WCA

Water cycle algorithm: A detailed standard code. Inspired by the observation of the water cycle process and movements of rivers and streams toward the sea, a population-based metaheuristic algorithm, the water cycle algorithm (WCA) has recently been proposed. Lately, an increasing number of WCA applications have appeared and the WCA has been utilized in different optimization fields. This paper provides detailed open source code for the WCA, of which the performance and efficiency has been demonstrated for solving optimization problems. The WCA has an interesting and simple concept and this paper aims to use its source code to provide a step-by-step explanation of the process it follows.


References in zbMATH (referenced in 27 articles , 1 standard article )

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  1. Chou, Jui-Sheng; Truong, Dinh-Nhat: A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean (2021)
  2. Tenreiro Machado, J. A.; Abedi Pahnehkolaei, Seyed Mehdi; Alfi, Alireza: Complex-order particle swarm optimization (2021)
  3. Ustun, Deniz; Carbas, Serdar; Toktas, Abdurrahim: Multi-objective optimization of engineering design problems through Pareto-based bat algorithm (2021)
  4. Yan, Zheping; Zhang, Jinzhong; Zeng, Jia; Tang, Jialing: Nature-inspired approach: an enhanced whale optimization algorithm for global optimization (2021)
  5. Zouache, Djaafar; Ben Abdelaziz, Fouad; Lefkir, Mira; Chalabi, Nour El-Houda: Guided moth-flame optimiser for multi-objective optimization problems (2021)
  6. Chauhan, Sandeep Singh; Kotecha, Prakash: Single-level production planning in petrochemical industries using novel computational intelligence algorithms (2020)
  7. Khalilpourazari, Soheyl; Mirzazadeh, Abolfazl; Weber, Gerhard-Wilhelm; Pasandideh, Seyed Hamid Reza: A robust fuzzy approach for constrained multi-product economic production quantity with imperfect items and rework process (2020)
  8. Kommadath, Remya; Kotecha, Prakash: Scheduling of jobs on dissimilar parallel machine using computational intelligence algorithms (2020)
  9. Kustudic, Mijat; Ben, Niu: A bacterial foraging framework for agent based modeling (2020)
  10. Liu, Jingsen; Xing, Yuhao; Ma, Yixiang; Li, Yu: Gravitational search algorithm based on multiple adaptive constraint strategy (2020)
  11. Luo, Jianping; Huang, Xiongwen; Yang, Yun; Li, Xia; Wang, Zhenkun; Feng, Jiqiang: A many-objective particle swarm optimizer based on indicator and direction vectors for many-objective optimization (2020)
  12. Qu, Chiwen; He, Wei; Peng, Xiangni; Peng, Xiaoning: Harris Hawks optimization with information exchange (2020)
  13. Babayan, Narek; Tahani, Mojtaba: Team arrangement heuristic algorithm (TAHA): theory and application (2019)
  14. Łapa, Krystian: Meta-optimization of multi-objective population-based algorithms using multi-objective performance metrics (2019)
  15. Luo, Qifang; Yang, Xiao; Zhou, Yongquan: Nature-inspired approach: an enhanced moth swarm algorithm for global optimization (2019)
  16. Ng, Kien Ming; Tran, Trung Hieu: A parallel water flow algorithm with local search for solving the quadratic assignment problem (2019)
  17. Ali, Javaid; Saeed, Muhammad; Rafiq, Muhammad; Iqbal, Shaukat: Numerical treatment of nonlinear model of virus propagation in computer networks: an innovative evolutionary Padé approximation scheme (2018)
  18. Bian, Chentong; Yin, Guodong; Xu, Liwei; Zhang, Ning: Virtual belt algorithm for the management of isolated autonomous intersection (2018)
  19. Farnad, Behnam; Jafarian, Ahmad: A new nature-inspired hybrid algorithm with a penalty method to solve constrained problem (2018)
  20. Farnad, Behnam; Jafarian, Ahmad; Baleanu, Dumitru: A new hybrid algorithm for continuous optimization problem (2018)

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