent algorithms. The experimental results show that the proposed JSWOA algorithm with multi-mechanisms is superior to WOA and the other state-of-the-art algorithms in the competition, exhibiting remarkable advantages in the solution accuracy and convergence speed. It is also suitable for dealing with high-dimensional global optimization problems.

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Acknowledgements

This paper is supported by Nation Science Foundation of China (No. 41404008), Fuzhou Science and Technology Project (No. 2017-G-73), Guiding Project of Fujian Science and Technology Program (No. 2018Y0021), Open Foundation of Key Laboratory for Digital Land and Resources of Jiangxi Province (No. DLLJ201911).

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Authors and Affiliations

  1. College of Civil Engineering, Fuzhou University, Fuzhou, 350116, China

    Qian Fan, Zhenjian Chen, Zhanghua Xia & Dongzheng Wang

  2. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, 999077, Hong Kong, China

    Zhao Li

  3. College of Civil Engineering, Hunan University, Changsha, 410082, Hunan, China

    Jiayong Yu

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  1. Qian Fan
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Correspondence to Zhao Li.

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Fan, Q., Chen, Z., Li, Z. et al. A new improved whale optimization algorithm with joint search mechanisms for high-dimensional global optimization problems. Engineering with Computers 37, 1851–1878 (2021). https://doi.org/10.1007/s00366-019-00917-8

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