Improved whale algorithm for solving engineering design optimization problems

被引:0
|
作者
Liu J. [1 ,2 ]
Ma Y. [1 ]
Li Y. [3 ]
机构
[1] College of Software, Henan University, Kaifeng
[2] Henan Provincial Intelligent Data Processing Engineering Research Center, Henan University, Kaifeng
[3] Institute of Management Science and Engineering, Henan University, Kaifeng
基金
中国国家自然科学基金;
关键词
Cross-border handling; Engineering design; Feedback mechanism; Optimization; Time complexity; Whale optimization algorithm;
D O I
10.13196/j.cims.2021.07.004
中图分类号
学科分类号
摘要
To better solve the engineering design optimization problems and improve the optimization performance and application ability of the whale optimization algorithm, the whale optimization algorithm based on piecewise random inertia weight and optimal feedback mechanism was proposed. For the random walk foraging strategy, a feedback mechanism based on the current global optimal solution was introduced to speed up the algorithm's convergence speed and enhance the stability of the solution. The piecewise random inertia weight was introduced into the shrinkage encirclement strategy and the spiral bubble net predation strategy, which improved the optimization accuracy and enhanced the ability of algorithm to jump out of the local extremum. The Cross-border processing was modified and improved to eliminate the potential loss of evolution results. Theoretical analysis proved that the improved algorithm had the same time complexity as the basic whale optimization algorithm. The experimental results of 6 representative comparison algorithms on 12 complex benchmark test functions and 3 engineering optimization design problems showed that the proposed algorithm had significantly better optimization performance, solution stability, applicability and effectiveness to different problems by comparing with 5 other comparison algorithms. © 2021, Editorial Department of CIMS. All right reserved.
引用
收藏
页码:1884 / 1897
页数:13
相关论文
共 35 条
  • [1] COLORNI A, DORIGO M, MANIEZZO V., Distributed optimization by ant colonies, Proceedings of European Conference on Artificial Life, pp. 134-142, (1991)
  • [2] SILVA B N, HAN K J., Mutation operator integrated ant colony optimization based domestic appliance scheduling for lucrative demand side management[J], Future Generation Computer Systems, 100, pp. 557-568, (2019)
  • [3] BAI Jianglong, CHEN Hanning, HU Yabao, Et al., Ant colony algorithm based on negative feedback and its application on robot path planning, Computer Integrated Manufacturing Systems, 25, 7, pp. 1767-1774, (2019)
  • [4] KENNEDY J, EBERHART R C., Particle swarm optimization, Proceedings of the IEEE International Conference on Neural Networks, pp. 1942-1948, (1995)
  • [5] ADHIKARI M, SRIRAMA S N., Multi-objective accelerated particle swarm optimization with a container-based scheduling for Internet-of-Things in cloud environment, Journal of Network and Computer Applications, 137, pp. 35-61, (2019)
  • [6] WU Yongming, DAI Longzhou, LI Shaobo, Et al., Mixed assembly line evolution balancing based on improved particle swarm algorithm, Computer Integrated Manufacturing Systems, 23, 4, pp. 781-790, (2017)
  • [7] YANG X S, DEB S., Cukoo search via levy flight[C], Proceedings of World Congress on Nature & Biologically Inspired Computing, pp. 210-214, (2009)
  • [8] CHEN Xu, YU Kunjie, Hybridizing cuckoo search algorithm with biogeography-based optimization for estimating photovoltaic model parameters[J], Solar Energy, 180, pp. 192-206, (2019)
  • [9] YANG X S., Bat algorithm for multi-objective optimization[J], International Journal of Bio-Inspired Computation, 3, 5, pp. 267-274, (2012)
  • [10] LIU Jingsen, JI Hongyuan, LI Yu, Robot path planning based on improved bat algorithm and cubic spline interpolation[J/OL], Acta Automatica Sinica, pp. 1-10, (2019)