Multiple hydropower reservoirs operation optimization by adaptive mutation sine cosine algorithm based on neighborhood search and simplex search strategies

2020 ◽  
Vol 590 ◽  
pp. 125223 ◽  
Author(s):  
Zhong-kai Feng ◽  
Wen-jing Niu ◽  
Shuai Liu ◽  
Bin Luo ◽  
Shu-min Miao ◽  
...  
Keyword(s):  
Search Strategies ◽  
Neighborhood Search ◽  
Adaptive Mutation ◽  
Operation Optimization ◽  
Simplex Search ◽  
Sine Cosine Algorithm ◽  
Hydropower Reservoirs

Reactive Search strategies using Reinforcement Learning, local search algorithms and Variable Neighborhood Search

2014 ◽  
Vol 41 (10) ◽  
pp. 4939-4949 ◽  
Author(s):  
João Paulo Queiroz dos Santos ◽  
Jorge Dantas de Melo ◽  
Adrião Dória Duarte Neto ◽  
Daniel Aloise
Keyword(s):  
Reinforcement Learning ◽  
Local Search ◽  
Variable Neighborhood Search ◽  
Search Strategies ◽  
Search Algorithms ◽  
Neighborhood Search ◽  
Local Search Algorithms

scholarly journals Peak Operation Problem Solving for Hydropower Reservoirs by Elite-Guide Sine Cosine Algorithm with Gaussian Local Search and Random Mutation

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2189 ◽  
Author(s):  
Shuai Liu ◽  
Zhong-Kai Feng ◽  
Wen-Jing Niu ◽  
Hai-Rong Zhang ◽  
Zhen-Guo Song
Keyword(s):  
Power Systems ◽  
Local Search ◽  
Electrical Power ◽  
Evolutionary Process ◽  
Electrical Power Systems ◽  
Random Mutation ◽  
Peak Loads ◽  
Sine Cosine Algorithm ◽  
Local Search Strategy ◽  
Hydropower Reservoirs

In recent years, growing peak pressure is posing a huge challenge for the operators of electrical power systems. As the most important clean renewable energy, hydropower is often advised as a response to the peak loads in China. Thus, a novel hybrid sine cosine algorithm (HSCA) is proposed to deal with the complex peak operation problem of cascade hydropower reservoirs. In HSCA, the elite-guide evolution strategy is embedded into the standard sine cosine algorithm to improve the convergence rate of the swarm. The Gaussian local search strategy is used to increase the diversity of the population. The random mutation operator is adopted to enhance the search capability of the individuals in the evolutionary process. The proposed method is applied to solve the complex peak operation problem of two hydropower systems. The simulations indicate that in different cases, HSCA can generate the scheduling results with higher quality than several benchmark methods. Hence, this paper provides a feasible method for the complex peak operation problem of cascade hydropower reservoirs.

scholarly journals A Multistrategy Artificial Bee Colony Algorithm Enlightened by Variable Neighborhood Search

2019 ◽  
Vol 2019 ◽  
pp. 1-19
Author(s):  
Wan-li Xiang ◽  
Yin-zhen Li ◽  
Rui-chun He ◽  
Xue-lei Meng ◽  
Mei-qing An
Keyword(s):  
Variable Neighborhood Search ◽  
Artificial Bee Colony ◽  
Search Strategy ◽  
Random Search ◽  
Search Strategies ◽  
Neighborhood Change ◽  
Neighborhood Search ◽  
Bee Colony ◽  
Opposition Based Learning ◽  
Change Of Variable

Artificial bee colony (ABC) has a good exploration ability against its exploitation ability. For enhancing its comprehensive performance, we proposed a multistrategy artificial bee colony (ABCVNS for short) based on the variable neighborhood search method. First, a search strategy candidate pool composed of two search strategies, i.e., ABC/best/1 and ABC/rand/1, is proposed and employed in the employed bee phase and onlooker bee phase. Second, we present another search strategy candidate pool which consists of the original random search strategy and the opposition-based learning method. Then, it is used to further balance the exploration and exploitation abilities in the scout bee phase. Last but not least, motivated by the scheme of neighborhood change of variable neighborhood search, a simple yet efficient choice mechanism of search strategies is presented. Subsequently, the effectiveness of ABCVNS is carried out on two test suites composed of fifty-eight problems. Furthermore, comparisons among ABCVNS and several famous methods are also carried out. The related experimental results clearly demonstrate the effectiveness and the superiority of ABCVNS.

scholarly journals A Modified Sine-Cosine Algorithm Based on Neighborhood Search and Greedy Levy Mutation

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Chiwen Qu ◽  
Zhiliu Zeng ◽  
Jun Dai ◽  
Zhongjun Yi ◽  
Wei He
Keyword(s):  
Optimization Problems ◽  
Local Development ◽  
Convergence Speed ◽  
Neighborhood Search ◽  
Local Optimum ◽  
Search Range ◽  
Inertia Weight ◽  
Mutation Strategy ◽  
Sine Cosine Algorithm ◽  
Conversion Parameter

For the deficiency of the basic sine-cosine algorithm in dealing with global optimization problems such as the low solution precision and the slow convergence speed, a new improved sine-cosine algorithm is proposed in this paper. The improvement involves three optimization strategies. Firstly, the method of exponential decreasing conversion parameter and linear decreasing inertia weight is adopted to balance the global exploration and local development ability of the algorithm. Secondly, it uses the random individuals near the optimal individuals to replace the optimal individuals in the primary algorithm, which allows the algorithm to easily jump out of the local optimum and increases the search range effectively. Finally, the greedy Levy mutation strategy is used for the optimal individuals to enhance the local development ability of the algorithm. The experimental results show that the proposed algorithm can effectively avoid falling into the local optimum, and it has faster convergence speed and higher optimization accuracy.

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